An ecological environment technology full life cycle evaluation method and system oriented to technology iteration

Abstract

The application discloses a kind of ecological environment technology full life cycle evaluation method, system for technical iteration, the method includes building comprehensive research and judgment layer, parameter layer and data layer three-level evaluation architecture, the operation data of the technology to be evaluated is collected;Configuration data benchmark refresh cycle T, score elimination coefficient k, weight adjustment coefficient alpha and grade division sample amount M;Quantifiable data is updated based on the quantile benchmark of period T and is corrected, and the noise reduction of unquantifiable data is based on score elimination coefficient k and discrete variance and extreme score value is removed, based on double-layer coupling weight, hierarchical calculation technology comprehensive score and each unit sub-item score are carried out, technology position determination is completed, and the technology ranking of management end is output, and the unit adaptation gap positioning and technical improvement optimization scheme of enterprise end are output.

Description

Technical Field

[0001] This invention belongs to the field of ecological and environmental technology evaluation and information processing technology, specifically involving a method and system for full life-cycle evaluation of ecological and environmental technologies oriented towards technological iteration, applicable to ecological and environmental fields such as water treatment, air pollution control, solid waste treatment and comprehensive utilization, ecological restoration, soil remediation, energy conservation and carbon emission reduction. Background Technology

[0002] Currently, the following problems generally exist in the promotion, application, enterprise operation and maintenance, and upgrading of ecological and environmental technologies in my country: Management departments rely heavily on expert qualitative and experience-based judgments when screening advanced environmental protection technologies and formulating policies. They lack standardized and quantitative evaluation systems, making it difficult to conduct cross-industry technology comparisons and accurate grading of different technologies within the same industry. They also cannot conduct batch cross-industry comparisons of similar technologies within the same industry to determine the industry ranking and application level of a particular technology. As a result, the technology selection results are highly subjective. Existing evaluation methods generally assess the overall effectiveness of the entire technology, but cannot be broken down and analyzed in units. This makes it difficult to accurately identify specific adaptation gaps, which is not conducive to the competent authorities in determining the technology adaptation scenarios, nor can it point out specific upgrade and transformation directions for enterprises. The data processing rules are rigid, and the evaluation weights and grading thresholds are set statically for a long time. This makes it impossible to adapt to the differences in working conditions in different industries and the differences in the application of similar technologies in different regions, resulting in insufficient timeliness and universality of the judgment results. The existing evaluation, commendation, and technological upgrading work are fragmented, and the evaluation results cannot simultaneously serve technology promotion decisions, the assessment of the overall technological status of the industry, and the implementation of enterprise technological upgrades, lacking a complete closed-loop system. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for evaluating the entire life cycle of ecological and environmental technologies in response to technological iterations, so as to solve the defects existing in the above-mentioned background technology.

[0004] This invention provides a method for assessing the entire life cycle of ecological and environmental technologies oriented towards technological iteration, comprising the following steps: S1 constructs a hierarchical evaluation architecture, which includes a comprehensive judgment layer, a parameter layer, and a data layer; the comprehensive judgment layer is used to realize technical evaluation and report output, and forms evaluation results through comprehensive judgment; the parameter layer is used to form multiple evaluation parameter modules; the data layer collects data at least according to process unit, equipment unit, and material unit. S2 collects operational data from each enterprise throughout the entire lifecycle of the technology to be evaluated, stores it uniformly in the sample database, binds each piece of operational data to a unit in the data layer, and assigns or classifies each piece of operational data to a parameter module in the parameter layer. S3 configures multiple types of dynamic control parameters, including at least one of the following: data refresh cycle parameters, outlier handling parameters, weight adjustment parameters, and hierarchical threshold update parameters; S4 Data Standardization Processing: Each piece of running data is preprocessed differently using dynamic control parameters to eliminate differences in units and abnormal interference, resulting in a standardized dataset; S5 Layered and step-by-step calculation: Based on the standardized dataset and the weight system preset by the domain expert group, calculate the comprehensive evaluation value of the technology to be evaluated and the sub-item evaluation value of each unit in the data layer; based on the comprehensive evaluation value, complete the benchmarking and ranking determination of the same technology across industries or similar technologies in the same industry, and clarify the industry application level and overall level of a single technology; based on the sub-item evaluation value of each unit in the data layer, determine the technological transformation and optimization information of each enterprise.

[0005] Optionally, the method further includes: S6 Dual-path evaluation output and closed-loop iteration: Based on the evaluation results, industry analysis information for the management end and technological transformation and optimization information for the enterprise end are output respectively; the feedback data after technological transformation is fed back to the sample database to update the dynamic control parameters, and the evaluation grading standards are dynamically adjusted according to the grading threshold to realize the closed-loop iteration of the evaluation model.

[0006] Optionally, in step S1, the parameter layer includes six major modules: basic parameters, performance parameters, economic parameters, quality parameters, R&D iteration parameters, and engineering application parameters; in step S2, the binding relationship between each operating data item and the process unit, equipment unit, and material unit is established to realize unitized decomposition analysis, and the attribution relationship between each operating data and each module parameter is established.

[0007] Optionally, in step S2, the quantifiable data in the operating data consists of measurement values ​​collected by sensors or detection instruments, including at least one of processing scale, removal efficiency, unit energy consumption, and material loss rate; the non-quantifiable data in the operating data consists of rating values ​​given by professionals from multiple fields for technical performance, including at least one of process adaptability, operational stability, and material durability.

[0008] Optionally, in step S3, the dynamic control parameters specifically include: The data benchmark refresh period T serves as the update period for quantifiable data benchmark correction. The score elimination coefficient k is used to determine the proportion of extreme scores to be eliminated from unquantifiable data. The weight adjustment coefficient α is used to fine-tune the weight ratio of each level in the weight system; The sample size M for classifying grades serves as the trigger condition for reclassifying the evaluation grade threshold. In step S4, the preprocessing of quantifiable data includes: numerical correction based on the quantile benchmark updated in period T; the preprocessing of non-quantifiable data includes: adaptively removing extreme score values ​​to achieve noise reduction based on the score removal coefficient k and the discrete variance of the dataset; after preprocessing, normalization is performed on the two types of data.

[0009] Optionally, in step S5, the weighting system is a two-layer coupled weighting system, including the weights of each evaluation parameter module and the weights of each unit item; the weight adjustment coefficient α has a value range of 0.01~0.10, and its fine-tuning is based on the degree of fit between the judgment results and actual applications, the evaluation emphasis of different industry governance technologies, the database sample size, the transition status of the three types of unit adaptation gaps, and the update results of the benchmark dataset.

[0010] Optionally, in step S5, the hierarchical calculation includes at least one of the following: calculation of individual indicator scores, calculation of scores for each parameter segment, calculation of comprehensive technical scores, sub-scores for process units, equipment units, and material units, and dynamic adjustment parameter updates; the benchmarking and ranking determination includes horizontal benchmarking of similar technologies across industries, screening of the advantages and disadvantages of different technologies in the same industry, and determination of the industry ranking of similar technologies in the same industry.

[0011] Optionally, in step S6, the industry assessment information for the management side includes technology rankings and reports on the distribution of similar technology levels, which are used to support the formulation of environmental management policies and the promotion of technologies; the technological upgrading and optimization information for enterprises includes unit adaptation gaps determined based on the difference between the evaluation values ​​of each unit sub-item and the target threshold, as well as matching process adjustments, equipment parameter tuning, or material replacement schemes.

[0012] Optionally, in step S6, when the cumulative amount of newly added valid data in the sample database reaches the sample size M for the grade division, the system re-statistically calculates the score distribution, dynamically updates the threshold for adapting the gap grade, and simultaneously supports the iterative optimization of the weight adjustment coefficient α.

[0013] Accordingly, another aspect of the present invention provides an ecological and environmental technology life-cycle assessment system oriented towards technological iteration, comprising: The architecture building module is used to construct the comprehensive analysis layer, parameter layer, and data layer, and to collect data in the data layer according to process unit, equipment unit, and material unit. The data acquisition module is used to acquire quantifiable and non-quantifiable datasets; The parameter configuration module is used to configure the data benchmark refresh cycle T, score elimination coefficient k, weight adjustment coefficient α, and grade division sample size M. The data processing module is used to perform benchmark correction on quantifiable data, noise reduction on non-quantifiable data, and perform normalization calculations. The hierarchical calculation module is used to calculate the overall technical score and the score of each unit, and to complete cross-industry benchmarking, screening of technical advantages and disadvantages within the same industry, and determination of industry ranking. The dual-path output module is used to output industry analysis reports for management and technological improvement and optimization solutions for enterprises. The closed-loop iteration module is used to feed the upgraded data back to the sample database and automatically correct the data benchmark refresh cycle T, score elimination coefficient k, weight adjustment coefficient α, and grade division sample size M based on the updated dataset.

[0014] The beneficial effects of the present invention include at least one of the following: By constructing a three-level hierarchical architecture and breaking it down into process, equipment, and material units, it breaks through the limitations of traditional overall evaluation. It can both macroscopically complete cross-industry benchmarking and determine the ranking of similar technologies, and microscopically locate unit adaptation gaps, resulting in a high degree of precision in the analysis. It can configure four types of collaborative dynamic control parameters: T, k, α, and M. Quantifiable data is corrected using quantile benchmarks, and non-quantifiable data is adaptively denoised using the k coefficient. The weights and classification thresholds are automatically iterated as the sample expands. It has strong universality and timeliness and can adapt to differentiated evaluation in multiple fields. Simultaneously, it realizes three evaluation functions: horizontal benchmarking of similar technologies across industries, screening of the advantages and disadvantages of different technologies in the same industry, and determination of the industry ranking of similar technologies in the same industry. The evaluation results are output through two paths to serve environmental management decisions and enterprise technological transformation, respectively, with complete application dimensions. A complete closed loop is formed, consisting of "data collection → hierarchical operation → dual-path output → data feedback → automatic parameter update". The evaluation model continuously optimizes itself as industry technology develops and the sample expands. Attached Figure Description

[0015] Figure 1 A flowchart illustrating the ecological and environmental technology life cycle assessment method for technology iteration provided by this invention; Figure 2 The diagram shows the three-level overall architecture of this invention, from top to bottom: comprehensive analysis layer, parameter layer (six major modules), and data layer (process / equipment / material unit). Figure 3 The four types of autonomous iterative adjustment parameter logic block diagrams provided by this invention demonstrate the collaborative relationship between k-denoising, T-benchmark update, α-weight fine-tuning, and M-threshold refresh. Detailed Implementation

[0016] Numerous specific details are set forth in the following description to provide a full understanding of the invention. However, the invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0017] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, regarding numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included within the scope of this invention. The upper and lower limits of these smaller ranges may be independently included. Unless otherwise stated, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described in this invention, any methods and materials similar to or equivalent to those described herein may be used in the practice or testing of this invention.

[0018] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will be obvious to those skilled in the art, and the specification and embodiments are merely exemplary.

[0019] The terms “comprising,” “including,” “having,” “containing,” etc., used in this invention are all open-ended terms, meaning that they include but are not limited to.

[0020] In the description of this invention, references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this invention.

[0021] Brief description of the attached diagram: Figure 1 This is a flowchart illustrating a main embodiment of the present invention; Figure 2 The diagram shows a three-layer architecture. In this architecture, data is processed from bottom to top across the three layers. The bottom-level unit data is aggregated into six major parameter modules and finally uploaded to the comprehensive analysis layer to complete the rating and evaluation, realizing the two-way function of overall evaluation and unit decomposition. Figure 3 The logic diagram for four types of autonomous iterative adjustment parameters is as follows: k is responsible for data preprocessing, T is responsible for benchmark updating, α is responsible for weight adaptation, and M is responsible for rating threshold refresh, which together support the autonomous iteration of the entire evaluation model.

[0022] An ecological environment technology evaluation method and system oriented towards technological iteration, provided by an embodiment of the present invention, includes the following steps: 1) Establish a three-tier data architecture: construct a comprehensive analysis layer, a parameter layer, and a data layer; the comprehensive analysis layer is used for overall quantitative analysis of the technology; the parameter layer is divided into six major sections: basic parameters, efficiency parameters, economic parameters, quality parameters, R&D iteration parameters, and engineering application parameters; the data layer collects data according to three sub-units: process, equipment, and materials, establishes a binding relationship between data and evaluation units, and realizes unit-based decomposition and analysis; 2) Initialization of weights in dual-layer coupling: A multi-domain expert group including environmental management, process research and development, equipment design, and on-site operation and maintenance was formed. The analytic hierarchy process was used in combination with multiple rounds of anonymous cross-validation to complete the initial weight assignment of data items within the six major modules. All weights satisfy the normalization constraint. 3) Full life cycle data collection: Continuously collect various data throughout the entire life cycle of various ecological and environmental technologies and store them uniformly in the sample database to form a technology sample covering multiple industries, processes, and enterprises; 4) Configure four types of collaborative autonomous iterative control parameters: including data benchmark refresh cycle T, score elimination coefficient k (range 0.05~0.20), weight coefficient α (range 0.01~0.10), and grade division sample size M; 5) Hierarchical and progressive adaptive calculation: Based on the hierarchical architecture of parameter layer and data layer, quantifiable data is corrected by relying on the quantile benchmark updated periodically by T, and extreme scores are dynamically removed from the non-quantifiable dataset by relying on the coefficient k to achieve noise reduction. The two types of data are normalized to unify the dimensions. The scores of individual data items, various parameter scores, and the comprehensive score of the entire technology are calculated in sequence, and the scores of each unit of process, equipment, and materials are output simultaneously. The four types of parameters are iterated collaboratively, and the adaptation gap level classification threshold is dynamically updated according to the sample size M of the level classification. The benchmarking and classification of similar technologies in different industries and different technologies in the same industry are completed, and the ranking of similar technologies in the same industry is determined. The industry application level and overall level of a single technology are clarified, providing a quantitative basis for environmental management departments to select technologies, formulate policies, and assess the current status of industry technologies. At the same time, the unit adaptation gap of the technology used by enterprises is located by combining the score differences. The general adaptability and quantitative judgment accuracy of the entire evaluation system under cross-industry governance scenarios, differentiated technology systems, diversified working conditions, and various governance supporting equipment are significantly improved. 6) Dual-path closed-loop iterative optimization: On the one hand, based on the benchmarking results, excellent technologies are selected for industry promotion, and the overall technical level of the industry is judged by combining the ranking distribution of similar technologies; on the other hand, for the adaptation gap of enterprise technologies, technical improvement and optimization schemes such as process adjustment, equipment parameter tuning, and material replacement are output; after the scheme is implemented, the newly added corresponding data will be fed back to the sample database, the database will be updated according to the preset cycle, and the four types of operating parameters will be automatically corrected based on the updated dataset to complete the closed-loop iterative update.

[0023] The three-level architecture enables the breakdown and analysis from the overall technology to three types of units: process, equipment, and materials. It completes the identification of subdivided adaptation gaps, breaking through the limitations of traditional methods that only evaluate the overall effect of the technology but cannot finely break it down to pinpoint adaptation gaps or units that need improvement.

[0024] The data benchmark refresh period T is used to control the update interval of the data quantile benchmarks of each unit of ecological and environmental technology. The system periodically and automatically retrieves data from the database to generate a brand new calibration benchmark.

[0025] The score elimination coefficient k ranges from 0.05 to 0.20. The system calculates the sample variance of the unquantifiable dataset to characterize the degree of data divergence, and automatically calculates the number of extreme samples to be eliminated based on this coefficient, thereby achieving adaptive noise reduction. The quantifiable dataset is processed using quantile benchmark correction.

[0026] The weight coefficient α ranges from 0.01 to 0.10. The fine-tuning is based on the degree of fit between the judgment results and the field application, the evaluation emphasis of differentiated governance technologies in different industries, the size of the database sample, the transition status of the adaptation gap of the three types of units, and the update results of the benchmark dataset. The system re-evaluates the weights based on the continuously updated dataset and automatically corrects the double-layer coupling weights based on the weight coefficient.

[0027] For every M new sets of valid data added to the database, the system recalculates the distribution of all values ​​and automatically updates the threshold for the adaptation gap level classification.

[0028] The hierarchical computing architecture includes a data processing module, a hierarchically coupled computing module, a result output module, and a parameter update module. It adopts a two-layer weighted computing approach with data and parameter layers, making it a dedicated architecture for ecological and environmental technology evaluation and optimization.

[0029] Based on the unit to which the adaptation gap belongs and the magnitude of the gap, a differentiated optimization strategy is matched to achieve precise targeted transformation.

[0030] The method is applicable to horizontal benchmarking and grading of ecological and environmental technologies such as water treatment, air pollution control, solid waste treatment, ecological restoration, soil remediation, energy conservation and carbon emission reduction, ranking of similar technologies in the same industry, tracing the source of adaptation gaps, screening and promoting advanced technologies and optimizing enterprise technology upgrades, and supporting the formulation of environmental management policies and the assessment of industry technology levels.

[0031] It is important to clarify that a three-tiered data architecture is constructed, consisting of a comprehensive assessment layer, a parameter layer, and a data layer. The comprehensive assessment layer is used to complete the overall quantitative assessment of ecological and environmental technologies, cross-technology benchmarking and grading, and industry ranking determination of similar technologies. The parameter layer is divided into six major parameter modules: basic information, efficiency indicators, economic indicators, quality indicators, R&D iteration indicators, and engineering application indicators. The data layer collects corresponding data according to three sub-units: process, equipment, and materials. At the same time, it establishes a binding association between each data point and the three sub-units to achieve one-to-one matching between data and evaluation units, supporting subsequent unitized adaptation gap decomposition analysis.

[0032] An evaluation expert group was formed by selecting professionals in environmental management, process development, equipment design, and on-site operation and maintenance in the environmental protection field. The analytic hierarchy process (AHP) was used in combination with multiple rounds of anonymous cross-validation to initially assign weights to the six major parameter modules and the weights of each data item within each module. All weight values ​​met the normalization constraints, and the initial weight assignments were adapted to the operating rules of various technologies and aligned with the environmental management evaluation priorities of different industries.

[0033] Full-cycle operating condition data collection: The system continuously collects operating condition data for various ecological and environmental technologies throughout their entire life cycle. Based on the data source, it is divided into two categories: quantifiable datasets and non-quantifiable datasets. The definitions of these two datasets are as follows: Quantifiable datasets: collections of measurement values ​​automatically generated by on-site sensors and testing instruments, including quantitative indicators such as processing scale, removal efficiency, unit energy consumption, material loss rate, and engineering input; Unquantifiable datasets: A set of standardized scores given by professionals from multiple fields for technology adaptation performance, including process adaptability, operational stability, material durability, and application satisfaction.

[0034] All collected data will be stored in a unified database, covering technical samples from multiple industries, multiple processes, and multiple enterprises with the same process, providing complete data support for cross-industry technology benchmarking and determining the industry ranking of similar technologies.

[0035] This system sets up four types of core control parameters that rely on a database and can be updated iteratively. The functions and control logic of each parameter are as follows: 1) Data benchmark refresh cycle T: Used to control the timed update interval of the system benchmark dataset. The refresh cycle can be flexibly set monthly, quarterly, or annually as needed, periodically generating a brand new data correction benchmark. The 0.05, 0.5, and 0.95 quantiles of the sample dataset are uniformly selected as correction boundaries: the 0.05 quantile represents the lower limit of normal industry operation, the 0.5 quantile represents the industry average operation, and the 0.95 quantile represents the upper limit of normal industry operation. Instantaneous extreme measured values ​​exceeding this range are uniformly corrected based on the three quantiles, adapting to the need for continuous expansion of industry technical samples, and providing the latest benchmark basis for cross-industry technical benchmarking, ranking of similar technologies, weight correction, and calculation of hierarchical thresholds.

[0036] 2) Score elimination coefficient k: The value ranges from 0.05 to 0.20. It is used for the screening calculation of discrete samples. The system determines the degree of data divergence based on the overall discrete variance of the dataset. Combined with this coefficient, it adaptively calculates the number of extreme values ​​to be eliminated, and eliminates extreme samples that deviate from the normal range. This completes the data noise reduction process and ensures the objectivity and stability of horizontal benchmarking and industry ranking results. The lower limit of the range can slightly eliminate abnormal extreme values ​​and retain the valid evaluation samples completely. The upper limit strictly controls the elimination ratio to avoid the accidental deletion of valid data.

[0037] 3) Weighting coefficient α: The value ranges from 0.01 to 0.10, and it is used for adaptive fine-tuning of the two-layer coupling weights. The two-layer coupling weights are divided into primary weights for the module and secondary weights for the process / equipment / material units. The value, adjustment, and linkage rules of α are as follows: The base value of α is fixed at the median value of 0.05. When the cumulative number of valid samples in the database reaches the level division sample M, it is increased to the upper limit of the interval 0.10. When the number of samples in the database does not reach M, the base value of 0.05 is maintained, and it is never lower than 0.01 or higher than 0.10.

[0038] The average unit fit gap of all samples in the current period is calculated. The higher the average unit gap, the greater the increase in the secondary weight of the corresponding unit. The maximum value of a single weight change is equal to the value of α in this case. After all weights are adjusted, normalization constraints are applied.

[0039] Based on the industry percentile benchmark updated in period T, the overall performance of each indicator is compared. The primary weight of the sector to which the weakest indicator belongs is adjusted accordingly, and the maximum value of a single sector weight change is also constrained within the range of the current α value.

[0040] After completing the adjustment of the weights of sectors and units, the weights of all first-level sectors and the second-level weights of each unit are re-normalized to ensure that the sum of all weights at the same level equals 1.

[0041] After each T-cycle benchmark update, the system synchronously calculates the total number of samples in the current database and the average adaptation gap of each unit. It then determines the value of α based on the M threshold, adjusts the weights according to the gap and the quality of the benchmark, and finally normalizes them to achieve the collaborative weight correction driven by the three types of data: T, M, and unit adaptation gap.

[0042] By combining the degree of alignment between the analysis results and the actual application effectiveness, the evaluation focus and adaptation patterns of different industries and differentiated governance technology systems under diverse working conditions are matched; at the same time, by combining the cumulative size of the database samples corresponding to the grade division sample size M, the dynamic transfer status of the adaptation gap of each evaluation unit, and the benchmark dataset updated in the data benchmark refresh cycle T, the autonomous dynamic correction of the two-layer coupling weights is completed, adapting to the differentiated evaluation focus of different industries and meeting the weight allocation needs of horizontal comparison of similar technologies. 4) Grade Classification Sample Size M: Sets the effective sample trigger quantity for refreshing the grade classification threshold. When the cumulative number of effective samples in the database reaches the set size, the system automatically recalculates and adapts the gap grade classification threshold and the technical quality classification standard, and simultaneously supports the iterative optimization of the weight coefficient α, ensuring that the judgment standard for cross-industry technical evaluation and the industry ranking of similar technologies is dynamically updated as industry samples improve.

[0043] This method addresses the characteristics of high dispersion and dynamic changes in sample distribution in ecological and environmental technology datasets by constructing a hierarchical, step-by-step computational architecture with parameter and data layers; and completes the overall computational process with the aforementioned four types of collaborative iterative control parameters.

[0044] The calculation process is as follows: Quantifiable data correction is performed based on the quantile benchmark updated over a period of T; non-quantifiable data noise reduction is performed based on the coefficient k; then, normalization is performed on both types of data to unify the dimensions; through hierarchical calculations, the scores of individual bottom-level data, the scores of the six major parameter modules, and the comprehensive score of the entire technology are calculated sequentially, and the scores of each independent unit of process, equipment, and materials are output simultaneously; the system dynamically updates and adapts the gap level classification threshold based on the level classification sample size M, and completes three types of judgment work based on the comprehensive score: 1) Horizontal benchmarking and comparison of similar technologies across different industries; 2) Ranking and classifying different technologies within the same industry to select advanced technologies with superior overall performance; 3) By comparing similar technologies in batches with those in the same industry, we can determine the level of this technology within the industry and intuitively reflect the overall application level of this technology in the industry.

[0045] The results of the analysis provide quantitative basis for environmental management departments to compile technology catalogs, formulate promotion policies, and assess the overall technological status of the industry. On the other hand, by combining the differences in scores, the analysis can accurately pinpoint the unit adaptation gaps of enterprise technologies and clarify the industry level of the enterprise's own technologies.

[0046] This invention introduces unit fit gap degree as a quantitative index for unit shortcomings, used to characterize the ratio of the actual score of a single evaluation unit to the industry's best standard score. The calculation algorithm is as follows: 1) The system automatically obtains the industry-optimal target score for each unit and indicator. This score is obtained by mapping the score from the 0.95 quantile of the current period. 2) Unit fit gap = (Unit optimal target score) (Actual calculated score of this unit) ÷ Optimal target score of the unit; 3) Preset grading thresholds: Gap degree < 0.3 indicates good fit; 0.3 ≤ Gap degree ≤ 0.5 indicates moderate fit gap; Gap degree > 0.5 indicates severe fit gap.

[0047] The entire evaluation system can significantly improve the universal adaptability and quantitative judgment accuracy of cross-industry governance scenarios, differentiated technology systems, diverse working conditions and various technologies, and fully guarantee the fairness of cross-industry technology comparison and the ranking of similar technologies in the industry, as well as the accuracy of the rating results.

[0048] A complete operational loop is established, encompassing "data collection → modular data processing → hierarchical and step-by-step calculation and benchmarking → adaptation for gap tracing and industry ranking determination → dual-path solution output → autonomous updating of unit control parameters," with two main application output directions: 1) For environmental management: Output industry-wide technology rankings and industry-level distribution reports of similar technologies, select the best technologies with excellent performance, good cost performance and strong adaptability, and present the overall application level of similar technologies to support management departments in formulating relevant policies such as technology promotion and industry control. 2) For enterprise operation and maintenance: Identify the industry position of the enterprise in the technologies it uses, and provide targeted technical improvement and optimization solutions for process improvement, equipment adjustment, and material replacement to address the technology adaptation gaps.

[0049] Meanwhile, the system will rely on a continuously updated database to automatically complete the correction and iteration of four types of core parameters, so as to achieve synchronous upgrading of evaluation standards, judgment models and industry technology development.

[0050] In summary, according to embodiments of the present invention: First, a three-tiered, dedicated architecture is used to integrate overall evaluation with refined unit breakdown. This invention constructs a three-tiered architecture from top to bottom: a comprehensive assessment layer, a parameter layer, and a data layer. The parameter layer is divided into six major modules based on evaluation dimensions, while the data layer is bound to three sub-units based on process, equipment, and materials. Differentiated data processing is conducted based on both quantifiable and non-quantifiable data sets. This overcomes the limitations of traditional, broad-based evaluation methods. It can both macroscopically assess the overall technical score and perform horizontal benchmarking and ranking, and delve down to the bottom-level sub-units to independently calculate scores, achieving precise identification of unit-level adaptation gaps. This addresses the lack of refinement in existing evaluation methods from an architectural perspective.

[0051] Then, by using the coordinated control of four types of parameters (T, k, α, M), adaptive dynamic computation throughout the entire process can be achieved. Unlike traditional static parameter-based methods, this invention employs a complementary design of four distinct and synergistic core control parameters, forming a complete closed-loop control logic. The score elimination coefficient *k* is used for adaptive filtering and noise reduction of discrete extrema; the data benchmark refresh cycle *T* periodically updates the quantile correction benchmark; the weighting coefficient *α* dynamically fine-tunes the dual-layer coupled weights based on multiple dimensions to adapt to different industry evaluation focuses; and the grade classification sample size *M* automatically updates the grading threshold based on the cumulative amount of effective samples. These four types of parameters work in tandem to form an adaptive computation mechanism specifically designed for ecological and environmental technology evaluation, solving the problems of rigid and inflexible traditional evaluation parameters.

[0052] Furthermore, a comprehensive evaluation system will be constructed, capable of simultaneously realizing three types of assessment functions, taking into account both government and enterprise usage scenarios. This evaluation system can simultaneously complete three types of evaluation tasks: cross-industry horizontal benchmarking and comparison of similar governance technologies, selection of the best technologies from different technologies within the same industry, and determination of the hierarchical level of similar technologies within the same industry. Evaluation results are output through two paths: for environmental management departments, it provides technology rankings and an overall industry technology level analysis report, offering quantitative basis for technology promotion catalog compilation and industry policy formulation; for enterprises, it provides unit adaptation gaps and targeted technological transformation solutions. This achieves simultaneous support for environmental management decision-making and enterprise technological upgrading and transformation, with comprehensive application dimensions.

[0053] Finally, a data feedback closed-loop iteration mechanism is constructed to enable long-term autonomous upgrading of the evaluation model. This invention employs a fully closed-loop process: "data input → preprocessing and hierarchical calculation → dual-path result output → data feedback → automatic parameter update." New data generated after enterprise technological upgrades are fed back into the database. Based on the updated dataset, the four core parameters are automatically recalculated, and the data correction benchmark, weighting ratio, and grading standards are updated synchronously. This allows the evaluation model to continuously optimize along with industry technological upgrades, effectively adapting to the long-term development needs of the ecological and environmental protection industry.

[0054] The beneficial effects of this invention include: The analysis is comprehensive and takes into account both government and enterprise application scenarios. This system relies on a three-tier architecture to achieve three core analytical functions: it can perform quantitative benchmarking and quality screening of similar technologies across industries and different processes within the same industry; it can determine the ranking of similar technologies within the same industry, clearly determine the industry application level of a certain technology, and facilitate management departments to grasp the overall status of industry technology; it can accurately locate the unit adaptation gap of enterprise technology, output targeted transformation solutions, and achieve integrated support for macro-management decision-making, industry level assessment and micro-enterprise technology transformation.

[0055] A three-tiered architecture combined with a unit-based decomposition model allows for highly refined analysis. This system's top-down hierarchical architecture breaks through the limitations of traditional overall technology evaluation. It can both macroscopically complete horizontal comparisons of industry technologies and rank similar technologies, and microscopically locate three sub-units to assess adaptation gaps, significantly improving the accuracy of technology rating and adaptation gap location.

[0056] The four parameters work together to adaptively adjust the system, resulting in strong versatility and timeliness. The four parameters T, k, α, and M are synergistically controlled, and the weight ratio and grading threshold can be dynamically adjusted according to the industry sample size and cross-industry evaluation focus. It is suitable for technical evaluation in multiple fields such as water treatment, air pollution control, and solid waste treatment. The grading standards can be continuously optimized as industry data accumulates, and can adapt to the evaluation needs of environmental management in different periods in the long term.

[0057] The complete closed-loop iterative architecture enables the model to have long-term self-optimization capabilities. The entire system forms a complete closed loop encompassing evaluation and grading, policy formulation, enterprise technological transformation, data feedback, and parameter updates. As the industry's technical samples continue to expand, the evaluation system continuously optimizes itself, adapting to the long-term iteration of ecological and environmental technologies and the upgrading of environmental management work.

[0058] Example 1: Technical Benchmarking Evaluation in the Municipal Water Treatment Industry (Management-level Evaluation + Ranking of Similar Technologies) The AAO biological treatment process, MBR membrane treatment process, and oxidation ditch process of a municipal wastewater treatment plant in a certain region were selected as evaluation objects.

[0059] Step 1: Adopting the aforementioned three-level evaluation framework, the six major parameter modules (basic parameters including treatment scale and design water volume, efficiency parameters including COD / BOD / NH3-N / TN / TP removal rates, economic parameters including energy consumption per ton of water, chemical consumption per ton of water, and engineering investment, quality parameters including equipment operation stability and failure rate, R&D iteration parameters including frequency of technical upgrades in recent years and integration of new technologies, and engineering application parameters including process adaptability and application satisfaction) remain unchanged. The data layer collects and binds data according to process units (anaerobic / anoxic / aerobic zones), equipment units (blowers / propellers / sludge pumps), and material units (packing / membrane modules).

[0060] Step 2: Establish an expert group in the fields of environmental management, process design, and on-site operation and maintenance. Use the analytic hierarchy process (AHP) combined with two rounds of anonymous cross-validation to complete the initial assignment of the dual-layer coupling weights. The weights of the six major modules are 0.10, 0.25, 0.20, 0.15, 0.10, and 0.20, respectively. The weights of the data items within each module are normalized.

[0061] Step 3: Collect data on the treatment scale, influent and effluent water quality indicators, COD removal rate, energy consumption per ton of water (kWh / m³), chemical consumption per ton of water (kg / m³), consumable replacement cycle, process compatibility score (1~10 points), equipment operation stability score (1~10 points), and material durability score (1~10 points) of each water plant in batches, and store them in the database to form a municipal water treatment industry sample library.

[0062] Step 4: Set dynamic control parameters—the data benchmark refresh cycle T is updated quarterly for the quantile benchmark; the score elimination coefficient k=0.10, the number of extreme scores to be eliminated is determined by variance × k, and the elimination ratio is ≤10%; the weight adjustment coefficient α=0.05, which is combined with the water treatment industry evaluation to fine-tune the weight of efficiency parameters and economic parameters; the grade division sample size M=50 groups, after adding 50 groups of valid samples, the system re-statistically calculates the comprehensive score distribution of all samples and refreshes the four-level threshold of excellent / good / medium / poor for the comprehensive grading of the entire technology.

[0063] Step 5: Perform 0.05 / 0.5 / 0.95 quantile correction on quantifiable data using the updated quantile benchmark for the current season. For instantaneous water quality data exceeding the upper limit during the rainy season, assign a unified score based on the 0.95 quantile. For non-quantifiable scores, remove extreme values ​​and normalize to [0,1] using min-max. Based on a two-layer coupled weighting, fine-tune the weights of the sectors and units with α=0.05, prioritizing the upward adjustment of equipment unit weights (due to higher average equipment unit adaptation gaps). After adjustment, the weights are re-normalized. Calculate the individual score → sector score → comprehensive score for each plant, simultaneously outputting the scores for each plant's process unit, equipment unit, and material unit. Perform batch calculations for similar AAO processes at multiple water plants across the region, comparing each plant's comprehensive score with historical similar samples to obtain the industry percentile ranking (e.g., P). 85This indicates that the plant is superior to 85% of similar water plants; a comprehensive score ranking is generated through horizontal comparison of various processes to select the most efficient and low-consumption preferred process; based on the unit adaptation gap degree = (unit optimal target score)... Calculate the gap value for each unit by dividing the actual score by the optimal target score. Units with a gap value greater than 0.3 are marked as units that require technical upgrades.

[0064] Step Six: The management system outputs a technology ranking table, a report on the distribution of similar AAO process levels (15% excellent, 40% good, 35% intermediate, and 10% poor), and an overall industry technology level assessment conclusion. This information is provided to the environmental protection department for compiling a directory of advanced technologies for municipal wastewater treatment. For water plants with lower rankings, the system identifies low scores for specific equipment unit items (fitness gap > 0.3), highlighting the issue of high blower energy consumption and providing targeted guidance for subsequent technological upgrades at the enterprise level. New operational data is fed back into the database, triggering parameter iteration.

[0065] Example 2: Diagnosis of Technological Upgrading for Industrial Air Pollution Control Enterprises (Enterprise-Level Positioning + Technological Upgrading) An evaluation was conducted on the existing air pollution control facilities of a chemical company, which consisted of a cyclone dust collector, a bag filter, and a SCR denitrification system.

[0066] Steps 1-3: The three-level architecture is used. The six major modules of the parameter layer are the same. The data layer collects data according to process unit (cyclone pre-dust removal section / SCR reaction section), equipment unit (induced draft fan / ammonia injection grid / bag cleaning device), and material unit (filter bag material / catalyst model). Collect quantifiable and non-quantifiable data such as inlet and outlet dust concentration, NOx concentration, denitrification efficiency, energy consumption per unit treated gas volume (kWh / 1000Nm³), catalyst deactivation rate, operational stability score, and material durability score.

[0067] Step 4: Set T = half a year, k = 0.08, α = 0.03, M = 30.

[0068] Step 5: Complete data correction, noise reduction, normalization, and hierarchical calculation to obtain the comprehensive score of the facility and determine its industry ranking among similar SCR+bag dust collection technologies in the local industry (e.g., ranking at the 60th percentile). The hierarchical output results show that the unit energy consumption score of the equipment unit is significantly lower than the target threshold, the unit adaptation gap is calculated to be 0.35 (exceeding the initial judgment threshold of 0.3 for moderate gap), and the process unit and material unit scores are within the normal range.

[0069] Step Six: Output the technical upgrade and optimization plan to the enterprise – It is recommended to replace the high-efficiency variable frequency induced draft fan and reduce the excess ammonia injection rate to optimize the operating parameters, and at the same time, it is suggested to check the bag filter cleaning frequency; After the enterprise completes the transformation according to the plan, the post-transformation operating data (energy consumption per unit treated gas volume decreased by 18% and NOx removal rate maintained at ≥92%) is fed back to the database. The system automatically re-evaluates the weight adjustment coefficient α, updates the quantile benchmark and grade classification threshold based on the expanded sample set, so that the industry ranking judgment standard of similar enterprises and similar technologies is updated synchronously, and the model closed-loop iteration is realized.

[0070] It should be noted that although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope defined in the claims of the present invention.

Claims

1. A method for evaluating the entire life cycle of ecological and environmental technologies oriented towards technological iteration, characterized in that, Includes the following steps: S1 constructs a hierarchical evaluation architecture, which includes a comprehensive judgment layer, a parameter layer, and a data layer; the comprehensive judgment layer is used at least to realize technical evaluation and report output, and forms evaluation results through comprehensive judgment; the parameter layer is used to form multiple evaluation parameter modules; the data layer collects data according to process unit, equipment unit, and material unit. S2 collects operational data from each enterprise throughout the entire lifecycle of the technology to be evaluated, stores it uniformly in the sample database, binds each piece of operational data to a unit in the data layer, and assigns or classifies each piece of operational data to a parameter module in the parameter layer. S3 configures multiple types of dynamic control parameters, including at least one of the following: data refresh cycle parameters, outlier handling parameters, weight adjustment parameters, and hierarchical threshold update parameters; S4 Data Standardization Processing: Each piece of running data is preprocessed differently using dynamic control parameters to eliminate differences in units and abnormal interference, resulting in a standardized dataset; S5 Layered and step-by-step calculation: Based on the standardized dataset and the weight system preset by the domain expert group, calculate the evaluation value of each segment of the technology to be evaluated, the comprehensive evaluation value, and the sub-item evaluation value of each unit in the data layer. Based on the comprehensive evaluation value, the same technology across industries or similar technologies within the same industry are benchmarked and ranked, clarifying the industry application level and overall level of each enterprise's technology; based on the sub-evaluation values ​​of each unit in the data layer, the technological upgrading and optimization information of each enterprise is determined.

2. The method according to claim 1, characterized in that, The method further includes: S6 Dual-path evaluation output and closed-loop iteration: Based on the evaluation results, industry analysis information for the management end and technological transformation and optimization information for the enterprise end are output respectively; the feedback data after technological transformation is fed back to the sample database to update the dynamic control parameters, and the evaluation grading standards are dynamically adjusted according to the grading threshold to realize the closed-loop iteration of the evaluation model.

3. The method according to claim 1, characterized in that, In step S1, the parameter layer includes six major sections: basic parameters, efficiency parameters, economic parameters, quality parameters, R&D iteration parameters, and engineering application parameters. The basic parameters are general basic inherent attribute indicators of various ecological environment governance technologies, covering basic information such as design treatment scale, equipment rated parameters, process configuration, and engineering construction volume. In step S2, the binding relationship between each operating data item and the process unit, equipment unit, and material unit is established to realize unitized disassembly and analysis, and the attribution relationship between each operating data and each module parameter is established.

4. The method according to claim 1, characterized in that, In step S2, the quantifiable data in the operational data consists of measurement values ​​collected by sensors or detection instruments, including at least one of processing scale, removal efficiency, unit energy consumption, and material loss rate; the non-quantifiable data in the operational data consists of rating values ​​given by professionals from multiple fields for technical performance, including at least one of process adaptability, operational stability, and material durability.

5. The method according to claim 1, characterized in that, In step S3, the dynamic control parameters specifically include: The data benchmark refresh period T serves as the update period for quantifiable data benchmark correction. The score elimination coefficient k is used to determine the proportion of extreme scores to be eliminated from unquantifiable data. The weight adjustment coefficient α is used to fine-tune the weight ratio of each level in the weight system; The sample size M for classifying grades serves as the trigger condition for reclassifying the evaluation grade threshold. In step S4, the preprocessing of quantifiable data in the running data includes: numerical correction based on the quantile benchmark updated in period T; the preprocessing of non-quantifiable data in the running data includes: adaptively removing extreme score values ​​to achieve noise reduction based on the score removal coefficient k and the discrete variance of the dataset; after the preprocessing is completed, the two types of data are normalized.

6. The method according to claim 5, characterized in that, In step S5, the weighting system is a two-layer coupled weighting system, which includes the weights of each evaluation parameter module and the weights of each unit item. The weight adjustment coefficient α ranges from 0.01 to 0.10, and its fine-tuning is based on factors such as the fit between the judgment results and actual applications, the evaluation emphasis of governance technologies in different industries, the size of the database sample, the transition status of the adaptation gap of the three types of units, and the update results of the benchmark dataset.

7. The method according to claim 1, characterized in that, In step S5, the hierarchical calculation includes at least one of the following: calculation of the score of each parameter segment, calculation of the comprehensive technical score, calculation of the sub-item score of process unit, equipment unit, and material unit, and calculation of dynamic control parameter update; the benchmarking and ranking determination includes horizontal benchmarking of similar technologies across industries, screening of the advantages and disadvantages of different technologies in the same industry, and determination of the industry ranking of similar technologies in the same industry.

8. The method according to claim 2, characterized in that, In step S6, the industry analysis information for the management end includes technology rankings and reports on the distribution of similar technology levels, which are used to support the formulation of environmental management policies and the promotion of technologies. The technological upgrading and optimization information for enterprises is generated based on the unit sub-item evaluation values ​​obtained by hierarchical calculation in step S5. It includes the unit adaptation gap determined based on the difference between the unit sub-item evaluation value and the target threshold, as well as the matching process adjustment, equipment parameter setting or material replacement scheme.

9. The method according to claim 2, characterized in that, In step S6, when the cumulative amount of newly added valid data in the sample database reaches the sample size M for the grade division, the system re-statistically calculates the score distribution, dynamically updates the threshold for the adapted gap grade, and simultaneously supports the iterative optimization of the weight adjustment coefficient α.

10. A life-cycle assessment system for ecological and environmental technologies oriented towards technological iteration, characterized in that, include: The architecture building module is used to construct the comprehensive analysis layer, parameter layer, and data layer, and to collect data in the data layer according to process unit, equipment unit, and material unit. The data acquisition module is used to acquire quantifiable and non-quantifiable datasets; The parameter configuration module is used to configure the data benchmark refresh cycle T, score elimination coefficient k, weight adjustment coefficient α, and grade division sample size M. The data processing module is used to perform benchmark correction on quantifiable data, noise reduction on non-quantifiable data, and perform normalization calculations. The hierarchical calculation module is used to calculate the overall technical score and the score of each unit, and to complete cross-industry benchmarking, screening of technical advantages and disadvantages within the same industry, and determination of industry ranking. The dual-path output module is used to output industry analysis reports for management and technological improvement and optimization solutions for enterprises. The closed-loop iteration module is used to feed the upgraded data back to the sample database and automatically correct the data benchmark refresh cycle T, score elimination coefficient k, weight adjustment coefficient α, and grade division sample size M based on the updated dataset.

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