Two-type industry intelligent diagnosis and optimization method and system based on multi-dimensional data fusion

Abstract

The application discloses a two-type industry intelligent diagnosis and optimization method and system based on multi-dimensional data fusion, first establishes a two-type industry evaluation index system; then adopts a machine learning algorithm to train an intelligent diagnosis model, analyzes data modes from the dimensions of resource utilization efficiency, environmental influence degree and economic output benefit, determines an industry short board identification result; further processes the identification result through a deep learning algorithm, generates a customized optimization scheme including a technology upgrading path and a resource configuration adjustment strategy in combination with policy and benchmark data; if the customized optimization scheme does not match real-time data feedback, an updated scheme is obtained by adjusting fusion data input, and dynamic iteration processing and visual monitoring are performed until convergence to two-type development standards, and finally, an application scene is simulated to evaluate the effect and update the index system. The embodiment realizes accurate diagnosis and closed-loop optimization of the industry short board, improves the resource saving and environment-friendly level, and supports sustainable iterative development.

Description

Technical Field

[0001] This invention relates to the field of two-type industries technology, and in particular discloses a method and system for intelligent diagnosis and optimization of two-type industries based on multi-dimensional data fusion. Background Technology

[0002] Existing methods often reveal significant shortcomings when addressing the complex needs of developing both resource-saving and environmentally friendly industries. Many traditional approaches lack comprehensive consideration of multiple factors when processing information, leading to incomplete assessments of the industry's current state and an oversight of hidden problems. Furthermore, existing methods frequently fail to adjust strategies promptly in the face of a dynamically changing environment, resulting in diagnostic and improvement measures that are out of touch with actual needs and ill-suited to the rapidly evolving industrial landscape.

[0003] A deeper technical challenge lies in how to effectively integrate and collaboratively analyze multi-source information. Information from different sources, such as economic performance, resource usage, and environmental impact data, each has different characteristics and modes of expression, exhibiting significant differences in format and dimension. These differences make it difficult to establish a unified basis for judgment during analysis, thus affecting the accurate identification of industry problems. For example, in a manufacturing company, resource consumption data might show low production efficiency, while environmental emissions data might not exceed standards, and economic output data might point to excessive costs. If this information cannot be integrated into a holistic analytical framework, it becomes difficult to determine whether the problem requires adjustments to the production process or upgrades to technology and equipment; the root cause of the problem remains unclear.

[0004] Therefore, how to break down the barriers between multiple sources of information and form an analytical system that can comprehensively reflect the current state of the industry has become a key issue that urgently needs to be addressed in the diagnosis and optimization of the two types of industries. Summary of the Invention

[0005] This invention provides a method and system for intelligent diagnosis and optimization of two types of industries based on multidimensional data fusion, aiming to solve at least one defect in the above-mentioned prior art.

[0006] One aspect of this invention relates to a method for intelligent diagnosis and optimization of two types of industries based on multi-dimensional data fusion, comprising the following steps:

[0007] S100. Acquire industrial economic data, resource consumption data, environmental emission data, and policy standard data. Remove noise and outliers through data cleaning. Then, perform feature extraction and data fusion modeling to obtain the evaluation index system for two types of industries, which includes resource-saving industries and environmentally friendly industries.

[0008] S200: Employ machine learning algorithms to train an intelligent diagnostic model for the evaluation index system of two types of industries. Analyze data patterns from the dimensions of resource utilization efficiency, environmental impact, and economic output benefits to determine the identification results of industry weaknesses, where industry weaknesses refer to the weak links in industrial development.

[0009] S300 uses deep learning algorithms to process the results of industry weakness identification, and combines policy guidance data and industry benchmark data to generate customized optimization solutions, including technology upgrade paths and resource allocation adjustment strategies.

[0010] S400. Determine whether the resource configuration adjustment part in the customized optimization scheme matches the real-time data feedback. If they do not match, adjust the fused data input by comparing the scheme parameters with the feedback threshold to obtain the updated optimization scheme. The feedback threshold is set based on historical data.

[0011] S500: For the updated optimization scheme, obtain real-time data feedback for dynamic iterative processing, use visual monitoring function to track the changes in scheme deduction, determine whether the iteration converges to the two-type development requirements, and obtain the final closed-loop optimization result.

[0012] S600, based on the final closed-loop optimization results, simulates industrial application scenarios, evaluates the effectiveness of the optimization scheme, and updates the evaluation index system for the two types of industries to support subsequent iterations.

[0013] Further, step S100 includes:

[0014] S110. By obtaining raw records from the industrial economic database and environmental emission monitoring points, the raw records are preliminarily processed, and data cleaning tools are used to remove noise and missing parts to obtain the sorted initial dataset.

[0015] S120. Based on the initial dataset, feature selection is performed on key indicators of resource-saving industries and environmentally friendly industries. Statistical tools are used to analyze the distribution characteristics of key indicators of resource-saving industries and environmentally friendly industries to determine the core feature combination.

[0016] S130. If some indicators in the core feature combination exceed the preset threshold, the initial dataset is modeled using a data integration tool to obtain the evaluation index system for the two types of industries.

[0017] Further, step S200 includes:

[0018] S210. Employ machine learning algorithms to train an intelligent diagnostic model for the evaluation index system of the two types of industries;

[0019] S220. Based on the trained intelligent diagnostic model, analyze the data patterns from the dimensions of resource utilization efficiency, environmental impact, and economic output benefits to determine the identification results of industrial weaknesses.

[0020] Further, step S300 includes:

[0021] S310. Based on the results of the industry weakness identification, use deep learning algorithms to deeply mine the weakness data and construct a deep learning algorithm model.

[0022] S320, by combining policy guidance data and industry benchmark data, uses deep learning algorithm models to achieve three-dimensional matching and logical integration of shortcomings, policy requirements, and benchmark experience, generating customized optimization solutions.

[0023] Further, step S400 includes:

[0024] S410. Compare the resource configuration adjustment part of the customized optimization plan with the real-time data feedback, and use the data comparison tool to determine the degree of matching to obtain the preliminary matching result;

[0025] S420. If the initial matching results show inconsistency, the key parameter values ​​in the customized optimization scheme are obtained through the parameter extraction tool and compared with the feedback threshold set based on historical data benchmark to determine the parameter deviation range.

[0026] S430. For the parameter deviation range, use a data fusion tool to adjust the content of the fusion data input, generate the adjusted input dataset, and obtain the updated data combination;

[0027] S440. Apply the updated data combination to the adjustment logic rules of the customized optimization scheme through the data integration tool to generate the updated optimization scheme.

[0028] Further, step S500 includes:

[0029] S510. For the updated optimization scheme, obtain real-time data feedback, use data acquisition tools to obtain the latest operating data from multiple sources, compare the latest operating data with the environmental standards, and obtain the judgment result of the data deviation range.

[0030] S520. If the data deviation range exceeds the preset threshold, the dynamically adjusted content is re-integrated through the data fusion tool, key variables are extracted from the feedback loop, and the adjusted data combination content is determined.

[0031] S530. For the adjusted data combination content, a visualization monitoring tool is used to record the change trajectory of the scheme in real time, and to obtain the trend information of iterative convergence during change tracking.

[0032] S540. The trend information of iterative convergence is compared with the preset criteria for resource conservation and environmental friendliness by using a closed-loop optimization tool to obtain the final closed-loop optimization result.

[0033] Further, step S600 includes:

[0034] S610. Based on the scenario data in the final closed-loop optimization results, extract relevant information from the pre-established database, compare the extracted relevant information with industry standards, and determine the degree of adaptation of the scenario data.

[0035] S620. Based on the adaptability of the scene data, a data processing tool is used to virtually map the optimization scheme. It is determined whether the mapped content of the optimization scheme meets the preset threshold. If it does not meet the threshold, dynamic adjustment is performed to obtain the adjusted scheme content.

[0036] S630. For the adjusted plan content, obtain the data feedback required for effect evaluation, extract performance data from the virtual operating environment, compare it with environmental factors, and determine the evaluation results.

[0037] S640. Based on the assessment results, update the evaluation index system for resource-saving and environmentally friendly industries using integrated tools.

[0038] Another aspect of the present invention relates to a two-type industrial intelligence diagnosis and optimization system based on multi-dimensional data fusion, used to execute the above-described two-type industrial intelligence diagnosis and optimization method based on multi-dimensional data fusion, comprising:

[0039] The module for acquiring the evaluation index system for resource-saving and environmentally friendly industries is used to acquire industrial economic data, resource consumption data, environmental emission data, and policy and standard data. Noise and outliers are removed through a data cleaning process, and then feature extraction and data fusion modeling are performed to obtain the evaluation index system for resource-saving and environmentally friendly industries.

[0040] The module for determining the identification results of industrial weaknesses is used to train an intelligent diagnostic model for the evaluation index system of two types of industries using machine learning algorithms. It analyzes data patterns from the dimensions of resource utilization efficiency, environmental impact, and economic output benefits to determine the identification results of industrial weaknesses, where industrial weaknesses refer to the weak links in industrial development.

[0041] The customized optimization solution generation module is used to process the industry weakness identification results through deep learning algorithms, combine policy guidance data and industry benchmark data, and generate customized optimization solutions, including technology upgrade paths and resource allocation adjustment strategies.

[0042] The optimization scheme update module is used to determine whether the resource configuration adjustment part in the customized optimization scheme matches the real-time data feedback. If they do not match, the fused data input is adjusted by comparing the scheme parameters with the feedback threshold to obtain the updated optimization scheme. The feedback threshold is set based on historical data.

[0043] The final closed-loop optimization result acquisition module is used to obtain real-time data feedback for dynamic iterative processing of the updated optimization scheme, and to use the visualization monitoring function to track the changes in the scheme deduction, determine whether the iteration converges to the two-type development requirements, and obtain the final closed-loop optimization result.

[0044] The two-type industry evaluation index system update module is used to simulate industrial application scenarios based on the final closed-loop optimization results, evaluate the effectiveness of the optimization scheme, and update the two-type industry evaluation index system to support subsequent iterations.

[0045] The beneficial effects achieved by this invention are as follows:

[0046] This invention discloses a method and system for intelligent diagnosis and optimization of two types of industries based on multi-dimensional data fusion. It addresses unique business scenarios in industrial economic development, specifically how to identify industry weaknesses characterized by low resource utilization efficiency, significant environmental impact, and insufficient economic output through a data-driven approach. The method integrates policy guidance and industry benchmarks to generate customized optimization solutions, achieving resource conservation and environmentally friendly sustainable development. First, this invention acquires data on industrial economics, resource consumption, environmental emissions, and policy standards. After cleaning and feature extraction, a two-type industry evaluation index system is established. Then, machine learning algorithms are used to train an intelligent diagnostic model, analyzing data patterns from the dimensions of resource utilization efficiency, environmental impact, and economic output to determine the industry weakness identification results. Next, deep learning algorithms process the identification results, combining policy and benchmark data to generate customized optimization solutions, including technology upgrade paths and resource allocation adjustment strategies. If the customized optimization solution does not match real-time data feedback, the fused data input is adjusted to obtain an updated solution, and dynamic iterative processing and visual monitoring are performed until convergence to the two-type development standards. Finally, the application scenario is simulated to evaluate the effect and update the index system. This invention enables precise diagnosis and closed-loop optimization of industrial shortcomings, improves resource conservation and environmental friendliness, and supports sustainable iterative development. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating an embodiment of the intelligent diagnosis and optimization method for two types of industries based on multidimensional data fusion according to the present invention.

[0048] Figure 2 This is a functional block diagram of an embodiment of a two-type industrial intelligent diagnosis and optimization system based on multi-dimensional data fusion according to the present invention.

[0049] Explanation of icon numbers:

[0050] 10. Module for obtaining evaluation index system for resource-saving and environment-friendly industries; 20. Module for determining the results of industry weakness identification; 30. Module for generating customized optimization solutions; 40. Module for updating optimization solutions; 50. Module for obtaining final closed-loop optimization results; 60. Module for updating evaluation index system for resource-saving and environment-friendly industries. Detailed Implementation

[0051] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0052] like Figure 1 As shown, the first embodiment of the present invention proposes a two-type industrial intelligent diagnosis and optimization method based on multi-dimensional data fusion, including the following steps:

[0053] Step S100: Obtain industrial economic data, resource consumption data, environmental emission data, and policy standard data. Remove noise and outliers through data cleaning, and then perform feature extraction and data fusion modeling to obtain the evaluation index system for two types of industries, which includes resource-saving industries and environmentally friendly industries.

[0054] First, we comprehensively collected four types of key data: industrial economic data (such as output value, profit margin, input-output ratio, and other benefit-related data), resource consumption data (such as energy consumption, raw material utilization rate, water resource consumption, and other conservation-related data), environmental emission data (such as pollutant emissions, waste treatment rate, carbon emission intensity, and other environmental protection-related data), and policy and standard data (such as national / local environmental protection limits, energy conservation requirements, and compliance-related data such as policies supporting resource-saving and environmentally friendly industries). We then performed data cleaning on the collected raw data, including outlier detection (such as Z-score method and box plot method) and noise filtering (such as smoothing). The data cleaning process involves several steps: first, data processing and duplicate removal to eliminate invalid information and ensure data quality; then, feature extraction techniques (such as principal component analysis and key indicator screening) to extract core information from the data; and finally, multi-source data fusion modeling methods (such as weighted fusion and feature layer fusion) to integrate the logical relationships between the four types of data. This results in the construction of a two-type industry evaluation index system covering three dimensions: resource conservation, environmental friendliness, and economic benefits (the two-type industries include resource-saving and environmentally friendly industries). The system requires an accuracy rate of ≥97% after data cleaning and an index system coverage rate of ≥95%, providing a standardized and high-quality data benchmark for subsequent intelligent industrial diagnosis.

[0055] Step S200: Use machine learning algorithms to train an intelligent diagnostic model for the two types of industry evaluation index system. Analyze data patterns from the dimensions of resource utilization efficiency, environmental impact, and economic output benefits to determine the industry weakness identification results. The industry weakness refers to the weak link in industrial development.

[0056] Based on the two-type industry evaluation index system constructed in step S100, machine learning algorithms (such as random forest, support vector machine, logistic regression, etc.) adapted to the characteristics of industry data are selected. Through data partitioning (training set, validation set), model training, and parameter tuning, an intelligent diagnostic model for diagnosing industry health is constructed. The intelligent diagnostic model focuses on three core dimensions to conduct data pattern analysis: resource utilization efficiency (such as the compliance status and industry gap of indicators such as energy consumption per unit of output and raw material utilization rate), environmental impact (such as the compliance and optimization space of indicators such as pollutant emission compliance rate and carbon emission intensity), and economic output benefit (such as the rationality and growth potential of indicators such as input-output ratio and profit margin). Through indicator deviation calculation, data trend analysis, and industry benchmark comparison, the model accurately identifies the weak links in industrial development (i.e., industry shortcomings, such as excessive energy consumption, emission violations, and low efficiency), forming clear industry shortcomings identification results. The model diagnostic accuracy is required to be ≥94%, and the shortcomings location accuracy is required to be ≥93%, providing a clear problem orientation for the subsequent generation of customized optimization solutions.

[0057] Step S300: Process the industry weakness identification results through deep learning algorithms, combine policy guidance data and industry benchmark data to generate customized optimization solutions, including technology upgrade paths and resource allocation adjustment strategies.

[0058] Using the industry weakness identification results determined in step S200 (such as data on weak links like inefficient resource utilization, excessive environmental emissions, and insufficient economic output) as the core input, a deep learning algorithm adapted to the characteristics of the industry data (such as neural networks, reinforcement learning frameworks, and graph deep learning models) is employed to deeply decompose the weakness data, including quantification of causes, analysis of influencing factors, and assessment of improvement potential. Simultaneously, policy-oriented data (such as macro-level requirements like policies supporting resource-saving and environmentally friendly industries, energy consumption and environmental protection standards, and industrial development plans) and industry benchmark data (such as practical experience from leading enterprises' resource efficiency indicators, advanced technology application cases, and optimal resource allocation models) are standardized and integrated into the model. This is achieved through the algorithm's built-in key... The matching and logic fusion module achieves a precise three-dimensional adaptation of "shortcomings, policy requirements, and benchmark experiences," ultimately generating targeted and customized optimization solutions. The technology upgrade path clearly defines the key technologies the industry needs to introduce (such as energy-saving and emission-reducing technologies, clean production technologies, and digital transformation technologies), the priority of technology implementation, and the phased implementation steps. The resource allocation adjustment strategy covers the redistribution of core resources such as human resources, capital, energy, and raw materials (such as allocating capital to high-efficiency production capacity, optimizing energy consumption structure, and integrating idle production resources). The solution must have a ≥92% compatibility with industry shortcomings and a ≥95% policy compliance rate, providing precise action guidance for the industry's transformation towards resource conservation and environmental friendliness.

[0059] Step S400: Determine whether the resource configuration adjustment part in the customized optimization scheme matches the real-time data feedback. If they do not match, adjust the fused data input by comparing the scheme parameters with the feedback threshold to obtain the updated optimization scheme. The feedback threshold is set based on historical data.

[0060] Taking the customized optimization plan generated in step S300 as the object, the focus is on the resource allocation adjustment strategies (such as fund allocation, energy quotas, raw material scheduling, and manpower deployment), and real-time data feedback during the trial operation or execution of the plan (such as actual resource consumption rate, configuration execution efficiency, and output matching degree). The matching degree between the resource allocation adjustment and the real-time feedback is judged by the data comparison algorithm (such as whether there is a surplus / shortage of resources, or a disconnect between the configuration direction and actual demand). If it is determined to be mismatched, statistical analysis (such as quantiles) is performed based on historical operating data (such as past resource allocation effect data and industry average configuration level data). The algorithm uses regression analysis to set feedback thresholds (such as reasonable resource consumption range, configuration efficiency threshold, output benchmark, etc.) to quantitatively compare the resource configuration parameters in the customized optimization plan with the feedback thresholds, and locate the core dimensions of deviation (such as excessive capital investment or unreasonable energy configuration structure). Based on the deviation analysis results, the algorithm adjusts the previous fusion data input (such as supplementing real-time resource supply and demand data and correcting the weight ratio of policy / benchmark data), and recalculates the updated optimization plan through algorithm. The matching degree judgment accuracy is required to be ≥93%, and the resource configuration adaptation rate after the plan is adjusted should be improved by ≥18%, so as to ensure that the plan fits the real-time operation status of the industry.

[0061] Step S500: For the updated optimization scheme, obtain real-time data feedback for dynamic iterative processing, use visualization monitoring function to track the changes in the scheme deduction, determine whether the iteration converges to the two-type development requirements, and obtain the final closed-loop optimization result.

[0062] Based on the optimized scheme updated in step S400, real-time data feedback (such as real-time resource consumption data, dynamic environmental emission data, and real-time economic output data) is continuously collected during the scheme execution process. Multiple rounds of dynamic iteration are performed using algorithms, with each iteration fine-tuning scheme parameters (such as resource allocation ratios and technology implementation pace) and optimizing the execution path based on real-time feedback. Simultaneously, a visual monitoring function is enabled, using data dashboards, trend curves, and indicator dashboards to intuitively track changes in key indicators during the scheme's development process (such as energy consumption decline trends, emission compliance progress, and benefit growth). A logical verification algorithm is used to determine whether the iteration process has converged: that is, whether the core indicators stably meet the requirements of resource conservation and environmental protection (e.g., energy consumption per unit of output value is below the standard threshold; environmental friendliness and compliance, such as pollutant emissions meeting limits; and stable economic output, such as profit margins remaining within a reasonable range). When the iteration meets the criteria of "stable indicator convergence and continuous compliance with resource conservation and environmental protection standards," the iteration stops and the final closed-loop optimization result is output. The iteration convergence efficiency is required to be ≥90%, and the final result's compliance with the requirements of resource conservation and environmental protection is required to be ≥96%, ensuring the scheme's implementation effect meets the standards.

[0063] Step S600: Based on the final closed-loop optimization results, simulate industrial application scenarios, evaluate the effectiveness of the optimization scheme, and update the evaluation index system for the two types of industries to support subsequent iterations.

[0064] Based on the final closed-loop optimization results output in step S500, industrial simulation tools (such as process simulation systems and numerical simulation platforms) are used to build realistic industrial application scenarios, recreating key scenario elements such as production and operation processes, resource supply constraints, and changes in the market environment. The effectiveness of the optimization scheme is evaluated from three core dimensions: resource conservation (such as the rate of reduction in energy consumption per unit of output and the increase in resource recycling rate), environmental friendliness (such as the proportion of pollutant emission reduction and the level of carbon emission intensity reduction), and economic output (such as the degree of improvement in input-output ratio and the rate of profit margin growth), forming a quantitative effect evaluation report. Combining the evaluation results with new trends in industrial development (such as policy updates, technological iterations, and changes in market demand), the original two-oriented industrial evaluation index system is dynamically updated, including adjusting index weights, supplementing new scenario core indicators, and optimizing index calculation methods. The requirements are that the scenario simulation fit is ≥94%, the effect evaluation accuracy is ≥95%, and the coverage of the updated index system is ≥96%, providing a more suitable and scientific benchmark support for subsequent intelligent diagnosis and optimization of the industry.

[0065] Furthermore, the two-type industrial intelligent diagnosis and optimization method based on multi-dimensional data fusion proposed in this embodiment includes step S100 as follows:

[0066] Step S110: Obtain raw records from the industrial economic database and environmental emission monitoring points, perform preliminary processing on the raw records, and use data cleaning tools to remove noise and missing parts to obtain the sorted initial dataset.

[0067] The data cleaning process is defined by the following formula, which retains data points that are within a reasonable range and are not missing:

[0068] (1)

[0069] In formula (1), This represents the cleaned dataset obtained after data cleaning. This represents the set of raw records obtained from industrial economic databases and environmental emission monitoring stations. Indicates the first in the original record Data points, This represents the mean of the data. The standard deviation of the data This represents the threshold coefficient for noise filtering. This indicates a missing value.

[0070] When processing raw records from industrial economic databases and environmental emission monitoring stations, the data is first preliminarily processed using data cleaning tools. For example, in the energy consumption data of resource-saving industries in a certain region obtained from the database, some records may be missing or abnormal due to equipment malfunctions, such as negative or empty values ​​in a factory's monthly energy consumption data. In such cases, data cleaning tools are used to fill in missing values ​​or remove outliers using mean imputation, ensuring the completeness and accuracy of the data. This effectively improves the reliability of subsequent analysis and avoids biases caused by data noise.

[0071] Step S120: Based on the initial dataset, feature selection is performed on the key indicators of resource-saving industries and environmentally friendly industries. Statistical tools are used to analyze the distribution characteristics of the key indicators of resource-saving industries and environmentally friendly industries to determine the core feature combination.

[0072] The following formula is used to calculate the comprehensive characteristic score of resource-saving industries:

[0073] (2)

[0074] In formula (2), Indicates the first The comprehensive evaluation score of a resource-saving industry. This represents the total number of indicators involved in the evaluation. Indicates the first The weighting coefficients of each indicator Indicates the first The industry in Standardized values ​​for each indicator.

[0075] The following formula is used to analyze the dispersion of key indicators for environmentally friendly industries:

[0076] (3)

[0077] In formula (3), Indicates the first Standard deviation of an environmentally friendly industry indicator Indicates the number of samples. Indicates the first The sample at the th Observed values ​​for each environmentally friendly indicator Indicates the first The average of the environmentally friendly indicators.

[0078] In the feature selection phase, key indicators for resource-saving and environmentally friendly industries, such as energy intensity, wastewater discharge, and recycling rate, were analyzed using principal component analysis to identify the core features with the greatest impact on industry evaluation. For example, assuming that the energy intensity data for resource-saving industries in a certain region ranges from 0.5 to 2.0 tons of standard coal per 10,000 yuan of output value, while the wastewater discharge for environmentally friendly industries is concentrated between 0.1 and 0.8 tons per 10,000 yuan of output value, statistical analysis reveals that energy intensity and wastewater discharge are the core indicators affecting the evaluation of these two types of industries. This selection method helps to focus on key issues and improve the relevance of the evaluation system.

[0079] Step S130: If some indicators in the core feature combination exceed the preset threshold, the initial dataset is modeled using a data integration tool to obtain the evaluation index system for the two types of industries.

[0080] The comprehensive evaluation score of the two types of industries can be calculated using the root mean square method according to the following formula, which can comprehensively reflect the overall level and structural characteristics of industrial development:

[0081] (4)

[0082] In formula (4), This represents the comprehensive evaluation value of the evaluation index system for the two-oriented industries. This indicates the total number of evaluation indicators. Indicates the first The values ​​of each primary industry indicator, Indicates the first The values ​​of each secondary industry indicator.

[0083] After determining the core feature combination, if some indicators are found to exceed preset thresholds, such as an industry's energy intensity exceeding the threshold of 1.5 tons of standard coal per 10,000 yuan of output value, further multi-source fusion modeling using data integration tools is required. This involves combining industrial economic data with environmental monitoring data to construct a comprehensive evaluation model. For example, merging energy consumption data from a resource-saving industry with emission data from an environmentally friendly industry reveals that high energy consumption is often accompanied by high emissions, leading to adjustments in evaluation weights to highlight the importance of energy conservation and emission reduction. This multi-source fusion modeling can more comprehensively reflect the current state of the industry and provide a scientific basis for policy formulation.

[0084] When constructing the evaluation index system for resource-saving and environmentally friendly industries, a multi-dimensional index system including resource utilization efficiency, environmental impact index, and economic benefits is designed based on the integrated dataset. Suppose that after evaluation using this system, a region scores only 60 points in resource utilization efficiency but a high score of 80 points in the environmental impact index, indicating that the region performs well in environmental protection, but there is still room for improvement in resource conservation. This systematic evaluation helps to accurately identify the shortcomings in industrial development, provides data support for subsequent optimization, and enhances the scientific nature and sustainability of decision-making.

[0085] Furthermore, the two-type industrial intelligent diagnosis and optimization method based on multi-dimensional data fusion proposed in this embodiment includes step S200 as follows:

[0086] Step S210: Use machine learning algorithms to train an intelligent diagnostic model for the evaluation index system of the two types of industries.

[0087] The loss function of the intelligent diagnostic model is derived using the following formula:

[0088] (5)

[0089] In formula (5), This represents the loss function of the intelligent diagnostic model. This represents the total number of training samples. Indicates the first The true category label of each industry sample This indicates that the intelligent diagnostic model is effective for the first... The predicted probability of a sample. Indicates the first The feature vector of the evaluation index for each sample The first intelligent diagnostic model represents the... One parameter, Represents the regularization coefficient. This represents the total number of parameters in the intelligent diagnostic model.

[0090] When constructing an intelligent diagnostic model for the evaluation index system of resource-saving and environmentally friendly industries, the selection of machine learning algorithms is crucial. Considering the characteristics of resource-saving and environmentally friendly industries, algorithms suitable for multi-dimensional data analysis are adopted. A common approach is to utilize decision trees or random forest algorithms, as these methods can handle complex relationships between multiple variables and prioritize indicators such as resource utilization efficiency, environmental impact, and economic output. For example, assuming the intelligent diagnostic model trains data on resource-saving industries in a certain region and finds that resource utilization efficiency has a high weight, this suggests that this indicator may be a key factor influencing industry evaluation. Through this algorithmic analysis, potential patterns in the data can be intuitively identified, providing a basis for subsequent weakness identification.

[0091] Step S220: Based on the trained intelligent diagnostic model, analyze the data patterns from the dimensions of resource utilization efficiency, environmental impact, and economic output benefits to determine the identification results of industrial weaknesses.

[0092] The results of industry weakness identification are obtained using the following formula:

[0093] (6)

[0094] In formula (6), Indicates the first The weakness identification index of each industry Indicates the first The actual output of each industry Indicates the first Resource input for each industry Indicates the first Environmental impact assessment values ​​for each industry Indicates the first The economic benefits of each industry Indicates the first The benchmark economic benefits of an industry , , These represent the weighting coefficients for resource utilization efficiency, environmental impact, and economic output benefits, respectively.

[0095] In training the intelligent diagnostic model, the dataset is divided into multiple dimensions for feature input. Specifically, resource utilization efficiency is refined into energy consumption per unit of output and raw material recycling rate; environmental impact includes exhaust emissions and wastewater treatment rate; and economic output benefits focus on indicators such as output per unit of energy consumption. For example, if a resource-saving industry's energy consumption per unit of output is 1.8 tons of standard coal, significantly higher than the industry average, the intelligent diagnostic model will mark this indicator as a weakness after training. Through the comprehensive input of multi-dimensional data, the intelligent diagnostic model can capture industry characteristics from different perspectives, ensuring the comprehensiveness of the analysis.

[0096] This study focuses on data pattern analysis and industry weakness identification, extracting patterns from historical data and providing in-depth interpretation of the model's output. For example, consider an environmentally friendly industry with a wastewater treatment rate of 85%, higher than the industry standard, but with low economic output, only 0.6 million yuan per unit of energy consumption. Model analysis suggests that while the industry invests heavily in environmental protection, its resource allocation efficiency is insufficient, limiting its economic benefits. This multi-faceted analysis helps to comprehensively examine the industry's current situation from the perspectives of resources, environment, and economy, thus providing targeted suggestions for optimization.

[0097] After identifying industry weaknesses, the analysis is further refined. For industries with low resource utilization efficiency, the intelligent diagnostic model suggests optimizing production processes or introducing energy-saving technologies. For industries with high environmental impact, it recommends strengthening emission control measures. For example, if the recycling rate of resource-saving industries in a certain region is only 40%, the intelligent diagnostic model identifies this as a primary area for improvement. Combined with economic output data, it recommends improving efficiency through technological upgrades. This model-based diagnostic approach provides clear guidance for industry optimization and helps improve the rationality of resource allocation.

[0098] Furthermore, the two-type industrial intelligent diagnosis and optimization method based on multi-dimensional data fusion proposed in this embodiment includes step S300 as follows:

[0099] Step S310: Based on the industry weakness identification results, use deep learning algorithms to deeply mine the weakness data and construct a deep learning algorithm model.

[0100] The following formula is used to achieve deep feature extraction and pattern discovery of bottleneck data:

[0101] (7)

[0102] In formula (7), This represents the output of the deep mining. This indicates the number of layers in a neural network. Indicates the first Layer activation weights This indicates a modified linear unit activation function. Indicates the first The weight matrix of the layer, This indicates the hidden state of the previous layer. Indicates the first The bias vector of the layer.

[0103] When conducting in-depth analysis of the identified industry weaknesses, a specialized analytical model is constructed using deep learning algorithms, focusing on the weakness data of resource-saving and environmentally friendly industries. This analytical model can extract hidden features from massive amounts of data, particularly in dimensions such as resource utilization efficiency, environmental impact, and economic output benefits, uncovering the root causes of potential problems.

[0104] The core of deep learning algorithms lies in their multi-layered neural network structure, which can simulate complex nonlinear relationships, thereby enabling deeper analysis of bottleneck data. For example, for industries with low resource utilization efficiency, deep learning algorithm models learn key variables affecting efficiency through historical data, such as aging production equipment or unreasonable processes, and generate optimization directions accordingly. In constructing deep learning algorithm models, policy-guided data and industry benchmark data are used as auxiliary inputs, forming a multi-dimensional analysis framework with bottleneck data. Policy-guided data includes energy conservation and emission reduction targets or industry support policies, while industry benchmark data provides reference indicators for outstanding enterprises. Suppose that the energy consumption per unit output of a resource-saving industry is 2.1 tons of standard coal, far exceeding the industry benchmark of 1.5 tons of standard coal, while the policy requires it to be reduced to below 1.7 tons of standard coal within the next three years. The deep learning algorithm model learns from this data, identifies the gaps in energy consumption control within the industry, and combines this with the energy-saving technology application experience of benchmark enterprises to generate targeted improvement suggestions. This three-dimensional matching approach ensures that the optimization plan not only meets policy requirements but also has industry feasibility.

[0105] Step S320: Simultaneously, by combining policy guidance data and industry benchmark data, a three-dimensional matching and logical fusion of the shortcomings, policy requirements, and benchmark experience is achieved through a deep learning algorithm model to generate a customized optimization solution.

[0106] Define a formula for calculating the 3D matching degree, which is used to calculate similarity matching in a multidimensional feature space using a Gaussian kernel function:

[0107] (8)

[0108] In formula (8), Indicates the first The shortcomings and the first The degree of matching of benchmark experience Indicates the total number of feature dimensions. Indicates the first The shortcomings are in the first Feature values ​​in each dimension Indicates the first The benchmark experience in the first Feature values ​​in each dimension Indicates the first Standard deviation of each dimension Indicates the first Feasibility weight of each benchmark experience.

[0109] Define a formula for generating customized optimization solutions. This formula is used to generate the optimal customized solution by comprehensively considering the effectiveness of the measures and the implementation cost.

[0110] (9)

[0111] In formula (9), Indicates that for the first A comprehensive score for the optimization solutions to each problem. This represents the total number of possible optimization measures. Indicates the first The basic effectiveness coefficient of each optimization measure Indicates the first The first measure is for the first The relevance score of each question This represents a cost sensitivity parameter. Indicates the first The measures target the first The implementation cost of this problem This represents the baseline cost.

[0112] This paper analyzes the three-dimensional matching and logical integration of shortcomings, policy requirements, and benchmarking experience from two aspects: technological upgrading paths and resource allocation adjustment strategies. Regarding technological upgrading paths, the deep learning algorithm model suggests introducing advanced energy-saving equipment or optimizing production processes. Taking an environmentally friendly industry as an example, its emissions exceed policy standards. However, analysis using the deep learning algorithm model reveals that benchmark companies in similar scenarios have adopted high-efficiency filtration technology, reducing emissions by 30%. Based on this, the technological upgrading path generated by the deep learning algorithm model is to introduce similar technologies and adjust equipment selection according to the company's actual situation. Regarding resource allocation adjustment strategies, assuming an industry's raw material recycling rate is only 35%, lower than the industry benchmark of 50%, the model might suggest reallocating resources, prioritizing investment in recycling facilities, and considering policy subsidies to reduce implementation costs. This multi-faceted matching analysis supports the rationality of the optimization plan from different perspectives.

[0113] When generating customized optimization solutions, deep learning models can also propose phased implementation suggestions based on the specific shortcomings of an industry. For industries with low resource utilization efficiency, the initial focus can be on optimizing low-cost processes, such as improving operational procedures to reduce energy consumption, followed by investment in equipment upgrades. This progressive logic from core solutions to extended solutions ensures both the operability of the solutions and enhances their applicability through diversified implementation paths. Especially under the guidance of policies and benchmark data, the solutions can better balance short-term costs and long-term benefits, providing clear guidance for industry optimization while effectively improving the rationality and environmental friendliness of resource allocation.

[0114] Furthermore, the two-type industrial intelligent diagnosis and optimization method based on multi-dimensional data fusion proposed in this embodiment includes step S400 as follows:

[0115] Step S410: Compare the resource configuration adjustment part in the customized optimization plan with the real-time data feedback, and use the data comparison tool to determine the degree of matching to obtain the preliminary matching result.

[0116] The following formula is used to evaluate the overall matching degree by calculating the relative deviation between the preset value and the real-time value of each configuration item:

[0117] (10)

[0118] In formula (10), This score represents the degree of matching between resource allocation and real-time data. Indicates the total number of resource configuration items. Indicates the first The weighting coefficient of each resource allocation item. Indicates the first The default values ​​for each resource configuration item. Indicates the first Real-time feedback data values ​​corresponding to each resource configuration item.

[0119] When comparing the resource allocation adjustments in a customized optimization plan with real-time data feedback, a data comparison tool is used for detailed analysis. The core of this tool is to compare the resource allocation data in the plan with the actual real-time data to determine if they match. For example, in a resource-saving industry, the optimization plan suggests adjusting the energy input ratio in a certain production stage from 30% to 25%, while real-time data shows the actual ratio remains at 28%. The comparison tool can quickly identify this inconsistency in the initial matching result. This method can promptly detect deviations in plan implementation, providing a basis for subsequent adjustments.

[0120] Step S420: If the preliminary matching results show inconsistency, the key parameter values ​​in the customized optimization scheme are obtained through the parameter extraction tool and compared with the feedback threshold set based on historical data benchmark to determine the parameter deviation range.

[0121] The following formula is used to determine a reasonable feedback threshold boundary based on the statistical characteristics of historical data:

[0122] (11)

[0123] In formula (11), Indicates the range of feedback thresholds. This represents the mean of historical data. The standard deviation of historical data This represents the confidence coefficient.

[0124] In cases where initial matching results show inconsistencies, key parameter values ​​are obtained using parameter extraction tools and compared with feedback thresholds set based on historical data benchmarks. The parameter extraction tool extracts core indicators from the proposed solution, such as energy input ratio or raw material utilization rate, and compares them with historical benchmark values ​​to determine the range of deviation. For example, assuming the historical benchmark threshold requires an energy input ratio deviation of no more than 2%, while the actual deviation is 3%, the range of deviation can be clearly defined. This method helps to accurately pinpoint the root cause of the problem and provides data support for adjustments.

[0125] Step S430: For the parameter deviation range, use a data fusion tool to adjust the content of the fusion data input, generate the adjusted input dataset, and obtain the updated data combination.

[0126] The following formula is used to adjust and correct the original data based on the parameter deviation range:

[0127] (12)

[0128] In formula (12), Indicates the first The fused data value after adjustment of each data point Indicates the first The original input data values ​​for each data point This represents the parameter deviation adjustment factor. Indicates the first The parameter deviation value of each data point Indicates the reference deviation baseline value. The standard deviation represents the range of deviations.

[0129] The following formula is used to calculate the final data combination obtained after multiple adjustments:

[0130] (13)

[0131] In formula (13), This indicates the result of the updated data combination. Represents the combination of basic data. This indicates an update to the intensity adjustment factor. This indicates the total number of adjustment operations. Indicates the first The response value of the next adjustment. Indicates the first The processing parameters were adjusted this time. Indicates the first The mean parameter adjusted each time. This represents the regularization constant.

[0132] When adjusting for parameter deviations, data fusion tools are used to optimize the input data, generating an adjusted input dataset. These tools integrate multi-source data, such as real-time feedback and historical trend data, to create a more comprehensive data combination. For example, if the original dataset shows low resource utilization in a certain production stage, integrating real-time monitoring data and industry reference data generates an updated data combination that reflects a more realistic situation. This approach improves the accuracy of data input, laying the foundation for subsequent adjustments. For instance, when applying the updated data combination to the adjustment logic rules of a customized optimization plan, the data integration tool generates an updated optimization plan. The data integration tool combines the new dataset with established logic rules to recalculate resource allocation strategies. If the adjusted dataset shows that resource utilization in a certain stage can be increased to 45%, the tool will reallocate resource proportions according to the rules, generating a plan that better meets actual needs. This method ensures the dynamic adaptability of the optimization plan, enabling it to better respond to real-time changes.

[0133] Step S440: Apply the updated data combination to the adjustment logic rules of the customized optimization scheme through the data integration tool to generate the updated optimization scheme.

[0134] The following formula describes the process of generating a new optimization scheme by combining the data integration results with adjustment logic rules through time integration:

[0135] (14)

[0136] In formula (14), This indicates the updated optimization plan. Indicates time The results of data integration This indicates the function for adjusting logical rules. Indicates the applied weighting coefficient. This indicates the integration timeframe.

[0137] Examining the application of data integration tools from multiple perspectives reveals their flexibility in various scenarios. If real-time data feedback indicates a sudden increase in energy consumption of a company's production equipment, the data integration tool can combine historical data with adjusted datasets to re-plan equipment maintenance and resource allocation strategies, ensuring energy consumption remains within reasonable limits. On the other hand, if the recycling rate of raw materials falls short of expectations, the data integration tool can analyze new data combinations to propose specific suggestions for optimizing the recycling process. These multi-directional adjustment strategies support each other, resulting in a comprehensive improvement effect and enhancing the practicality and enforceability of the solutions.

[0138] Furthermore, the two-type industrial intelligent diagnosis and optimization method based on multi-dimensional data fusion proposed in this embodiment includes step S500 as follows:

[0139] Step S510: For the updated optimization scheme, obtain the real-time data feedback content, use data acquisition tools to obtain the latest running data from multiple sources, compare the matching degree of the latest running data with the environmental standards, and obtain the judgment result of the data deviation range.

[0140] The following formula is used to calculate the degree of deviation between operational data and environmental standards:

[0141] (15)

[0142] In formula (15), This indicates the total number of data collection points. Indicates the first Real-time operational data values ​​of each collection point Indicates the first The environmental standard values ​​corresponding to each collection point It represents the percentage of overall data deviation; this formula calculates the relative deviation of real-time data from each collection point from the standard value, and then calculates the average to obtain the overall deviation range.

[0143] When obtaining real-time data feedback on updated optimization schemes, data acquisition tools collect the latest operational data from multiple sources. The core of these tools lies in covering all key nodes in the production process, ensuring the comprehensiveness and timeliness of the data. For example, in a resource-saving industry, the data acquisition tool extracts data from energy consumption monitoring points of production equipment and raw material usage records, discovering that energy consumption in a certain stage is 5% higher than expected, while environmental standards require deviations to be controlled within 3%. By comparing this data, it can be clearly determined that the data deviation exceeds the preset threshold, providing a basis for subsequent adjustments.

[0144] Step S520: If the data deviation range exceeds the preset threshold, the dynamically adjusted content is re-integrated using a data fusion tool, key variables are extracted from the feedback loop, and the adjusted data combination content is determined.

[0145] The key variables extracted from the feedback loop are obtained using the following formula:

[0146] (16)

[0147] In formula (16), This indicates the index of the variable in the feedback loop. Indicates the first Importance coefficients of each variable Indicates the first The response strength of each variable in the feedback loop Indicates the first The delay time of each variable, This represents the key variables extracted from the feedback loop.

[0148] When data deviation exceeds a threshold, the data fusion tool's role is to reintegrate dynamically adjusted data, extracting key variables from the feedback loop. By combining real-time data with historical trends, the tool creates a more realistic data set. For example, assuming a 5% deviation in energy consumption, the tool will extract key variables affecting energy consumption, such as equipment runtime and production load, and combine this with historical data analysis to determine a reasonable adjustment range, ultimately generating the adjusted data set. This approach helps to accurately pinpoint the root cause of the problem.

[0149] Step S530: For the adjusted data combination content, use a visualization monitoring tool to record the change trajectory of the scheme in real time, and obtain the trend information of iterative convergence during change tracking.

[0150] The convergence trend assessment value is obtained using the following formula:

[0151] (17)

[0152] In formula (17), Indicates the first The convergence trend assessment value of the wheel, Indicates the current round. Indicates the first The results of the wheel's deduction, Indicates the convergence value of the objective. Indicates the standard deviation of the result. This indicates the convergence speed adjustment parameter.

[0153] When recording the adjusted data set in real time, the visualization monitoring tool can intuitively present the trajectory of the solution's evolution and obtain information on the trend of iterative convergence. The tool displays data changes through charts or dynamic curves, helping analysts quickly assess the effectiveness of the adjustments. For example, if the adjusted data set reduces energy consumption deviation from 5% to 3.5%, the visualization monitoring tool will record this trajectory and show the trend of gradual deviation convergence. This intuitive presentation facilitates the timely detection of potential problems.

[0154] Step S540: Compare the iterative convergence trend information with the preset resource conservation and environmental friendliness standard judgment rules using the closed-loop optimization tool to obtain the final closed-loop optimization result.

[0155] The core mechanism of parameter iterative update in closed-loop optimization tools is described by the following formula:

[0156] (18)

[0157] In formula (18), Indicates the first The optimized parameter vector for the next iteration. Indicates the first The current parameter vector for the next iteration. This represents the iteration step size coefficient. This represents the gradient of the objective function at the current parameters. This represents the convergence adjustment factor. This represents the preset target value for resource and environmental standards. Indicates the first The current standard evaluation value for the next iteration.

[0158] The following formula is used to determine whether the optimization results meet the environmental friendliness standard:

[0159] (19)

[0160] In formula (19), Indicators representing environmental friendliness assessment indicators This represents the total number of environmental benefit factors. Indicates the first The weighting coefficients of each environmental benefit factor. Indicates the first The values ​​of each environmental benefit factor, This represents the total number of resource consumption factors. Indicates the first The weighting coefficients of each resource consumption factor. Indicates the first The value of each resource consumption factor. The threshold representing the environmentally friendly standard.

[0161] The following formula is used to evaluate the convergence state of the iterative process and determine whether the final optimization result has been reached:

[0162] (20)

[0163] In formula (20), Indicators representing the convergence trend of iterations. This represents the total number of dimensions of the optimization parameters. Indicates the first In the nth iteration The values ​​of the parameters, Indicates the first In the nth iteration The values ​​of the parameters, This represents the threshold for the convergence stopping criterion.

[0164] The closed-loop optimization tool compares the iterative convergence trend information with preset resource-saving and environmentally friendly standards to arrive at the final optimization result. The core of the closed-loop optimization tool lies in ensuring that the solution meets the predetermined goals through continuous feedback and adjustment. For example, if the trend information shows that the energy consumption deviation is close to the standard value of 3%, the tool will further fine-tune the resource allocation strategy according to preset rules, such as reducing the equipment operating frequency, ultimately controlling the deviation to 2.8%. This closed-loop mechanism can dynamically adapt to changes.

[0165] If real-time feedback indicates that waste emissions from a certain production stage slightly exceed the standard, the closed-loop optimization tool, combining trend information and standard rules, adjusts resource input for waste treatment in the production process to ensure emissions meet standards. On the other hand, if raw material utilization is low, the closed-loop optimization tool analyzes iterative data and proposes suggestions for optimizing material ratios. These multi-directional adjustment strategies support each other, resulting in a comprehensive improvement effect and enhancing the executability of the solution.

[0166] Furthermore, the two-type industrial intelligent diagnosis and optimization method based on multi-dimensional data fusion proposed in this embodiment includes step S600 as follows:

[0167] Step S610: Based on the scenario data in the final closed-loop optimization results, extract relevant information from the pre-established database, compare the extracted relevant information with industry standards, and determine the degree of adaptation of the scenario data.

[0168] The final scenario data results for closed-loop optimization are obtained using the weighted least squares method according to the following formula:

[0169] (twenty one)

[0170] In formula (21), This represents the final closed-loop optimized scenario data. This represents the total number of optimization objectives. Indicates the first The weighting coefficients of each optimization objective. Representing scene data In the Function mapping values ​​in each dimension Indicates the first Target values ​​for each dimension.

[0171] Relevant information can be extracted from the database using the following formula:

[0172] (twenty two)

[0173] In formula (22), This indicates relevant information extracted from the database. Indicates query conditions or scenario characteristics. This represents the total number of related data entries in the database. Indicates the first The relevance weight of each data item Indicates the query conditions and the first A similarity function for each data entry. Represents the first in the database The content of each data entry.

[0174] The overall fit is assessed by calculating the average deviation of each parameter using the following formula:

[0175] (twenty three)

[0176] In formula (23), This score represents the degree of compatibility between the scenario data and industry standards. This indicates the total number of alignment parameters. Indicates the first Each scene data parameter value, Indicates the first Industry standard benchmark value, This function determines the validity of parameter matching; it returns 1 if the parameter is within a reasonable range and 0 otherwise.

[0177] When analyzing scenario data from the final closed-loop optimization results, specific information related to resource conservation and environmental friendliness is extracted from a pre-established database. Scenario data typically includes key indicators such as energy consumption data, waste emissions, and raw material utilization rates during the production process. Assuming the database stores historical operating data for a certain production stage over the past three months, extracting this information reveals that the current optimization result shows energy consumption of 100 units per hour, while the industry standard is 95 units per hour, indicating a slight discrepancy. This comparison helps clarify the gap between the optimization results and industry standards, providing a basis for subsequent adjustments.

[0178] Step S620: Based on the adaptability of the scene data, use data processing tools to virtually map the optimization scheme, determine whether the mapping content of the optimization scheme meets the preset threshold, and if not, make dynamic adjustments to obtain the adjusted scheme content.

[0179] The revised plan is derived using the following formula:

[0180] (twenty four)

[0181] In formula (24), This indicates the content of the dynamically adjusted plan. This indicates the content of the original optimization scheme. This indicates an adjustment to the strength coefficient. This indicates a preset target threshold. This represents the numerical value of the currently mapped content. Indicates the time decay factor. This indicates adjusting the number of iterations. It represents the base of the natural constant.

[0182] To assess the suitability of scenario data, when using data processing tools for virtual mapping, the optimization scheme is judged to meet preset thresholds by simulating the production environment. The core of virtual mapping lies in converting the optimization scheme into simulated operating parameters and testing its performance in a virtual environment. For example, if the preset threshold is an energy consumption deviation of no more than 3%, but the virtual mapping shows a deviation of 4%, dynamic adjustments are needed. Dynamic adjustments involve optimizing equipment runtime or reducing unnecessary resource investment, ultimately reducing the deviation to 2.5%, meeting expectations. This method can identify potential problems in advance without affecting actual production.

[0183] Step S630: For the adjusted plan content, obtain the data feedback required for effect evaluation, extract performance data from the virtual operating environment, compare it with environmental factors, and determine the evaluation result.

[0184] The following formula is used to comprehensively evaluate the effectiveness of the plan by calculating the weighted average improvement of each indicator relative to the benchmark value:

[0185] (25)

[0186] In formula (25), This indicates the results of the effectiveness evaluation. This indicates the total number of evaluation indicators. Indicates the first The weighting coefficients of each indicator Indicates the first [number] after the adjustment plan The actual performance value of each indicator Indicates the first The benchmark performance value of each indicator.

[0187] The following formula is used to weight and fuse multi-source data to obtain complete performance data:

[0188] (26)

[0189] In formula (26), This represents the overall performance data extracted from the virtual operating environment. This represents the performance data output by the virtual simulation system. This represents historical operation log data. This represents key performance indicator data. , , These represent the fusion weight coefficients for the three types of data sources.

[0190] When obtaining feedback on the effectiveness of the adjusted solution, performance data is extracted from the virtual operating environment and comprehensively compared with environmental factors. Environmental factors include external conditions such as temperature and humidity in the production workshop, which affect energy consumption and emissions. For example, if the virtual operating data shows an adjusted energy consumption of 98 units per hour, but this may rise to 100 units per hour in a high-temperature environment, the comparison can determine that the evaluation result indicates a need for further optimization of equipment heat dissipation conditions. This evaluation method comprehensively considers external variables, ensuring the applicability of the solution.

[0191] Step S640: Based on the evaluation results, update the evaluation index system for resource-saving and environmentally friendly industries using integrated tools.

[0192] The integration and updating coefficient of the evaluation index system for resource-saving and environmentally friendly industries is obtained through the following formula:

[0193] (27)

[0194] In formula (27), This represents the integration and update coefficient of the evaluation index system for the two types of industries. Indicates the number of industry types. Indicates the first The original evaluation value of each industry type Indicates the first The adjusted evaluation value of each industry type Indicates the first Integration parameters for various industry types Indicates the first The effectiveness coefficient of integration tools for various industry types Indicates the first The tool impact index for each industry type.

[0195] When updating the evaluation index system for resource-saving and environmentally friendly industries based on assessment results, the integration tool combines new data feedback with existing indicators to form a more realistic evaluation standard. For example, if the original indicator required 95 units of energy consumption per hour, but the assessment results show that this standard is difficult to achieve under high-temperature environments, the integration tool might adjust the indicator to 97 units per hour, while also adding consideration for environmental adaptability. This updating method makes the evaluation system more flexible, adaptable to the actual needs under different production conditions, and enhances the guiding significance of the indicators.

[0196] Please see Figure 2This embodiment provides a two-type industry intelligent diagnosis and optimization system based on multi-dimensional data fusion, used to execute the above-mentioned two-type industry intelligent diagnosis and optimization method based on multi-dimensional data fusion. It includes a two-type industry evaluation index system acquisition module 10, an industry weakness identification result determination module 20, a customized optimization scheme generation module 30, an optimization scheme update module 40, a final closed-loop optimization result acquisition module 50, and a two-type industry evaluation index system update module 60. The two-type industry evaluation index system acquisition module 10 acquires industrial economic data, resource consumption data, environmental emission data, and policy standard data. Noise and outliers are removed through data cleaning, followed by feature extraction and data fusion modeling to obtain the two-type industry evaluation index system, where the two types of industries include resource-saving industries and environmentally friendly industries. The industry weakness identification result determination module 20 uses machine learning algorithms to train an intelligent diagnostic model for the two-type industry evaluation index system, analyzing data patterns from the dimensions of resource utilization efficiency, environmental impact, and economic output benefits to determine the industry weakness identification result. The results are as follows: The industrial weakness refers to the weak links in industrial development; the customized optimization scheme generation module 30 processes the industrial weakness identification results through deep learning algorithms, combines policy guidance data and industry benchmark data to generate customized optimization schemes, including technology upgrade paths and resource allocation adjustment strategies; the optimization scheme update module 40 determines whether the resource allocation adjustment part of the customized optimization scheme matches the real-time data feedback. If they do not match, the integrated data input is adjusted by comparing the scheme parameters with the feedback threshold to obtain an updated optimization scheme, where the feedback threshold is set based on historical data; the final closed-loop optimization result acquisition module 50 acquires real-time data feedback for the updated optimization scheme, performs dynamic iterative processing, uses visual monitoring functions to track the changes in scheme evolution, determines whether the iteration converges to the requirements of the two-type development, and obtains the final closed-loop optimization result; the two-type industry evaluation index system update module 60, based on the final closed-loop optimization result, simulates industrial application scenarios, evaluates the effectiveness of the optimization scheme, and updates the two-type industry evaluation index system to support subsequent iterations.

[0197] This embodiment discloses a method and system for intelligent diagnosis and optimization of two-type industries based on multi-dimensional data fusion. Compared with existing technologies, it first acquires data on industrial economics, resource consumption, environmental emissions, and policy standards. After cleaning and feature extraction, the data is fused and modeled to establish an evaluation index system for two-type industries. Then, a machine learning algorithm is used to train an intelligent diagnostic model, analyzing data patterns from the dimensions of resource utilization efficiency, environmental impact, and economic output benefits to determine the identification results of industrial weaknesses. Next, a deep learning algorithm is used to process the identification results, combining policy and benchmark data to generate customized optimization schemes, including technology upgrade paths and resource allocation adjustment strategies. If the customized optimization scheme does not match the real-time data feedback, the fused data input is adjusted to obtain an updated scheme, and dynamic iterative processing and visual monitoring are performed until convergence to the two-type development standards. Finally, the application scenario is simulated to evaluate the effect and update the index system. This embodiment achieves accurate diagnosis and closed-loop optimization of industrial weaknesses, improves resource conservation and environmental friendliness, and supports sustainable iterative development.

[0198] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A method for intelligent diagnosis and optimization of two types of industries based on multi-dimensional data fusion, characterized in that, Includes the following steps: S100. Acquire industrial economic data, resource consumption data, environmental emission data, and policy standard data. Remove noise and outliers through data cleaning. Then, perform feature extraction and data fusion modeling to obtain the evaluation index system for two types of industries, which includes resource-saving industries and environmentally friendly industries. S200. Using machine learning algorithms, train an intelligent diagnostic model for the two types of industry evaluation index systems, analyze data patterns from the dimensions of resource utilization efficiency, environmental impact, and economic output benefits, and determine the industry weakness identification results, wherein the industry weakness refers to the weak link in industrial development. S300. Process the industry weakness identification results through deep learning algorithms, combine policy guidance data and industry benchmark data, and generate customized optimization solutions. The customized optimization solutions include technology upgrade paths and resource allocation adjustment strategies. S400. Determine whether the resource configuration adjustment part of the customized optimization scheme matches the real-time data feedback. If they do not match, adjust the fused data input by comparing the scheme parameters with the feedback threshold to obtain the updated optimization scheme. The feedback threshold is set based on historical data. S500: For the updated optimization scheme, obtain real-time data feedback for dynamic iterative processing, use visual monitoring function to track the changes in scheme deduction, determine whether the iteration converges to the two-type development requirements, and obtain the final closed-loop optimization result. S600. Based on the final closed-loop optimization results, simulate industrial application scenarios, evaluate the effectiveness of the optimization scheme, and update the two-type industry evaluation index system to support subsequent iterations. Step S600 includes: S610. Based on the scenario data in the final closed-loop optimization results, extract relevant information from the pre-established database, compare the extracted relevant information with industry standards, and determine the degree of adaptation of the scenario data. The final scenario data results for closed-loop optimization are obtained using the weighted least squares method according to the following formula: ； in, This represents the final closed-loop optimized scenario data. This represents the total number of optimization objectives. Indicates the first The weighting coefficients of each optimization objective. Representing scene data In the Function mapping values ​​in each dimension Indicates the first Target values ​​for each dimension; Relevant information can be extracted from the database using the following formula: ； in, This indicates relevant information extracted from the database. Indicates query conditions or scenario characteristics. This represents the total number of related data entries in the database. Indicates the first The relevance weight of each data item Indicates the query conditions and the first A similarity function for each data entry. Represents the first in the database The content of each data entry; The overall fit is assessed by calculating the average deviation of each parameter using the following formula: ； in, This score represents the degree of compatibility between the scenario data and industry standards. This indicates the total number of alignment parameters. Indicates the first Each scene data parameter value, Indicates the first Industry standard benchmark value, This function determines the validity of parameter matching; it returns 1 if the parameter is within a reasonable range and 0 otherwise. S620. Based on the adaptability of the scene data, a data processing tool is used to virtually map the optimization scheme. It is determined whether the mapped content of the optimization scheme meets the preset threshold. If it does not meet the threshold, dynamic adjustment is performed to obtain the adjusted scheme content. The revised plan is derived using the following formula: ； in, This indicates the content of the dynamically adjusted plan. This indicates the content of the original optimization scheme. This indicates an adjustment to the strength coefficient. This indicates a preset target threshold. This represents the numerical value of the currently mapped content. Indicates the time decay factor. This indicates adjusting the number of iterations. Represents the base of the natural constant; S630. For the adjusted plan content, obtain the data feedback required for effect evaluation, extract performance data from the virtual operating environment, compare it with environmental factors, and determine the evaluation results. S640. Based on the assessment results, update the evaluation index system for the two types of industries using an integrated tool.

2. The method for intelligent diagnosis and optimization of two types of industries based on multi-dimensional data fusion according to claim 1, characterized in that, Step S100 includes: S110. By obtaining raw records from the industrial economic database and environmental emission monitoring points, the raw records are preliminarily processed, and data cleaning tools are used to remove noise and missing parts to obtain the sorted initial dataset. S120. Based on the initial dataset, feature selection is performed on key indicators of resource-saving industries and environmentally friendly industries. Statistical tools are used to analyze the distribution characteristics of key indicators of resource-saving industries and environmentally friendly industries to determine the core feature combination. S130. If some indicators in the core feature combination exceed the preset threshold, the initial dataset is modeled using a data integration tool to obtain the evaluation index system for the two types of industries.

3. The method for intelligent diagnosis and optimization of two types of industries based on multi-dimensional data fusion according to claim 2, characterized in that, Step S200 includes: S210. Use machine learning algorithms to train an intelligent diagnostic model for the evaluation index system of the two types of industries; S220. Based on the trained intelligent diagnostic model, analyze the data patterns from the dimensions of resource utilization efficiency, environmental impact, and economic output benefits to determine the identification results of industrial weaknesses.

4. The method for intelligent diagnosis and optimization of two types of industries based on multi-dimensional data fusion according to claim 3, characterized in that, Step S300 includes: S310. Based on the industry weakness identification results, a deep learning algorithm is used to deeply mine the weakness data and construct a deep learning algorithm model. S320, by combining policy guidance data and industry benchmark data, uses deep learning algorithm models to achieve three-dimensional matching and logical integration of shortcomings, policy requirements, and benchmark experience, generating customized optimization solutions.

5. The method for intelligent diagnosis and optimization of two types of industries based on multi-dimensional data fusion according to claim 4, characterized in that, Step S400 includes: S410. Compare the resource configuration adjustment part of the customized optimization plan with the real-time data feedback, and use the data comparison tool to determine the degree of matching to obtain the preliminary matching result; S420. If the preliminary matching results show inconsistency, the key parameter values ​​in the customized optimization scheme are obtained through the parameter extraction tool and compared with the feedback threshold set based on historical data benchmark to determine the parameter deviation range. S430. For the parameter deviation range, use a data fusion tool to adjust the content of the fused data input, generate an adjusted input dataset, and obtain the updated data combination; S440. The updated data combination is applied to the adjustment logic rules of the customized optimization scheme through a data integration tool to generate an updated optimization scheme.

6. The method for intelligent diagnosis and optimization of two types of industries based on multi-dimensional data fusion according to claim 5, characterized in that, Step S500 includes: S510. For the updated optimization scheme, obtain real-time data feedback, use data acquisition tools to obtain the latest operating data from multiple sources, compare the latest operating data with the environmental standards, and obtain the judgment result of the data deviation range. S520. If the data deviation range exceeds the preset threshold, the dynamically adjusted content is re-integrated using a data fusion tool, key variables are extracted from the feedback loop, and the adjusted data combination content is determined. S530. For the adjusted data combination content, a visualization monitoring tool is used to record the change trajectory of the scheme in real time, and to obtain the trend information of iterative convergence during change tracking. S540. The trend information of iterative convergence is compared with the preset criteria for resource conservation and environmental friendliness by using a closed-loop optimization tool to obtain the final closed-loop optimization result.

7. The method for intelligent diagnosis and optimization of two types of industries based on multi-dimensional data fusion according to claim 1, characterized in that, In step S630, The following formula is used to comprehensively evaluate the effectiveness of the plan by calculating the weighted average improvement of each indicator relative to the benchmark value: ； in, This indicates the results of the effectiveness evaluation. This indicates the total number of evaluation indicators. Indicates the first The weighting coefficients of each indicator Indicates the first [number] after the adjustment plan The actual performance value of each indicator Indicates the first The baseline performance value of each indicator; The following formula is used to weight and fuse multi-source data to obtain complete performance data: ； in, This represents the overall performance data extracted from the virtual operating environment. This represents the performance data output by the virtual simulation system. This represents historical operation log data. This represents key performance indicator data. , , These represent the fusion weight coefficients for the three types of data sources.

8. The method for intelligent diagnosis and optimization of two types of industries based on multi-dimensional data fusion according to claim 7, characterized in that, In step S640, the integration and update coefficient of the evaluation index system for the two types of industries is obtained through the following formula: ； in, This represents the integration and update coefficient of the evaluation index system for the two types of industries. Indicates the number of industry types. Indicates the first The original evaluation value of each industry type Indicates the first The adjusted evaluation value of each industry type Indicates the first Integration parameters for various industry types Indicates the first The effectiveness coefficient of integration tools for various industry types Indicates the first The tool impact index for each industry type.

9. A two-type industry intelligent diagnosis and optimization system based on multi-dimensional data fusion, used to execute the two-type industry intelligent diagnosis and optimization method based on multi-dimensional data fusion as described in any one of claims 1 to 8, characterized in that, include: The two-type industry evaluation index system acquisition module (10) is used to acquire industrial economic data, resource consumption data, environmental emission data and policy standard data. Noise and outliers are removed through the data cleaning process, and then feature extraction and data fusion modeling are performed to obtain the two-type industry evaluation index system, in which the two-type industries include resource-saving industries and environmentally friendly industries. The module (20) for determining the industrial weakness identification result is used to train an intelligent diagnostic model for the two types of industrial evaluation index system using machine learning algorithms, analyze data patterns from the dimensions of resource utilization efficiency, environmental impact degree and economic output benefits, and determine the industrial weakness identification result, wherein the industrial weakness refers to the weak link in industrial development; The customized optimization scheme generation module (30) is used to process the industry weakness identification results through deep learning algorithms, and combine policy guidance data and industry benchmark data to generate customized optimization schemes, including technology upgrade paths and resource allocation adjustment strategies. The optimization scheme update module (40) is used to determine whether the resource configuration adjustment part in the customized optimization scheme matches the real-time data feedback. If they do not match, the fusion data input is adjusted by comparing the scheme parameters and the feedback threshold to obtain the updated optimization scheme. The feedback threshold is set based on historical data. The final closed-loop optimization result acquisition module (50) is used to obtain real-time data feedback for the updated optimization scheme and perform dynamic iterative processing. It uses a visual monitoring function to track the changes in the scheme deduction, judge whether the iteration converges to the two-type development requirements, and obtain the final closed-loop optimization result. The two-type industry evaluation index system update module (60) is used to simulate industrial application scenarios, evaluate the effect of the optimization scheme, and update the two-type industry evaluation index system to support subsequent iterations based on the final closed-loop optimization results.

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