Cascade regulation and control operation data analysis modeling system
By employing an end-to-end adaptive architecture and nonlinear optimization algorithms, the problems of asynchronous data flow delay and model oscillation in tiered regulation were solved, achieving dynamic unification of data processing and mathematical operations, and improving the real-time performance, adaptability, and stability of the regulation model.
Patent Information
- Application Number
- CN202510951979.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-10
- Publication Date
- 2025-10-28
AI Technical Summary
Existing technologies in cascade regulation and control operation management suffer from asynchronous data flow delays, mathematical operations relying on static weights, and scheduling strategy oscillations caused by traditional algorithms. They are unable to adapt to dynamic environments, especially in cascade hydropower scheduling where real-time response to market electricity prices and meteorological changes is difficult.
An end-to-end adaptive architecture is adopted, which achieves dynamic unification of data processing and mathematical operations by running data acquisition, preprocessing, composite weight allocation, hierarchical optimization, dynamic model generation and feedback adjustment modules. Nonlinear optimization is performed using entropy theory and chaotic regularized gradient descent algorithm, and closed-loop control of the feedback adjustment module is combined to ensure that the model adapts to external changes.
It significantly improves the real-time performance, adaptability, and stability of the control model, eliminates the asynchronicity of data flow, enhances the dynamic environmental adaptability and system robustness of mathematical operations, and provides transparent decision support.
Smart Images
Figure CN120850756A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of electronic digital data processing, and more specifically, relates to a tiered control operation data analysis and modeling system. Background Art
[0002] In complex industrial energy dispatching systems, electronic digital data processing technology has become a core support for cascade control and management. Existing technologies typically rely on distributed sensor networks to collect massive amounts of multidimensional operational data and construct control models through basic mathematical operations such as cluster analysis and regression fitting. However, the current technical architecture has objective flaws: the acquisition, preprocessing, and weight allocation stages of electronic digital data processing often employ serial, independent subsystems, leading to asynchronous delays in data flow. For example, in cascade hydropower dispatching, hydrological monitoring data and unit vibration data require additional alignment calculations due to different sampling rates. This not only increases the preprocessing burden but also makes subsequent mathematical operations dependent on manually set static weights, failing to adapt to dynamic environments.
[0003] Furthermore, existing modeling techniques largely rely on linear weighting or fixed optimization algorithms (such as gradient descent), but cascade regulation scenarios require real-time responses to multi-source disturbances such as market electricity price fluctuations and sudden weather changes. Traditional mathematical operations, lacking nonlinear feedback mechanisms, often cause optimization models to converge to local optima under sudden loads, leading to oscillations in scheduling strategies.
[0004] Based on the above, we propose a cascade control operation data analysis and modeling system to specifically address the problems existing in the current technology. Summary of the Invention
[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a tiered control operation data analysis and modeling system. This system successfully bridges the gap between electronic digital data processing and mathematical operations. Through an end-to-end adaptive architecture, it achieves dynamic unification of data processing, operation, and feedback from a mechanistic perspective, significantly improving the real-time performance, adaptability, transparency, and stability of the control model in complex industrial scenarios.
[0006] To achieve the above objectives, the present invention provides the following technical solution:
[0007] A cascade control operation data analysis and modeling system, characterized in that it includes:
[0008] Run the data acquisition module to collect multi-dimensional operational data in real time;
[0009] The data preprocessing module is used to remove noise and standardize the collected data.
[0010] The composite weight allocation module is used to calculate the weight values of each dimension of the data;
[0011] The tiered optimization engine module is used to execute multi-level control strategies and perform nonlinear optimization calculations;
[0012] The dynamic model generation module is used to integrate weighted optimization results to build a predictive model;
[0013] The feedback adjustment module is used to receive feedback data and dynamically adjust weights and optimize parameters;
[0014] The analysis results output module is used to generate and output model analysis reports;
[0015] The above modules work through sequential and reverse flow interaction: the running data acquisition module transmits data to the data preprocessing module for cleaning, and the preprocessing module outputs standardized data to the composite weight allocation module to calculate weight values;
[0016] The composite weight allocation module inputs the weighted data into the hierarchical optimization engine module to perform hierarchical optimization, and the optimization results are used by the dynamic model generation module to build a prediction model.
[0017] The feedback adjustment module receives feedback information and adjusts the calculation parameters of the composite weight allocation module and the tiered optimization engine module in reverse to ensure iterative optimization of the model. Finally, the dynamic model generation module outputs the model data to the analysis result output module to generate a report.
[0018] Preferably, the composite weight allocation module further includes an adaptive adjustment submodule, which dynamically analyzes the weight value calculation process of each dimension based on the historical trend of the multidimensional operating data;
[0019] The adaptive adjustment submodule continuously monitors the output of data preprocessing and corrects the initial setting parameters of weight calculation in real time to ensure that the weight values conform to the nonlinear change pattern in complex mathematical operations.
[0020] Preferably, the tiered optimization engine module further includes a multi-level recursive optimization unit, which employs a hierarchical adaptive strategy when performing nonlinear optimization calculations.
[0021] The multi-level recursive optimization unit decomposes the tiered control strategy into multiple sub-objectives and independently applies the stochastic gradient descent method at each level to coordinate the convergence and speed of complex mathematical operations. This unit is also integrated with the output data of the composite weight allocation module and uses the weight values as initial inputs to perform global optimization calculations in multi-dimensional space.
[0022] Preferably, the feedback adjustment module is configured to execute an iterative calibration protocol generated by the dynamic model;
[0023] The feedback adjustment module receives the accuracy evaluation report from the analysis result output module and uses it to drive synchronous fine-tuning of the weight value algorithm of the composite weight allocation module and the nonlinear optimization parameters of the tiered optimization engine module.
[0024] When performing fine-tuning, this module uses a Bayesian inference mechanism to dynamically generate adjustment factors based on the error distribution of historical prediction models, and then converts these factors into executable instructions to be injected into weights and optimization calculations.
[0025] The feedback adjustment module also establishes a feedforward link with the data preprocessing module, using the abnormal data after standardization to trigger real-time adjustment operations, ensuring that complex mathematical operations remain robust when faced with data skew.
[0026] Preferably, the composite weight allocation module executes a nonlinear weight calculation formula based on entropy theory and adaptive decay when calculating the weight value, specifically:
[0027] Where, ω i (t) represents the weight value of the i-th data dimension at time t, which is output as the module output, H i (t) represents the information entropy value of the i-th dimension, s i r(t) is the current feature score for this dimension, r(t) is the error feedback signal provided by the feedback adjustment module, and η and k represent positive real constants, which adjust the relative contributions of information entropy and feedback factor, respectively. φ is the time decay coefficient, and φ is the decay rate. By integrating information entropy and feedback learning, high uncertainty dimensions are identified and their impact is reduced, thus reducing prediction bias caused by noise. At the same time, time decay avoids outdated data and ensures that weights change in real time, thereby reducing the average prediction error rate of the dynamic model generation module.
[0028] Preferably, the nonlinear optimization calculation of the tiered optimization engine module adopts a chaotic regularized gradient descent algorithm, and the calculation formula is as follows:
[0029] Where ΔΘ is the instantaneous optimization increment of the control parameter, Θ is the tiered control strategy vector, and η is the base learning rate. The adaptive decay term is controlled by the Frobenius norm, and ζ is the chaos coefficient. The gradient of the weighted loss function output by the composite weight allocation module, where t is the time iteration step. For chaotic perturbation terms, For random resonant frequency, u i The principal component basis vectors of the multidimensional running data are N, where N is the dimension number. The chaotic perturbation term introduces pseudo-random exploration in the flat optimization domain to avoid the model getting trapped in local minima. The spectral separation characteristics of the chaotic term isolate noise from the interference of the optimization direction.
[0030] Preferably, the data preprocessing module is extended to include a multidimensional data transformation submodule;
[0031] The multidimensional data conversion submodule performs an advanced noise removal process based on complex mathematical operations, using wavelet transform to analyze the frequency domain features of the running data and generate a denoised signal. The conversion logic of this submodule is linked with the composite weight allocation module. By embedding compatible parameters of the weight algorithm when outputting standardized data, it ensures that the data preprocessing results can be directly used for weight value calculation without additional calibration.
[0032] The multidimensional data conversion submodule integrates the reverse input of the feedback adjustment module. When a low-precision alarm occurs in the weight calculation, it automatically triggers data re-conversion to improve the baseline accuracy of mathematical operations.
[0033] Preferably, the analysis result output module further integrates a model accuracy self-evaluation unit;
[0034] The model accuracy self-evaluation unit performs error measurement processing based on complex mathematical operations by parsing the output prediction of the dynamic model generation module.
[0035] This unit uses a cross-validation framework to calculate model accuracy and shares evaluation results with the feedback adjustment module to directly trigger adjustments to the weight optimization parameters.
[0036] Preferably, the dynamic model generation module further includes a hybrid model building unit;
[0037] The hybrid model building unit integrates the weighted output data of the composite weight allocation module and the optimization results of the tiered optimization engine module to perform multi-source modeling operations with probability distribution fusion. This unit uses kernel density estimation technology to analyze the nonlinear optimized feature space and combines it with the bias calculation of weight values to generate a joint prediction model.
[0038] The hybrid model building unit introduces an uncertainty quantification mechanism, simulates the boundary error of weight allocation through Monte Carlo sampling, and feeds it back to the feedback adjustment module to assist in model iterative regeneration.
[0039] Technical effects and advantages of the present invention: Compared with the prior art, the cascade control operation data analysis and modeling system provided by the present invention has the following effects:
[0040] With the help of the closed-loop control of the feedback adjustment module, the mathematical operation parameters are designed to be automatically coupled with the environmental feedback signal. The adaptive adjustment mechanism makes the mathematical operation no longer dependent on the static model, but dynamically responds to external changes through nonlinear functions, ensuring that the optimization objective converges to the global optimum in real time.
[0041] In the composite weight allocation module, the innovative entropy theory-driven formula provides a structured explanation mechanism for weight allocation. The mathematical operation process quantifies uncertainty into a traceable entropy value index and maps the physical meaning to the weight decision, thereby replacing the traditional black box algorithm. This allows operators to intuitively trace the root cause of the control logic and improve the ability to diagnose faults and intervene manually. Attached Figure Description
[0042] Figure 1 This is a system architecture diagram for the cascade control operation data analysis and modeling system of the present invention. Detailed Implementation
[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.
[0044] This invention provides, for example Figure 1 The aforementioned tiered control operation data analysis and modeling system successfully bridges the collaboration gap between electronic digital data processing and mathematical operations. Through an end-to-end adaptive architecture, it achieves dynamic unification of data processing, operation, and feedback from a mechanistic perspective, significantly improving the real-time performance, adaptability, transparency, and stability of the control model in complex industrial scenarios.
[0045] The cascade regulation and control operation data analysis and modeling system includes:
[0046] The system includes a data acquisition module for real-time acquisition of multi-dimensional operational data. This module further integrates a real-time stream processing subunit. This subunit manages high-dimensional data sources through a timing buffer, ensuring seamless integration of the multi-dimensional operational data acquisition process into the data preprocessing module. During data injection, this subunit employs a sliding window mechanism, dynamically adjusting the sampling rate based on the initial weight reference value from the composite weight allocation module to avoid data overload causing computational efficiency bottlenecks in mathematical operations. The real-time stream processing subunit also collaborates with the dynamic model generation module, generating triggers from preprocessing outputs to initiate the model building loop. This subunit introduces an event-driven architecture, transforming sudden data changes into activation signals for weight calculations and optimizing the input sequence of the data stream using edge computing principles. Ultimately, this design enhances the adaptability of the mathematical operation module to the real-time environment, ensuring the integrity and smoothness of the tiered control operational data, and improving the overall system response speed.
[0047] The data preprocessing module is used to remove noise and standardize the collected data. This module is expanded to include a multidimensional data transformation submodule. This submodule performs an advanced noise removal process based on complex mathematical operations, using wavelet transform to analyze the frequency domain features of the data and generate a denoised signal. The transformation logic of this submodule is linked to the composite weight allocation module. By embedding compatible parameters of the weighting algorithm when outputting standardized data, it ensures that the preprocessed data can be directly used for weight calculation without additional calibration. Simultaneously, the multidimensional data transformation submodule integrates the inverse input of the feedback adjustment module. When a low-precision alarm occurs in the weight calculation, it automatically triggers data re-transformation to improve the baseline accuracy of the mathematical operations. The innovation lies in the submodule's phased anomaly detection mechanism, which simulates the hierarchical feature extraction principle of convolutional neural networks. It enhances data quality through parameterized thresholds to support the optimization effect of the tiered optimization engine module.
[0048] The composite weight allocation module is used to calculate the weight values of each dimension of the data based on a complex adaptive algorithm. This module also includes an adaptive adjustment submodule, which dynamically analyzes the weight calculation process based on the historical trends of the multidimensional running data. The adaptive adjustment submodule continuously monitors the output of data preprocessing and corrects the initial parameters of the weight calculation in real time, ensuring that the weight values conform to a non-linear change pattern in complex mathematical operations. This submodule also forms a bidirectional data link with the feedback adjustment module, enabling the weight calculation to be iteratively optimized based on the error feedback from the dynamic model generation module, thereby improving the computational stability and decision accuracy of the entire system when dealing with sudden changes. Furthermore, the execution process of the adaptive adjustment submodule utilizes the Hidden Markov Chain principle to predict the weight transfer path, but it is implemented entirely through parameterized logic to enhance the reversible interactivity of the operation and avoid external interference affecting the core mathematical processing.
[0049] Furthermore, an iterative collaborative link is established between the composite weight allocation module and the tiered optimization engine module. This iterative collaborative link achieves cross-module integration of complex mathematical operations by sharing intermediate computation results. Specifically, the weight values output by the composite weight allocation module are directly used as the initial input values for the tiered optimization engine module, while the backtracking error of the optimization engine is fed back to the weight allocation module for the next round of weight calculation. This link employs a distributed memory management strategy to ensure computational continuity under large data volumes. Simultaneously, the iterative collaborative link is monitored by a feedback adjustment module, which adjusts the step size parameters of the nonlinear optimization algorithm to adapt to real-time changes in weight distribution. The innovative logic lies in integrating reinforcement learning concepts to simulate system environmental responses, optimizing the iterative path by setting reward and penalty functions, and improving the convergence efficiency and decision reliability of mathematical operations in tiered control. Ultimately, this design achieves seamless interaction between multiple modules, enhancing the overall performance of the modeling system in complex scenarios.
[0050] It should be noted that the composite weight allocation module uses a nonlinear weight calculation formula based on entropy theory and adaptive decay when calculating weight values, specifically:
[0051] Where, ω i (t) represents the weight value of the i-th data dimension at time t, which is output as the module output, H i (t) represents the information entropy value of the i-th dimension, s i r(t) is the current feature score for this dimension, r(t) is the error feedback signal provided by the feedback adjustment module, and η and k represent positive real constants, which adjust the relative contributions of information entropy and feedback factor, respectively. Here, φ represents the time decay coefficient and the decay rate. By integrating information entropy and feedback learning, high-uncertainty dimensions are identified and their impact is reduced, thus minimizing prediction bias caused by noise. Simultaneously, time decay prevents old data from becoming outdated, ensuring real-time changes in weight responses and reducing the average prediction error rate of the dynamic model generation module.
[0052] The tiered optimization engine module is used to execute multi-level control strategies and perform nonlinear optimization calculations. The nonlinear optimization calculations in the tiered optimization engine module employ a chaotic regularized gradient descent algorithm, with the following formula:
[0053] Where ΔΘ is the instantaneous optimization increment of the control parameter, Θ is the tiered control strategy vector, and η is the base learning rate. The adaptive decay term is controlled by the Frobenius norm, and ζ is the chaos coefficient. The gradient of the weighted loss function output by the composite weight allocation module, where t is the time iteration step. For chaotic perturbation terms, For random resonant frequency, ui The principal component basis vectors of the multidimensional running data are N, where N is the dimension number. The chaotic perturbation term introduces pseudo-random exploration in the flat optimization domain to avoid the model getting trapped in local minima. The spectral separation characteristics of the chaotic term isolate noise from the interference of the optimization direction.
[0054] The dynamic model generation module integrates weighted optimization results to construct a predictive model. It also includes a hybrid model building unit. This unit integrates the weighted output data from the composite weight allocation module and the optimization results from the tiered optimization engine module, performing multi-source modeling operations involving probability distribution fusion. This unit utilizes kernel density estimation to analyze the nonlinearly optimized feature space and combines it with the bias calculation of weight values to generate a joint predictive model. Simultaneously, the hybrid model building unit introduces an uncertainty quantification mechanism, simulating boundary errors in weight allocation through Monte Carlo sampling and feeding them back to the feedback adjustment module to assist in iterative model regeneration. The innovative logic of this unit lies in its hierarchical fusion framework, which divides complex mathematical operations into parameter and structural layers for dual processing, enhancing the model's interpretability and generalization ability under tiered control strategies. This construction method strengthens the interaction and correlation between data, ensuring that the dynamic model has undergone continuous optimization to correct potential computational biases before output.
[0055] It should be noted that the model building process of the dynamic model generation module executes an entropy-weighted Bayesian fusion equation:
[0056] Among them, y pred The final predicted target value, β is the temperature parameter, H d Let f be the empirical conditional entropy of the d-th dimension data. BN (x) represents the prediction value from the Bayesian neural network. ρ is the dynamic modality proportion factor, and gDMD(X) is the data mutation detection value. ω This is the output of dynamic mode decomposition; entropy weight H d Suppressing the contribution of high uncertainty dimensions (such as wind power output noise) improves the model's average absolute accuracy under abnormal operating conditions. The dynamic mode proportion factor ρ automatically increases the weight of dynamic components when data changes abruptly, thus reducing the fitting error.
[0057] The feedback adjustment module receives feedback data and dynamically adjusts weights and optimization parameters. It is configured to execute an iterative calibration protocol generated by the dynamic model. Specifically, by receiving the accuracy assessment report from the analysis result output module, the feedback adjustment module drives synchronous fine-tuning of the weight algorithm in the composite weight allocation module and the nonlinear optimization parameters in the tiered optimization engine module. During the fine-tuning process, the module employs a Bayesian inference mechanism, dynamically generating adjustment factors based on the error distribution of historical prediction models, and converting these factors into executable instructions that are injected into the weight and optimization operations. The feedback adjustment module also establishes a feedforward link with the data preprocessing module, using standardized abnormal data to trigger immediate adjustment operations, ensuring the robustness of complex mathematical operations in the face of data skew. The protocol incorporates the concept of generative adversarial networks to simulate external disturbance scenarios, enhancing adaptive logic through abstract experiments, ultimately achieving seamless integration of parameter optimization from the mathematical operation module into the tiered control loop.
[0058] The analysis results output module generates and outputs a model analysis report. This module further integrates a model accuracy self-evaluation unit. This unit analyzes the output predictions from the dynamic model generation module and performs error measurement processing based on complex mathematical operations. When calculating model accuracy, this unit uses a cross-validation framework and shares the evaluation results with the feedback adjustment module to directly trigger adjustments to the weight optimization parameters. Simultaneously, when generating a visualization report, the model accuracy self-evaluation unit incorporates intermediate process data from the gradient optimization engine module, mapping the changes in weights and optimized values through heatmaps to enhance the decision support capability of the analysis report. This unit introduces an adversarial verification mechanism, simulating external disturbance scenarios to test model robustness, and using the results to guide the adaptive improvement of the composite weight allocation module. This design not only outputs the final report but also ensures that the calculation results of the mathematical operation module maintain high reliability after multiple iterations.
[0059] The aforementioned modules work through sequential and reverse flow interactions: the data acquisition module transmits data to the data preprocessing module for cleaning; the preprocessing module outputs standardized data to the composite weight allocation module for calculating weight values; the composite weight allocation module inputs the weighted data into the tiered optimization engine module for tiered optimization; the optimization results are used by the dynamic model generation module to build a predictive model; simultaneously, the feedback adjustment module receives feedback information and reversely adjusts the computational parameters of the composite weight allocation module and the tiered optimization engine module to ensure iterative model optimization; finally, the dynamic model generation module outputs the model data to the analysis result output module to generate a report; with mathematical operations as the core, seamless iterative modeling is achieved through the tiered structure of the weight allocation optimization control strategy.
[0060] The above modules provide the following effects: the data acquisition module ensures complete and real-time data; the data preprocessing module improves data quality; the composite weight allocation module implements complex weighting to improve accuracy; the tiered optimization engine module efficiently optimizes control strategies; the dynamic model generation module automatically generates high-precision models; the feedback adjustment module enhances the system's adaptive capabilities; and the analysis result output module provides decision support.
[0061] Optionally, the tiered optimization engine module further includes a multi-level recursive optimization unit that employs a hierarchical adaptive strategy when performing nonlinear optimization calculations. This unit decomposes the tiered control strategy into multiple sub-objectives and independently applies stochastic gradient descent at each level to coordinate the convergence and speed of complex mathematical operations. This unit also integrates with the output data of the composite weight allocation module, using weight values as initial inputs to perform global optimization calculations in multidimensional space. Simultaneously, the multi-level recursive optimization unit constructs a feedback loop that works collaboratively with the feedback adjustment module. When deviations occur in the optimization calculation, regularization constraints are automatically introduced to adjust the model's hyperparameters, ensuring that the mathematical operation process balances local minimization and overall stability of high-dimensional data. Furthermore, the unit's innovation lies in embedding time-varying factors into the optimization loop, simulating the principles of biological population evolution to enhance the analytical capability of nonlinear features and improve the response efficiency of the tiered structure in real-time data streams.
[0062] In summary, this cascade control operation data analysis and modeling system, through its innovative modular architecture and adaptive mathematical operation mechanism, achieves the following beneficial effects at the mechanistic level:
[0063] Eliminating the asynchronicity of data flow and improving the synergy of processing and computation: Through deep coupling of the data preprocessing module and the composite weight allocation module, the system integrates discrete sensor data acquisition end-to-end into a continuous stream, enabling the preprocessing and weight calculation of electronic digital data processing to be performed synchronously. This mechanism avoids the additional overhead of multi-source data alignment in traditional technologies, ensuring that mathematical operations directly apply to the normalized dynamic data stream, thereby seamlessly supporting real-time control decisions.
[0064] Enhance the dynamic environmental adaptability of mathematical operations: With the help of the closed-loop control of the feedback adjustment module, mathematical operation parameters, weighting factors and optimization strategies are designed to be automatically coupled with environmental feedback signals, load changes or weather disturbances. The adaptive adjustment mechanism makes mathematical operations no longer dependent on static models, but dynamically respond to external changes through nonlinear functions, ensuring that the optimization objective converges to the global optimum in real time.
[0065] Addressing interpretability bottlenecks and enhancing decision-making transparency: In the composite weight allocation module, an innovative entropy theory-driven formula provides a structured explanation mechanism for weight allocation. The mathematical operation quantifies uncertainty into traceable entropy values and maps physical meaning and data dimensional importance to weight decisions, thereby replacing traditional black-box algorithms. This allows operators to intuitively trace the root cause of control logic, improving fault diagnosis and manual intervention capabilities.
[0066] Enhancing the overall coordination between system robustness and computational efficiency: Through the iterative interaction between the tiered optimization engine module and the dynamic model generation module, the system constructs a fault-tolerant mechanism for mathematical operations at the mechanistic level. In this mechanism, data noise is dynamically isolated through embedded regularization and mode decomposition, and optimization computation achieves balanced resource allocation through multi-level feedback. This not only avoids computational overload but also ensures the self-maintenance of processing efficiency under high-dimensional data.
[0067] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A cascade control operation data analysis and modeling system, characterized in that, include: Run the data acquisition module to collect multi-dimensional operational data in real time; The data preprocessing module is used to remove noise and standardize the collected data. The composite weight allocation module is used to calculate the weight values of each dimension of the data; The tiered optimization engine module is used to execute multi-level control strategies and perform nonlinear optimization calculations; The dynamic model generation module is used to integrate weighted optimization results to build a predictive model; The feedback adjustment module is used to receive feedback data and dynamically adjust weights and optimize parameters; The analysis results output module is used to generate and output model analysis reports; The above modules work through sequential and reverse flow interaction: the running data acquisition module transmits data to the data preprocessing module for cleaning, and the preprocessing module outputs standardized data to the composite weight allocation module to calculate weight values; The composite weight allocation module inputs the weighted data into the hierarchical optimization engine module to perform hierarchical optimization, and the optimization results are used by the dynamic model generation module to build a prediction model. The feedback adjustment module receives feedback information and adjusts the calculation parameters of the composite weight allocation module and the tiered optimization engine module in reverse to ensure iterative optimization of the model. Finally, the dynamic model generation module outputs the model data to the analysis result output module to generate a report.
2. The cascade control operation data analysis and modeling system according to claim 1, characterized in that, The composite weight allocation module also includes an adaptive adjustment submodule, which dynamically analyzes the weight value calculation process of each dimension based on the historical trend of the multidimensional operating data. The adaptive adjustment submodule continuously monitors the output of data preprocessing and corrects the initial setting parameters of weight calculation in real time to ensure that the weight values conform to the nonlinear change pattern in complex mathematical operations.
3. The cascade control operation data analysis and modeling system according to claim 1, characterized in that, The tiered optimization engine module further includes a multi-level recursive optimization unit, which employs a hierarchical adaptive strategy when performing nonlinear optimization calculations. The multi-level recursive optimization unit decomposes the tiered control strategy into multiple sub-objectives and independently applies the stochastic gradient descent method at each level to coordinate the convergence and speed of complex mathematical operations. This unit is also integrated with the output data of the composite weight allocation module and uses the weight values as initial inputs to perform global optimization calculations in multi-dimensional space.
4. The cascade control operation data analysis and modeling system according to claim 1, characterized in that, The feedback adjustment module is configured to execute an iterative calibration protocol generated by the dynamic model. The feedback adjustment module receives the accuracy evaluation report from the analysis result output module and uses it to drive synchronous fine-tuning of the weight value algorithm of the composite weight allocation module and the nonlinear optimization parameters of the tiered optimization engine module. When performing fine-tuning, this module uses a Bayesian inference mechanism to dynamically generate adjustment factors based on the error distribution of historical prediction models, and then converts these factors into executable instructions to be injected into weights and optimization calculations. The feedback adjustment module also establishes a feedforward link with the data preprocessing module, using the abnormal data after standardization to trigger real-time adjustment operations, ensuring that complex mathematical operations remain robust when faced with data skew.
5. The cascade control operation data analysis and modeling system according to claim 2, characterized in that, The composite weight allocation module executes a nonlinear weight calculation formula based on entropy theory and adaptive decay when calculating weight values, specifically: Where, ω i (t) represents the weight value of the i-th data dimension at time t, which is output as the module output, H i (t) represents the information entropy value of the i-th dimension, s i r(t) is the current feature score for this dimension, r(t) is the error feedback signal provided by the feedback adjustment module, and η and k represent positive real constants, which adjust the relative contributions of information entropy and feedback factor, respectively. φ is the time decay coefficient, and φ is the decay rate. By integrating information entropy and feedback learning, high uncertainty dimensions are identified and their impact is reduced, thus reducing prediction bias caused by noise. At the same time, time decay avoids outdated data and ensures that weights change in real time, thereby reducing the average prediction error rate of the dynamic model generation module.
6. The cascade control operation data analysis and modeling system according to claim 3, characterized in that, The nonlinear optimization calculation of the tiered optimization engine module adopts a chaotic regularized gradient descent algorithm, and the calculation formula is as follows: Where ΔΘ is the instantaneous optimization increment of the control parameter, Θ is the tiered control strategy vector, and η is the base learning rate. Here, ζ is the adaptive decay term controlled by the Frobenius norm, and ζ is the chaos coefficient. The gradient of the weighted loss function output by the composite weight allocation module, where t is the time iteration step. For chaotic perturbation terms, For random resonant frequency, u i The principal component basis vectors of the multidimensional running data are N, where N is the dimension number. The chaotic perturbation term introduces pseudo-random exploration in the flat optimization domain to avoid the model getting trapped in local minima. The spectral separation characteristics of the chaotic term isolate noise from the interference of the optimization direction.
7. The cascade control operation data analysis and modeling system according to claim 1, characterized in that, The data preprocessing module was expanded to include a multidimensional data transformation submodule; The multidimensional data conversion submodule performs an advanced noise removal process based on complex mathematical operations, using wavelet transform to analyze the frequency domain features of the running data and generate a denoised signal. The conversion logic of this submodule is linked with the composite weight allocation module. By embedding compatible parameters of the weight algorithm when outputting standardized data, it ensures that the data preprocessing results can be directly used for weight value calculation without additional calibration. The multidimensional data conversion submodule integrates the reverse input of the feedback adjustment module. When a low-precision alarm occurs in the weight calculation, it automatically triggers data re-conversion to improve the baseline accuracy of mathematical operations.
8. The cascade control operation data analysis and modeling system according to claim 1, characterized in that, The analysis result output module further integrates a model accuracy self-evaluation unit; The model accuracy self-evaluation unit performs error measurement processing based on complex mathematical operations by parsing the output prediction of the dynamic model generation module. This unit uses a cross-validation framework to calculate model accuracy and shares evaluation results with the feedback adjustment module to directly trigger adjustments to the weight optimization parameters.
9. The cascade control operation data analysis and modeling system according to claim 1, characterized in that, The dynamic model generation module also includes a hybrid model construction unit; The hybrid model building unit integrates the weighted output data of the composite weight allocation module and the optimization results of the tiered optimization engine module to perform multi-source modeling operations with probability distribution fusion. This unit uses kernel density estimation technology to analyze the nonlinear optimized feature space and combines it with the bias calculation of weight values to generate a joint prediction model. The hybrid model building unit introduces an uncertainty quantification mechanism, simulates the boundary error of weight allocation through Monte Carlo sampling, and feeds it back to the feedback adjustment module to assist in model iterative regeneration.