A system for comprehensive evaluation of ecological and environmental protection industry development

Abstract

The application discloses a kind of for ecological environmental protection industry development comprehensive evaluation system, specifically related to ecological environmental protection evaluation field, including data perception processing module, cross-domain index quantification module, dynamic correlation analysis module, deduction optimization module and intelligent deduction module.A kind of for ecological environmental protection industry development comprehensive evaluation system realizes the active access and multi-source integration of real-time market signal, environment and industry data required for ecological environmental protection industry comprehensive evaluation by data perception processing module, provides high-quality basic data support for industry assessment;Real-time quantitative evaluation of ecological environmental protection industry cross-domain index is completed by realizing the deep fusion of data center and industry evaluation model through cross-domain index quantification module, constructs the real-time calculation engine serving industry evaluation, and realizes the real-time correlation research and collaborative analysis of ecological environmental protection industry cross-domain evaluation index through dynamic correlation analysis module, improves the real-time calculation engine serving industry decision.

Description

Technical Field

[0001] This invention relates to the field of ecological and environmental protection evaluation technology, and more specifically, to a system for comprehensive evaluation of the development of the ecological and environmental protection industry. Background Technology

[0002] As the environmental protection industry shifts from qualitative management to digital decision-making, the operational and dynamic comprehensive evaluation of the industry's development level has become a core business and management need to support and guide market resource allocation and optimize corporate green investment. Traditional technical approaches mainly use pre-set indicator systems to weight and summarize lagging annual or quarterly statistical data to generate a descriptive scoring report. However, long-term practice has shown that data processing relies on manual collection and verification, which is inefficient and prone to errors, making it difficult to meet the needs of high-frequency and fast-paced business decision-making. Evaluation models are rigid and inflexible; once the weights are set, they are difficult to adaptively adjust with industry dynamics, policy guidance, or regional differences, resulting in a delayed response of evaluation results to market changes and investment risks.

[0003] To address the problems of traditional technologies, existing technologies have established regional environmental protection data platforms and adopted machine learning models based on big data analysis to dynamically train and optimize indicator weights. This has shifted from offline systems that rely on manual drivers and static rules to online analysis platforms that are data-driven and have certain dynamic computing capabilities, thereby improving data processing speed and model complexity.

[0004] However, in practical use, it still has some shortcomings. For example, the lack of a real-time computing engine that deeply integrates the existing data platform and the evaluation model makes it difficult to achieve real-time correlation analysis and collaborative optimization of cross-domain indicators; the lack of a closed-loop learning mechanism that actively accesses real-time market signals and continuously iterates and optimizes the parameters of the evaluation model makes it impossible to conduct reliable predictive analysis of the potential commercial value of future trends in industrial development. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a system for comprehensive evaluation of the development of the ecological and environmental protection industry, which solves the problems mentioned in the background art through the following solutions.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A system for comprehensive evaluation of the development of the ecological and environmental protection industry, comprising:

[0008] Data sensing and processing module: used to actively access and collect the first state signal in real time through a dedicated data adapter, and to preprocess the first market signal to obtain the first state characteristics;

[0009] Cross-domain indicator quantification module: used to dynamically call the indicator quantification model for real-time calculation based on the first market feature, so as to generate a second state feature that updates over time;

[0010] Dynamic correlation analysis module: It is used to analyze and update the dynamic correlation strength between various quantitative indicators in the second state feature in real time through a preset dynamic graph calculation model, and generate a third state feature that describes the interaction relationship between cross-domain indicators.

[0011] The deduction and optimization module is used to deduce the development trend of the ecological and environmental protection industry with the third state characteristics as constraints, and to generate a set of optimization schemes by simulating the evolution of various quantitative indicators through a multi-objective optimization algorithm.

[0012] Intelligent simulation module: used to perform scheme simulation on the set of optimization schemes and output a structured decision report containing recommended actions, expected effects and risk warnings.

[0013] Preferably, the data sensing and processing module, including the dedicated data adapter, comprises at least:

[0014] An environmental monitoring adapter unit is used to access the first signal stream of the Internet of Things sensor network and remote sensing data platform. The first signal stream includes environmental quality parameters and resource consumption measurement data related to ecological and environmental protection.

[0015] The industrial economic adaptation unit is used to access the second signal stream of the enterprise ERP and market trading platform. The second signal stream includes economic operation indicators, production activity data and market transaction dynamics.

[0016] The intelligence text adaptation unit is used to access and capture third signal streams from policy release platforms, patent databases of scientific research institutions, and public opinion information sources. The third signal streams include policy regulations, technical intelligence, and market opinion information in the form of unstructured text.

[0017] The first status signal is composed of real-time signal streams collected in parallel by the environmental monitoring adaptation unit, the industrial economic adaptation unit, and the intelligence text adaptation unit.

[0018] Preferably, the cross-domain index quantization module generates the index quantization model of the second state feature through an adaptive model selection framework, which executes as follows:

[0019] Feature analysis is performed on the first state feature to determine the structure type, temporal completeness, and confidence level of the source data corresponding to the indicator to be quantified;

[0020] Based on the results of the feature analysis, at least one computational model is dynamically matched and called from a pre-set quantization model library, wherein the quantization model library includes multiple types of models, such as statistical parametric regression models, machine learning nonparametric interpolation models, and domain knowledge rule-based inference models.

[0021] The source data is calculated in real time using the invoked computing model to generate and output the second state feature.

[0022] Preferably, the quantification indicators in the second state feature include at least:

[0023] The original index value is calculated in real time based on the first state feature; and,

[0024] Derivative index values ​​are generated by predictively adjusting the original index values ​​based on historical sequences and real-time feedback through an online learning mechanism.

[0025] The online learning mechanism continuously optimizes the adjustment parameters or algorithms for generating the derived index values ​​based on the prediction error signal from the closed-loop learning feedback.

[0026] Preferably, the dynamic correlation analysis module is used to characterize each quantitative indicator in the second state feature and its dynamic correlation strength as a dynamic graph structure, wherein:

[0027] Map each quantitative indicator to a node in the graph. , forming a set of nodes ;

[0028] indicators and Between moments The identified significant relationships are mapped to edges in the graph. , forming an edge set ;

[0029] For each edge Assign a weight value , forming a weight set The weight values ​​are used to quantify the indicators. right The dynamic correlation strength at time t;

[0030] Meanwhile, the calculation and updating of its edge weights are specifically expressed as follows:

[0031]

[0032] in, This represents the raw value of the association strength calculated based on the data at the current moment. This is represented as the preset learning rate parameter;

[0033] It continuously outputs its dynamically updated graph structure. As a third state feature.

[0034] Preferably, the dynamic correlation analysis module, for indicators and Obtain the raw values ​​of association strength from time series data. And determining the direction of the edges, specifically including:

[0035] Within the sliding time window, fit the following regression model and calculate its sum of squared residuals:

[0036] Constrained model: ;

[0037] Unrestricted model: ;in, Represented as the lag order, Represented as autoregressive coefficients, Represented as a lag order index, , Represented as indicators and The observation at the k-th time unit before time point t, , This represents the prediction residuals of the constrained and unconstrained models at time t. Expressed as cross-regression coefficients;

[0038] The sum of squared residuals based on the constrained model and the unconstrained model and Calculate the F-statistic:

[0039]

[0040] in, This represents the total number of valid time series data points within the sliding time window;

[0041] If the F-statistic indicates the indicator Historical data for indicators If the current value has a significant causal effect, then a sub-node is created in the dynamic graph. Pointing to node The directed edges, and the original values ​​of the calculated association strength. Set as the F-statistic or the cross-regression coefficient in the unrestricted model. The norm of .

[0042] Preferably, the dynamic correlation analysis module obtains the original value of the correlation strength. Mutual information can also be calculated. Specifically, it includes:

[0043] For indicators and Estimate their joint probability distribution within the current time window and marginal probability distribution and ;

[0044] Calculation indicators and Inter-information Specifically, it is expressed as:

[0045]

[0046] If the mutual information value If the salience exceeds a preset threshold, then in the graph structure Nodes in and Create an undirected edge between them and its original correlation strength value. Set as the mutual information value .

[0047] Preferably, the dynamic correlation analysis module determines the original value for calculating the correlation strength based on a preset selection strategy. Specifically, the selection strategy includes:

[0048] If the indicator and If the data is a high-quality time series and the analysis task requires a clear correlation direction, then methods based on constrained and unconstrained models should be prioritized for calculation. ;

[0049] If the indicator and If the data relationships are expected to be complex and nonlinear, or if the data is an irregular time series, then the mutual information calculation method is used to calculate... .

[0050] The technical effects and advantages of this invention are as follows:

[0051] 1. This invention realizes the active access and multi-source integration of real-time market signals, environmental and industrial data required for the comprehensive evaluation of the ecological and environmental protection industry through the data perception and processing module, providing high-quality basic data support for industry assessment and alleviating the shortcomings of existing technologies that lack active access to real-time market signals;

[0052] 2. This invention achieves deep integration of data platform and industry assessment model through cross-domain indicator quantification module, builds real-time computing engine to serve industry evaluation, completes real-time quantitative assessment of cross-domain indicators of ecological and environmental protection industry, and alleviates the defects of existing technology that lack closed-loop learning mechanism and lack deep integration of data platform and assessment model real-time computing engine.

[0053] 3. This invention realizes real-time correlation analysis and collaborative analysis of cross-domain assessment indicators of the ecological and environmental protection industry through a dynamic correlation analysis module, improves the real-time computing engine serving industry decision-making, and solves the shortcomings of existing technologies in achieving real-time correlation analysis of cross-domain indicators. Attached Figure Description

[0054] Figure 1 This is a block diagram of a system for comprehensive evaluation of the development of the ecological and environmental protection industry, according to an embodiment of this application. Detailed Implementation

[0055] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0057] Hereinafter, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first," "second," and "third" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0058] As attached Figure 1 The system shown is used for comprehensive evaluation of the development of the ecological and environmental protection industry, including a data perception and processing module, a cross-domain indicator quantification module, a dynamic correlation analysis module, a deduction and optimization module, and an intelligent deduction module.

[0059] The data sensing and processing module is used to actively access and collect the first state signal in real time through a dedicated data adapter, and to preprocess the first market signal to obtain the first state characteristics.

[0060] It should be noted that the dedicated data adapter includes at least an environmental monitoring adaptation unit, an industrial economics adaptation unit, and an intelligence text adaptation unit. The environmental monitoring adaptation unit is used to access an IoT sensor network distributed across various monitoring points via the MQTT protocol, and periodically retrieves wide-area monitoring data such as vegetation index and surface temperature provided by a satellite remote sensing data platform through API calls, forming a first signal stream. This first signal stream contains environmental quality parameters and resource consumption measurement data related to ecological and environmental protection. In this embodiment, the IoT sensors include, but are not limited to, water quality sensors, atmospheric composition sensors, and smart meters. The industrial economics adaptation unit is used to periodically access tables related to energy consumption and output value in the ERP system of partner companies through a secure database connection, and then... The bSocket interface listens to real-time market data and transaction snapshots released by the carbon emission rights trading platform to form a second signal stream, which includes economic operation indicators, production activity data, and market transaction dynamics. The intelligence text adaptation unit is configured with a targeted web crawler to access and regularly crawl policy documents released by the policy release platform and patent databases of major research institutions. Simultaneously, it obtains public opinion information on environmental protection technologies from social media through public APIs to form a third signal stream, which includes policy regulations, technical intelligence, and market opinion information in unstructured text form. The first status signal is composed of real-time signal streams collected in parallel by the environmental monitoring adaptation unit, the industrial economic adaptation unit, and the intelligence text adaptation unit.

[0061] Furthermore, the preprocessing includes at least the following: Data cleaning: For the numerical data in the first and second signal streams, upper and lower limits are set, outliers are automatically filtered or marked, and missing values ​​are processed using time-based linear interpolation; Format standardization: All numerical values ​​are uniformly converted to international standard units, and the timestamps of all data are calibrated to Beijing time, and the sampling frequency is uniformly normalized to the basic time granularity; Text structuring: For the unstructured text in the third signal stream, the built-in natural language processing unit is invoked. The natural language processing unit uses a named entity recognition model pre-trained on environmental protection text to extract key entities and values ​​such as "policy name", "issuing agency", "technical term", and "quantitative target", and classifies and scores them, converting them into structured fields; The technical term categories are further subdivided into subcategories such as "clean energy", "pollution control", and "recycling"; Spatiotemporal tagging: A precise timestamp and geographic location code are attached to each processed data record.

[0062] It should be noted that the construction of the named entity recognition model includes: collecting historical policy documents, environmental patent abstracts, and corporate social responsibility reports to form a training corpus, and having experts annotate the entity categories involved; using the BERT-base model as a base, fine-tuning the training on the annotated corpus with a learning rate of 2e-5 and a batch size of 32, and finally achieving an entity recognition F1 score of 0.92 on the retained test set; in a specific experimental embodiment, the model trained by the above process can achieve an entity recognition F1 score of 0.92 on the independent test set, verifying the effectiveness of the model in this domain task; the upper and lower limits of the values ​​in the data cleaning are mainly set according to the reasonable range of each parameter specified in national standards such as the "Environmental Monitoring Technical Specifications"; the basic time granularity is set to 1 hour; and the geographic location code adopts the national administrative division code.

[0063] Furthermore, after the above preprocessing, the data perception processing module outputs a first state feature, specifically a unified time-series feature table with spatiotemporal labels. Each record contains a source identifier, standardized time, spatial location, and specific feature values ​​after cleaning and transformation. In this embodiment, it can be expressed as: "City A, CO2 emission concentration: 125.6 mg / m³, Company A's output value: 5 million yuan, Policy B mentions 'photovoltaic subsidies': positive sentiment". The positive sentiment score is obtained by calculating the cosine similarity between the text fragment and the preset positive sentiment vocabulary, with a score range of 0 to 1, where a score greater than 0.6 is judged as positive.

[0064] The cross-domain indicator quantification module is used to dynamically call the indicator quantification model for real-time calculation based on the first market feature to generate a second state feature that updates over time. The quantified indicators in the second state feature include at least: the original indicator value calculated in real time based on the first state feature; and the derived indicator value generated by predictively adjusting the original indicator value based on historical sequences and real-time feedback through an online learning mechanism. The online learning mechanism continuously optimizes the adjustment parameters or algorithm for generating the derived indicator value based on the prediction error signal from the closed-loop learning feedback.

[0065] It should be noted that, to achieve the above functions, the cross-domain indicator quantification module internally maintains a structured meta-indicator library. The meta-indicator library is organized in the form of a knowledge graph, and its core fields include: "indicator dimension", "indicator name", "quantification formula / algorithm prototype", "required input data fields", and "unit of measurement". In this embodiment, under the "environmental benefits" dimension, there is a meta-indicator named "carbon emission intensity", whose quantified value is the ratio of total carbon dioxide emissions to industrial added value. The required input data fields are "regional CO2 emissions" and "regional industrial added value", and the unit of measurement is "tons / ten thousand yuan". Other dimensions also include at least "economic cost", "resource efficiency", "technology maturity" and "social impact", and each dimension has multiple similar meta-indicators pre-set.

[0066] In one possible implementation, the indicator quantification model is implemented through an adaptive model selection framework, the execution of which begins with a specific assessment task. In this embodiment, the assessment task is to assess the level of green industrial transformation in City A. Based on the task type, a specific indicator system for this assessment is dynamically constructed from the meta-indicator library. The logic is as follows: a task-indicator mapping rule table is pre-set. When a task is received, this table is queried, and relevant meta-indicators are automatically selected and combined. In this embodiment, for tasks of the "Regional Industrial Diagnosis" type, the rules instruct the loading of all indicators in the "Environmental Benefits" dimension and indicators such as "Environmental Protection Industry Investment Ratio" under the "Economic Costs" dimension. A preset initial weight based on expert experience is assigned to each indicator, thus forming the dynamic indicator system for this assessment. The preset initial weight is used in subsequent comprehensive scoring calculations to measure the relative importance of different indicators, and its initial value originates from the consensus value determined by domain experts using the Delphi method.

[0067] It should be noted that the model selection framework performs the following steps: Feature analysis is performed on the first state feature to determine the structure type, temporal completeness, and confidence level of the source data corresponding to the indicator to be quantified; wherein, temporal completeness is calculated based on the missing rate of data points within a specified time window, with a missing rate below 10% defined as "high," 10%-30% as "medium," and above 30% as "low"; the confidence level is directly mapped based on the data source; in this embodiment, data from calibrated IoT sensors is labeled "high," data from model estimation is labeled "medium," and data from statistical extrapolation is labeled "low"; feature analysis is performed on the first state feature to determine the structure type, temporal completeness, and confidence level of the source data corresponding to the indicator to be quantified; based on the results of the feature analysis, a preset quantity is selected... The system dynamically matches and calls at least one computational model from the quantization model library, which includes various models such as statistical parametric regression models, machine learning nonparametric interpolation models, and domain knowledge rule-based inference models. It listens for update events of the first state feature from the data perception and processing module. Whenever a new batch of data flows in, it automatically triggers a recalculation process for all related indicators, using the called computational model to perform real-time calculations on the source data, generating and outputting the second state feature. The second state feature is represented as a vector set containing all current indicator IDs, timestamps, calculated values, and data quality tags. The data quality tag is represented as an enumerated label that integrates confidence level and temporal completeness, used to indicate the reliability of the calculation result to downstream modules.

[0068] In this embodiment, the dynamic matching of the calculation model follows the following rules: Rule 1: If the source data of an indicator has a "high confidence level" and a "high temporal completeness", then the calculation formula stored in the meta-indicator library is directly called to calculate and output the original indicator value; Rule 2: If the source data has a "medium confidence level" or a "medium temporal completeness", then the "random forest-based regression imputation model" in the model library is called to estimate the missing or low-confidence parts using data from other relevant indicators; Rule 3: For qualitative indicators extracted from the text, the "sentiment analysis-based scoring model" is called to quantify them into scores between 0 and 1.

[0069] In this embodiment, the specific process of the online learning mechanism is as follows: the original index value refers to the instantaneous result directly calculated or estimated by the dynamic matching rules one to three of the calculation model; the derived index value is an indicator used to reflect the trend, which is obtained by secondary processing of the original index value, including but not limited to its "3-month moving average" and "the next period value predicted based on the ARIMA time series model"; the mechanism continuously receives the prediction error signal between the historical predicted value and the actual observed value, and uses this signal to optimize the prediction model that generates the derived index value online; it should be noted that the optimization object here is the weight parameter inside the prediction model; one specific implementation method is: for the trend prediction value generated by the linear regression model, the online learning mechanism uses the stochastic gradient descent rule to update the model weight parameter.

[0070] It should be noted that the key models in the quantitative model library need to be pre-built and trained. Specifically, for the random forest-based regression imputation model, the goal is to estimate the value of an indicator when the source data is missing or has low confidence. The construction process includes: collecting historical data for periods with complete data; using the indicator to be imputed as the target variable; and using 5-10 other logically related indicators, such as industrial output, total energy consumption, and temperature during the same period, as feature variables to form a training dataset. The scikit-learn library is used for training, and the number of decision trees is determined through grid search combined with five-fold cross-validation, with a typical optimal value of 100. Mean squared error is used as the loss function for fitting. In this embodiment, when it is necessary to calculate the "carbon emission intensity of City A" but the "industrial added value" data is missing, the random forest-based regression imputation model is called, using five features at the same time point: "electricity consumption of City A," "transportation volume," "average temperature," and "last month's industrial added value." The system takes as input and outputs an estimated value for the missing "industrial added value" to complete the indicator calculation. For the sentiment analysis-based scoring model, it is used to quantify the qualitative information extracted from text intelligence into numerical scores. Its construction process includes: collecting texts such as news reports, policy interpretations, and industry comments in the environmental protection field, and having experts label their sentiment tendencies, including but not limited to positive, neutral, and negative, to form an annotated corpus; using the pre-trained language model RoBERTa-base as a base, fine-tuning the training on the annotated corpus, treating it as a three-class classification task; training parameters include: learning rate 3e-5, batch size 16, and 3 training rounds; the trained model must achieve an accuracy of 0.88 on the reserved test set before it can be used for sentiment scoring in the production environment; in this embodiment, when processing a text record of "Policy B mentions photovoltaic subsidies", for the sentiment analysis-based scoring model, this text is used as input, and a structured result containing "sentiment category" and "confidence score" is output, and the confidence score can be directly used as part of the data quality label.

[0071] The dynamic correlation analysis module is used to analyze and update the dynamic correlation strength between various quantitative indicators in the second state feature in real time through a preset dynamic graph calculation model, and generate a third state feature that describes the interaction relationship between cross-domain indicators.

[0072] Specifically, the dynamic correlation analysis module characterizes each quantitative indicator in the second state feature and its dynamic correlation strength as a dynamic graph structure, wherein: each quantitative indicator is mapped to a node in the graph. , forming a set of nodes ; will indicators and Between moments The identified significant relationships are mapped to edges in the graph. , forming an edge set For each edge Assign a weight value , forming a weight set The weight values ​​are used to quantify the indicators. right The dynamic correlation strength at time t; continuously outputting its dynamically updated graph structure. As a third state feature.

[0073] It should be noted that initially, there are no edges between nodes, and the edge set is empty. The relationships between nodes and edges are stored and efficiently retrieved using an adjacency list, ensuring that the graph structure can dynamically add and delete nodes and edges and quickly respond to updates to association relationships. When an updated second state feature is received, the dynamic association analysis module is triggered, acquiring all historical sequence data of relevant indicators within a preset sliding time window. The length of the sliding time window is a configurable parameter, typically set according to the business analysis cycle. An incremental calculation strategy is adopted, focusing on indicator pairs that may be related to new data based on business rules or configuration. For each indicator pair to be calculated, the module calls the corresponding algorithm to calculate the original association strength at the current moment, based on its selection strategy. After calculating the original correlation strength, from the current graph structure Query the weight of the edge at the previous time step. If the edge does not exist, then consider it as... Set to 0 and apply the smoothing formula. Calculate new weights ,in, This represents the raw value of the association strength calculated based on the data at the current moment. This is represented as a preset learning rate parameter, specifically a hyperparameter between 0 and 1, used to control the smoothness of weight updates; The larger the value, the greater the weight on the newly calculated The more sensitive; The smaller the value, the smoother the weight change and the greater the historical inertia; in a typical configuration of this embodiment, The value can be 0.1, determined based on the 80th percentile of the pilot data distribution, indicating that only the associations with the strongest intensity in the top 20% are retained; further, the judgment... Does it exceed the association significance threshold? In this embodiment, the association significance threshold is 0.1. If it exceeds, then in the graph... Create or update from point to The edge or undirected edge, and its weight is set to If the value is below the threshold, the edge is removed.

[0074] In one possible implementation, for the index and Obtain the raw values ​​of association strength from time series data. And determining the direction of the edges to infer directional causal relationships between time series indicators, including: fitting the following regression model within a sliding time window and calculating its sum of squared residuals: Constrained model: Unrestricted model: ;in, Represented as the lag order, Represented as autoregressive coefficients, Represented as a lag order index, , Represented as indicators and The observation at the k-th time unit before time point t, , This represents the prediction residuals of the constrained and unconstrained models at time t. Represented as cross-regression coefficients; the sum of squared residuals based on the constrained model and the unconstrained model. and Calculate the F-statistic:

[0075]

[0076] in, This represents the total number of valid time series data points within the sliding time window. In this embodiment, to analyze the short-term impact of daily market transactions on emissions, T can be set to 30 days; to analyze the medium- and long-term impact of annual technology policies on industrial structure, T can be set to 24 months; for monthly industrial monitoring, T is set to 12 months; if the F statistic indicates the indicator... Historical data for indicators If the current value has a significant causal effect, then a sub-node is created in the dynamic graph. Pointing to node The directed edges, and the original values ​​of the calculated association strength. Set as the F-statistic or the cross-regression coefficient in the unrestricted model. The norm of .

[0077] It should be noted that the lag order The p-value indicates how many past time units of data are considered in the test. A p-value that is too large may lead to overfitting, while a p-value that is too small may miss crucial influences. In this embodiment, the AIC information criterion is used to automatically determine the optimal p-value, with the search range typically set to 1 to 12. The statistical significance of the causal relationship is determined by the calculated F-statistic. In this embodiment, at a 95% confidence level, the index... Historical data for indicators The current value has a significant causal effect; at this point, the original association strength... You can use the F-statistic or the cross-regression coefficient. The L2 norm is used to quantify the intensity of the influence.

[0078] In one possible implementation, the raw value of the association strength is obtained. Mutual information can also be calculated. This is used to capture generalized linear and non-linear dependencies between indicators, regardless of direction, including: for indicators and Estimate their joint probability distribution within the current time window and marginal probability distribution and ; Calculation indicators and Inter-information Specifically, it is expressed as:

[0079]

[0080] If the mutual information value If the salience exceeds a preset threshold, then in the graph structure Nodes in and Create an undirected edge between them and its original correlation strength value. Set as the mutual information value In this embodiment, the significance threshold is taken as the 95th percentile of the distribution.

[0081] It should be noted that obtaining the raw value of the association strength... The method is determined according to a preset selection strategy: if the indicator and If the data is a high-quality time series and the analysis task requires a clear correlation direction, then methods based on constrained and unconstrained models should be prioritized for calculation. If the indicator and If the data relationship is expected to be complex and nonlinear, or the data is an irregular time series (i.e., one of the indicators is a score obtained from text sentiment analysis), then the mutual information calculation method is used to calculate... In this embodiment, if the indicators mark the data quality of both parties as "high" and there is a clear potential causal direction in the business logic, then in order to analyze the impact of "environmental protection penalty intensity" on "the number of times enterprises violate emission regulations", the method based on the constrained model and the unconstrained model will be preferred.

[0082] In this embodiment, the effectiveness of the green transformation of the industrial park in City A is evaluated by continuously monitoring indicators such as "carbon emissions per unit output" v1, "proportion of clean technology investment" v2, "industrial water reuse rate" v3, and "air quality good rate" v4. On a certain day t, a batch of second-state features is received, showing a significant improvement in v2. The recalculation of the correlation pairs (v2,v1), (v2,v3), and (v2,v4) is then initiated. For (v2,v1), due to the high data quality and the need to determine the directional impact of investment on carbon reduction, a method based on both constrained and unconstrained models is used to calculate the result. At p=3, the F-statistic is 5.5. If the historical weight is 4.8 and λ=0.1 at this time, the new weight is 4.87, far exceeding the threshold of 0.1. Therefore, a directed edge from "proportion of clean technology investment" v2 to "carbon emissions per unit output" v1 is created and strengthened in the dynamic graph, with the weight updated to 4.87, and recorded as: "Clean technology investment is an important factor driving carbon emission reduction."

[0083] The deduction and optimization module is used to deduce the development trend of the ecological and environmental protection industry with the third state characteristics as constraints, and to generate a set of optimization schemes by simulating the evolution of various quantitative indicators through a multi-objective optimization algorithm.

[0084] It should be noted that the input of the inference and optimization module is the third state feature from the dynamic correlation analysis module and the second state feature updated in real time from the cross-domain index quantification module. Its output is a structured set of optimization schemes containing multiple alternative paths. Each scheme contains at least a set of decision variable settings, the future predicted trajectory of key indicators, and the multi-objective comprehensive evaluation results.

[0085] Specifically, the core of inferring the development trend of the ecological and environmental protection industry is a hybrid prediction model, which combines a graph neural network with a long short-term memory network time-series prediction model. Its construction and workflow are as follows: The input data consists of two parts. The first part is a sequence of historical indicator state vectors over the past N time units; the second part is the dynamic correlation graph at the current moment, whose node and edge relationships are converted into a graph structure input for the model, used to characterize the interaction network between indicators. The graph neural network is used to perform embedding learning on the input dynamic correlation graph, extracting the features of the topological relationships between indicators, and inputting them together with the historical indicator state vector sequence into the long short-term memory network, which then... The model learns the evolution patterns of the learning indicators themselves and those influenced by related indicators, and outputs a predicted sequence of indicator values ​​for the next M time units. In this embodiment, the hybrid prediction model requires supervised training using historical data. Its training data consists of historical indicator sequences and corresponding, post-validated dynamic correlation graphs. The loss function uses mean squared error to minimize the difference between the predicted and observed values. The optimizer uses Adam, with an initial learning rate typically set between 1e-3 and 5e-4, and early stopping is performed using validation set performance to prevent overfitting. In this embodiment, the graph neural network can be set to 2 layers, and the long short-term memory network can be set to 128 hidden units.

[0086] It should be noted that the hybrid prediction model adopts an encoder-decoder architecture. The encoder part is composed of the graph neural network, which is responsible for encoding the dynamic association graph into feature vectors of indicator nodes. The decoder part is composed of the long short-term memory network, whose input is the concatenation of historical indicator sequences and node feature vectors output by the encoder, and is responsible for time series prediction.

[0087] Furthermore, a multi-objective evolutionary algorithm is adopted to search for the optimal combination of decision parameters that maximizes the comprehensive benefits of industrial development under the constraints of dynamic correlation. The specific implementation process is as follows: Decision variables are the policy or investment parameters to be optimized, and the feasible range of each variable needs to be pre-defined; in this embodiment, they can be represented as "the proportion of fiscal subsidies for clean technology R&D" and "the benchmark price for carbon emission trading"; Objective function is defined as a target vector that needs to be optimized simultaneously, and each objective is a mathematical function of a relevant quantitative indicator; in this embodiment, it can be represented as F=[maximize (the growth rate of green industrial output value), minimize (the total amount of regional carbon emissions), minimize (the total fiscal cost of the policy)]; Constraints come from the third state feature, that is, changing a decision variable will drive changes in its directly related indicators. The changes will be transmitted to secondary related indicators based on the weight and direction of the edges in the graph, forming a chain calculation, and the relationship between all indicators must conform to the dynamic correlation strength range described in the graph of the third state feature; Execution of optimization algorithm: In this embodiment, a non-dominated sorting genetic algorithm with an elite strategy is used as the implementation of the multi-objective optimization algorithm, and its operation requires configuration of key The parameters, and their typical values ​​in this embodiment, are set as follows: the population size is set to 100; the number of generations is 200; the crossover probability and mutation probability are 0.85 and 0.05, respectively. The specific execution steps of the optimization algorithm include: randomly initializing a population containing a combination of decision variables of the population size; in each generation, for each individual in the population, i.e., a set of decision variables, calling the hybrid prediction model to simulate the evolution trajectory of various indicators in the future period driven by the set of variables, and calculating its multiple objective function values ​​accordingly; performing non-dominated sorting and selection based on the Pareto dominance relationship of the solutions, and generating the next generation population through crossover and mutation operations; repeating until the preset number of generations is reached; after the optimization algorithm is completed, outputting the non-dominated solution set in the final population, i.e., the Pareto optimal frontier; the non-dominated solution set is constructed into the optimization scheme set, and each scheme in the set clearly records: the combination of decision variables, the simulated evolution trajectory of key indicators from the present to the future, and the achieved values ​​of the objective functions of each sub-item under the scheme; it should be noted that there is no absolute superiority or inferiority between the schemes, only showing the trade-off relationship between different objectives.

[0088] In one specific implementation, to assess the path for S Industrial Park to reach carbon peak before the end of the year, the deduction and optimization module operates as follows: It receives the current values ​​and historical sequences of indicators such as "industrial output energy intensity" and "renewable energy ratio" for the park, as well as a dynamic correlation graph revealing the causal and correlation relationships between indicators such as "energy structure," "investment," "carbon emissions," and "technology costs." Based on historical data and the existing correlation graph, the hybrid prediction model predicts that, under unchanged policy conditions, the carbon emission trajectory will peak in year T1. The decision variables are set as "annual energy-saving renovation investment growth rate" and "mandatory photovoltaic installation ratio." For example, the objectives are to "minimize the peak year" and "minimize the cumulative total cost". Under the constraint of the dynamic correlation graph, the NSGA-II algorithm can be expressed as follows: increased investment will affect the reduction of technology costs and carbon emissions through graph correlation. Different combinations of variables are searched to generate a set of three typical Pareto optimal solutions: Solution A: high investment, high proportion, expected to peak in TA year, high total cost; Solution B: medium parameters, expected to peak in TB year, moderate cost; Solution C: low parameters, expected to peak in TC year, lowest total cost. Each solution is accompanied by a detailed annual indicator simulation data table.

[0089] The intelligent deduction module is used to perform deduction on the set of optimization schemes and output a structured decision report that includes recommended actions, expected effects and risk warnings.

[0090] It should be noted that the intelligent deduction module is composed of a rule-based logical reasoning unit and a case-based analogical reasoning unit connected in series. Its working objective is to perform feasibility filtering, business value assessment and implementation risk deduction on multiple optimization solutions input from upstream, and finally generate a report that can directly support management decisions.

[0091] Specifically, the construction and initialization of rule-based logical reasoning units and case-based analogical reasoning units include: constructing an extensible business rule base, which can be described and stored in the form of "IF <condition> THEN <action or conclusion>", mainly divided into: feasibility rules, used to quickly filter out solutions that do not meet hard constraints; in this embodiment, it includes "IF total fiscal cost of the solution > the upper limit of this year's environmental protection special budget THEN marked as 'infeasible'"; priority rules, used to initially score feasible solutions; in this embodiment, it includes "IF the predicted carbon emission reduction of the solution > 120% of the annual baseline target THEN priority score + 5"; constructing a historical case library, where each historical decision case is abstracted into a structured object, containing: situation feature vector: recording the industry status at the time of decision-making, composed of normalized values ​​of key indicators; action plan taken: recording the combination of decision variables implemented at that time; implementation effect vector: recording the rate of change of key result indicators after a period of time after the action is implemented; experience label: a qualitative summary marked by manual annotation; in this embodiment, it can be represented as "success: win-win for the economy and environment", "failure: excessive impact on social employment", "risk: unstable technology supply chain".

[0092] It should be noted that the <condition> section of the rules in the business rule base can call the indicator values ​​in the second state characteristics or perform logical and mathematical operations on the indicator values; its rules are initialized by domain experts based on policies, regulations, financial standards and business best practices, and can be maintained through the administrator interface; the cases in the historical case base come from publicly available industry planning assessment reports, enterprise internal project post-evaluation archives, and the simulation history of this system running in the simulation environment, and are stored in the historical case base after being verified by experts.

[0093] Furthermore, the specific workflow of the scheme derivation is as follows: Each scheme in the optimized scheme set is traversed and substituted into all feasibility rules in the business rule base for matching calculation; all schemes marked as "infeasible" by any feasibility rule are filtered out; the remaining schemes constitute a subset of initially qualified schemes; for each initially qualified scheme, the following is performed: a feature vector describing the current decision-making situation is constructed by combining the current second-state features, third-state features, and the scheme itself; the similarity between the current feature vector and the situation feature vector of each case in the historical case base is calculated; in this embodiment, the similarity algorithm uses weighted cosine similarity; specifically, for two vectors A and B, their similarity... ,in, The weight of indicator vi represents its importance in matching. The weight is determined by expert scoring and returns the K most similar historical cases. In this embodiment, K is 3. The implementation effect vector and experience label of the most similar historical case are used as the reference baseline. The action plan of the current plan is compared with the historical action plan, and the chain effect that the difference may cause is simulated based on the current dynamic association graph. In this embodiment, if the current plan adds "subsidies for energy storage technology" compared with similar historical cases, the additional impact on downstream indicators is estimated along the path of "energy storage technology" -> "grid absorption capacity" -> "renewable energy installed capacity" -> "carbon emissions" in the association graph, and the weight of the edge is used as the influence transmission coefficient. At least the following risks are taken into account: risks mentioned in the experience labels of similar historical cases; any key negative indicator shows a deteriorating trend in the chain reaction simulation; key decision variables in the plan touch the risk threshold preset in the rule base.

[0094] Furthermore, the generation logic of the structured decision report is as follows: The report template fixedly includes the following fields: Recommended Action: Combines the decision variables in the plan and automatically translates them into natural language descriptions through a preset template; in this embodiment, it can be expressed as: "It is recommended to increase the subsidy standard for industrial water-saving technology transformation in City A to 200 yuan / ton and reduce the annual carbon emission allowance for Industry B by 5%"; Expected Effects: A list extracting the predicted values ​​of 3-5 core indicators for the next 1st, 3rd, and 5th years and their percentage change relative to the baseline scenario from the predicted trajectory and extrapolation results of the plan; Risk Identification: A list, each risk package Includes a risk description, potentially affected entities, and severity level, with values ​​of "high," "medium," and "low," determined jointly by the magnitude of changes in risk indicators and the rule base; A comprehensive score: a score from 0 to 100, calculated by weighting the priority rule score, the positive deviation from the most similar case's effect, and the negative risk severity score, used for ranking among solutions; In this embodiment, the comprehensive score comprises 40% for the priority rule score, 30% for the positive deviation from the most similar case's effect, and 30% for the negative risk severity score; Reference cases: IDs of cited Top-K similar historical cases and brief conclusions.

[0095] In this embodiment, when three optimized plans for "zero-carbon transformation of C City High-tech Zone" were received: after initial screening according to rules, Plan 3 was filtered out because "the annual investment amount exceeds the district's fiscal affordability limit"; case matching was performed on the remaining plans, and Plan 1 had a similarity of 0.85 with the case of "Shenzhen D District 2019 Green Industry Stimulus Plan", with good historical results; the analysis showed that Plan 1 could strongly drive the reduction of carbon emissions, but may slightly affect the short-term GDP growth rate, with a risk level of "medium"; Plan 2 matched the case of "City B's radical closure of traditional industries", with a similarity of 0.78, and the case was labeled "failure: large social backlash", and the analysis showed that its employment risk level was "high"; finally, the decision report recommended Plan 1 and listed in detail the expected carbon reduction path, the supporting employment training plan, and the successful cases for reference, providing decision-makers with a clear and evidence-based basis for comparison.

[0096] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0097] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A system for comprehensive evaluation of the development of the ecological and environmental protection industry, characterized in that, include: Data sensing and processing module: used to actively access and collect the first state signal in real time through a dedicated data adapter, and to preprocess the first market signal to obtain the first state characteristics; Cross-domain indicator quantification module: used to dynamically call the indicator quantification model for real-time calculation based on the first market feature, so as to generate a second state feature that updates over time; Dynamic correlation analysis module: It is used to analyze and update the dynamic correlation strength between various quantitative indicators in the second state feature in real time through a preset dynamic graph calculation model, and generate a third state feature that describes the interaction relationship between cross-domain indicators. The deduction and optimization module is used to deduce the development trend of the ecological and environmental protection industry with the third state characteristics as constraints, and to generate a set of optimization schemes by simulating the evolution of various quantitative indicators through a multi-objective optimization algorithm. Intelligent simulation module: used to perform scheme simulation on the set of optimization schemes and output a structured decision report containing recommended actions, expected effects and risk warnings.

2. The system for comprehensive evaluation of the development of the ecological and environmental protection industry according to claim 1, characterized in that: The data sensing and processing module, including a dedicated data adapter, comprises at least: An environmental monitoring adapter unit is used to access the first signal stream of the Internet of Things sensor network and remote sensing data platform. The first signal stream includes environmental quality parameters and resource consumption measurement data related to ecological and environmental protection. The industrial economic adaptation unit is used to access the second signal stream of the enterprise ERP and market trading platform. The second signal stream includes economic operation indicators, production activity data and market transaction dynamics. The intelligence text adaptation unit is used to access and capture third signal streams from policy release platforms, patent databases of scientific research institutions, and public opinion information sources. The third signal streams include policy regulations, technical intelligence, and market opinion information in the form of unstructured text. The first status signal is composed of real-time signal streams collected in parallel by the environmental monitoring adaptation unit, the industrial economic adaptation unit, and the intelligence text adaptation unit.

3. The system for comprehensive evaluation of the development of the ecological and environmental protection industry according to claim 1, characterized in that: The cross-domain indicator quantization module generates the indicator quantization model of the second state feature through an adaptive model selection framework, which executes as follows: Feature analysis is performed on the first state feature to determine the structure type, temporal completeness, and confidence level of the source data corresponding to the indicator to be quantified; Based on the results of the feature analysis, at least one computational model is dynamically matched and called from a pre-set quantization model library, wherein the quantization model library includes multiple types of models, such as statistical parametric regression models, machine learning nonparametric interpolation models, and domain knowledge rule-based inference models. The source data is calculated in real time using the invoked computing model to generate and output the second state feature.

4. A system for comprehensive evaluation of the development of the ecological and environmental protection industry according to claim 3, characterized in that: The quantitative indicators in the second state characteristic include at least: The original index value is calculated in real time based on the first state feature; and, Derivative index values ​​are generated by predictively adjusting the original index values ​​based on historical sequences and real-time feedback through an online learning mechanism. The online learning mechanism continuously optimizes the adjustment parameters or algorithms for generating the derived index values ​​based on the prediction error signal from the closed-loop learning feedback.

5. A system for comprehensive evaluation of the development of the ecological and environmental protection industry according to claim 1, characterized in that: The dynamic correlation analysis module is used to characterize the quantitative indicators and their dynamic correlation strengths in the second state features as a dynamic graph structure, wherein: Map each quantitative indicator to a node in the graph. , forming a set of nodes ; indicators and Between moments The identified significant relationships are mapped to edges in the graph. , forming an edge set ; For each edge Assign a weight value , forming a weight set The weight values ​​are used to quantify the indicators. right The dynamic correlation strength at time t; Meanwhile, the calculation and updating of its edge weights are specifically expressed as follows: in, This represents the raw value of the association strength calculated based on the data at the current moment. This is represented as the preset learning rate parameter; It continuously outputs its dynamically updated graph structure. As a third state feature.

6. A system for comprehensive evaluation of the development of the ecological and environmental protection industry according to claim 5, characterized in that: The dynamic correlation analysis module, for indicators and Obtain the raw values ​​of association strength from time series data. And determining the direction of the edges, specifically including: Within the sliding time window, fit the following regression model and calculate its sum of squared residuals: Constrained model: ; Unrestricted model: ;in, Represented as the lag order, Represented as autoregressive coefficients, Represented as a lag order index, , Represented as indicators and The observation at the k-th time unit before time point t, , This represents the prediction residuals of the constrained and unconstrained models at time t. Expressed as cross-regression coefficients; The sum of squared residuals based on the constrained model and the unconstrained model and Calculate the F-statistic: in, This represents the total number of valid time series data points within the sliding time window; If the F-statistic indicates the indicator Historical data for indicators If the current value has a significant causal effect, then a sub-node is created in the dynamic graph. Pointing to node The directed edges, and the original values ​​of the calculated association strength. Set as the F-statistic or the cross-regression coefficient in the unrestricted model. The norm of .

7. A system for comprehensive evaluation of the development of the ecological and environmental protection industry according to claim 5, characterized in that: The dynamic correlation analysis module obtains the original value of the correlation strength. Mutual information can also be calculated. Specifically, it includes: For indicators and Estimate their joint probability distribution within the current time window and marginal probability distribution and ; Calculation indicators and Inter-information Specifically, it is expressed as: If the mutual information value If the salience exceeds a preset threshold, then in the graph structure Nodes in and Create an undirected edge between them and its original correlation strength value. Set as the mutual information value .

8. A system for comprehensive evaluation of the development of the ecological and environmental protection industry according to claim 5, characterized in that: The dynamic correlation analysis module determines the original value for calculating the correlation strength based on a preset selection strategy. Specifically, the selection strategy includes: If the indicator and If the data is a high-quality time series and the analysis task requires a clear correlation direction, then methods based on constrained and unconstrained models should be prioritized for calculation. ; If the indicator and If the data relationships are expected to be complex and nonlinear, or if the data is an irregular time series, then the mutual information calculation method is used to calculate... .

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