Intelligent energy-saving control system and method based on multi-parameter coupling analysis and predictive maintenance

Abstract

The invention relates to the field of industrial automation control, and discloses an intelligent control system and method based on multi-parameter coupling analysis and predictive maintenance. According to the method, flow, temperature and water quality parameters are collected in real time through a sensor network, and after data cleaning and standardization processing, a parameter coupling relation model is constructed; an LSTM neural network is adopted to predict a system energy consumption trend, and a time sequence regression model is combined to analyze equipment performance attenuation characteristics; and when the predicted value exceeds a threshold value, dynamically adjusting operation parameters and optimizing load distribution to form closed-loop control. Accurate energy-saving control under complex working conditions is achieved, energy consumption can be reduced by 8%-12% through tests, the equipment maintenance cost is reduced by 15%-20%, and the system operation efficiency is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an intelligent energy-saving control system and method based on multi-parameter coupling analysis and predictive maintenance. Background Technology

[0002] Energy-saving control systems hold a crucial position in modern industry and environmental protection technologies, directly impacting the core objectives of energy efficiency and sustainable environmental development. With accelerating industrialization, energy-saving technologies are not only key to reducing operating costs but also essential for achieving green production. However, current energy-saving control methods often face deep-seated limitations, primarily in their insufficient adaptability to complex operating conditions and untimely response to dynamic system changes. These methods often fail to achieve precise control in the face of changing environmental factors and equipment conditions, resulting in persistent energy waste and equipment wear. Against this backdrop, the core challenges facing this field are becoming increasingly apparent. First, systems need to handle real-time changes in various parameters during operation, such as flow rate, temperature, and water quality indicators. The complex coupling relationships between these parameters make it difficult for traditional control methods to achieve optimal adjustment, thus affecting energy efficiency. As this issue deepens, another key factor emerges: insufficient ability to predict and maintain equipment conditions, especially the pollution trends and performance degradation of core components such as membrane modules, lacking effective early warning and dynamic adjustment mechanisms. These two factors are interconnected; the former leads to decreased system operating efficiency, while the latter further exacerbates equipment wear and energy consumption, creating a vicious cycle. Therefore, how to achieve precise adjustment of the energy-saving control system in a complex, multi-parameter coupled environment, and how to identify equipment performance degradation trends in advance through predictive maintenance technology, have become critical issues that urgently need to be addressed. Summary of the Invention

[0003] This invention provides an intelligent energy-saving control system and method based on multi-parameter coupling analysis and predictive maintenance, mainly comprising: S1: Real-time acquisition of multi-parameter data during system operation through sensor network, covering key information such as flow rate, temperature and water quality indicators, to build an initial multi-parameter dataset, laying the data foundation for subsequent dynamic change analysis and obtaining records of multi-parameter dynamic changes; S2: For records of dynamic changes in multiple parameters, data preprocessing methods are applied to clean and standardize the collected data, eliminate noise interference and data missing problems, ensure data consistency, and obtain a standardized parameter dataset. S3: Based on a standardized parameter dataset, construct a coupling relationship model between parameters, use a pre-established correlation analysis method to identify the interactive effects between parameters, and obtain quantitative results of parameter coupling relationships; S4: Based on the quantification results of parameter coupling relationship and combined with historical operating data, train a long short-term memory network model to predict the energy consumption change trend of the system under different operating conditions and obtain the energy consumption distribution prediction characteristics. S5: If the energy consumption distribution prediction characteristics exceed the preset threshold range, a rule-based dynamic adjustment mechanism is triggered to adjust the control parameters for the energy consumption distribution prediction characteristics to optimize the operating status and obtain the adjusted operating parameter configuration. S6: Based on the adjusted operating parameter configuration, update the system control commands in real time, continuously monitor the equipment status, collect performance data of core components, and obtain real-time feedback information on equipment status; S7: Based on real-time feedback information of equipment status, apply time series regression model to analyze the contamination trend and performance degradation characteristics of core components, assess potential performance degradation risks, and obtain performance risk assessment results; S8: If the performance risk assessment results show that the risk of decline exceeds the preset threshold range, an early warning signal is generated, and the maintenance scheduling module is linked to adjust the operating load to obtain an optimized load allocation scheme. S9: Based on the optimized load distribution scheme, update the system operation strategy to reduce the risk of equipment wear and tear. At the same time, store all processing data and adjustment records from this operation into the database for subsequent model iteration and parameter optimization, and obtain a complete system operation optimization record.

[0004] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses an intelligent energy-saving control system and method based on multi-parameter coupling analysis and predictive maintenance, addressing energy consumption optimization and equipment performance management issues under complex operating conditions. The system collects multi-parameter data such as flow rate, temperature, and water quality in real time, constructs a parameter coupling relationship model, and uses a long short-term memory network to predict energy consumption trends. Based on the prediction results, the invention dynamically adjusts operating parameters to optimize energy consumption distribution. Simultaneously, the invention applies a time series regression model to analyze the pollution trends and performance degradation characteristics of core components, achieving predictive maintenance of equipment status. When potential risks are detected, the invention automatically generates early warning signals and adjusts load allocation, effectively reducing equipment wear and tear risks. Through the synergy of multi-dimensional data analysis and intelligent control strategies, this invention significantly improves the accuracy of energy-saving control and the reliability of system operation, providing an innovative solution for efficient energy utilization and long-term equipment management in industrial production, and is of great significance for promoting green manufacturing and sustainable development. Attached Figure Description

[0005] Figure 1 This is a flowchart of the intelligent control system and method based on multi-parameter coupling analysis and predictive maintenance according to the present invention.

[0006] Figure 2This is a schematic diagram of the intelligent control system and method based on multi-parameter coupling analysis and predictive maintenance according to the present invention.

[0007] Figure 3 This is another schematic diagram of the intelligent control system and method based on multi-parameter coupling analysis and predictive maintenance according to the present invention. Detailed Implementation

[0008] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0009] like Figure 1-3 The intelligent control system and method based on multi-parameter coupling analysis and predictive maintenance in this embodiment may specifically include: Steps S101 and S1: Real-time acquisition of multi-parameter data during system operation via sensor network, covering key information such as flow rate, temperature and water quality indicators, to construct an initial multi-parameter dataset, laying the data foundation for subsequent dynamic change analysis and obtaining records of multi-parameter dynamic changes.

[0010] Real-time data acquisition of system operation status is achieved through a sensor network, covering multiple parameters such as flow rate, temperature, and water quality indicators, to construct an initial dataset and obtain multi-dimensional monitoring results. Based on the multi-dimensional monitoring results in the initial dataset, data preprocessing techniques are used to clean and format the collected flow rate, temperature, and water quality indicators, obtaining standardized data sets. For these standardized data sets, time series analysis is used to detect dynamic changes in the multi-parameter information, determining the trends and anomaly distribution of each parameter. If the distribution of anomalies in the dynamic changes exceeds a preset threshold, an anomaly alarm mechanism is triggered. Combined with historical data analysis of the corresponding flow rate, temperature, and water quality indicators, the anomaly source category is determined. Based on the anomaly source category, a pre-established classification model is used to further subdivide the anomalies, and a support vector machine algorithm is employed for pattern recognition of the anomaly data to obtain specific classification results. Based on the specific anomaly classification results, targeted adjustment strategy data is generated, and combined with the system operation status and real-time acquired multi-parameter information, the optimized operating parameter configuration is determined. The system acquires optimized operating parameter configurations, automatically updates system operating rules, and dynamically adjusts flow information, temperature data, and water quality indicators to obtain continuous and stable monitoring data.

[0011] For example, a sensor network can be used to collect multi-parameter data in real time during system operation. Specifically, a high-precision sensor array can be deployed at key nodes of the system. For instance, in a water treatment system, flow sensors, temperature sensors, and a water quality analyzer can be installed to collect flow rate data per minute (e.g., average 50.3 cubic meters per hour), temperature data (e.g., real-time value 25.8 degrees Celsius), and water quality indicators (e.g., pH 7.2, turbidity 3.5 NTU). This data is transmitted to a cloud database via an Internet of Things (IoT) protocol (e.g., MQTT) to form an initial multi-parameter dataset. Subsequently, time series analysis algorithms (e.g., the ARIMA model) are used to preprocess the collected data, removing outliers (e.g., noise data with flow rate fluctuations exceeding ±10%), and constructing a multi-parameter dataset containing timestamps, laying the foundation for subsequent dynamic change analysis. During this process, the system automatically standardizes the data to ensure that parameters with different dimensions (e.g., temperature and flow rate) are compared on the same scale; for example, temperature data is normalized to the range of 0 to 1 (25.8 degrees Celsius corresponds to 0.42). Next, based on the initial dataset, the system extracts dynamic change records of multiple parameters using a sliding window algorithm (window size set to 30 minutes), calculates the short-term rate of change of each parameter (e.g., flow rate change rate of 0.5 cubic meters per minute per hour), and combines it with historical data to predict trends, generating dynamic change curves to help identify potential anomalies (e.g., temperature rise exceeding 2 degrees Celsius for 5 consecutive minutes). To establish a rigorous logical relationship, the system performs correlation analysis between the dynamic change records and the operating status of the water treatment equipment (e.g., pump power changes), using Pearson correlation coefficient calculations (e.g., flow rate and power correlation coefficient of 0.85) to reveal the potential causal relationship between parameter changes and equipment operation, thereby providing data support for subsequent optimization control.

[0012] Steps S102 and S2: For records of dynamic changes in multiple parameters, data preprocessing methods are applied to clean and standardize the collected data, eliminate noise interference and data loss issues, ensure data consistency, and obtain a standardized parameter dataset.

[0013] For the collected data, initial multi-parameter dynamic change records are obtained through a data acquisition system. Batch reading is used to extract complete data records from the storage unit, resulting in a preliminary dataset. Based on this preliminary dataset, data cleaning methods are employed to process the data records. To address noise interference, median filtering is used to remove outliers. If a data point deviates from a preset threshold range, it is replaced with the median of neighboring data points, resulting in a cleaned dataset. Using the cleaned dataset, interpolation methods are applied to fill in missing data. If null values ​​are found, linear interpolation is performed based on the trend of preceding and following data points to determine the complete, filled dataset. Based on the complete dataset, standardization is implemented. For the different dimensions of the multi-parameter data, standard deviation normalization is used to map each parameter value to a uniform range, resulting in a standardized dataset. Using the standardized dataset, data consistency is checked. If some data within the parameter dataset deviates from the overall trend, cluster analysis is used to regroup the data to determine if consistency requirements are met, resulting in a consistent dataset. Based on the consistent dataset, a time series analysis model is constructed to analyze the dynamic change characteristics. A sliding window method is used to extract the change trend, determining the final parameter dataset. The final parameter dataset is stored in a pre-defined database and stored in shards using distributed storage technology to obtain structured data resources available for subsequent analysis.

[0014] For example, in the data preprocessing of records of dynamic changes in multiple parameters, the first step is data cleaning. Assume we collected data on three parameters—temperature, pressure, and vibration frequency—from an industrial device during operation. The data collection period was once per minute for 24 hours, resulting in 1440 sets of data. We found that 5% of the temperature data was missing, the pressure data contained outliers (e.g., a pressure value of 9999 at a certain moment, far exceeding the normal range of 0-100), and the vibration frequency data had noise interference. Missing values ​​are handled using algorithms. Linear interpolation is used to fill in missing temperature data. For example, if the temperature is missing at a certain moment, and the temperatures before and after are 25.5 degrees and 26.0 degrees respectively, the interpolation result is 25.75 degrees. For abnormal pressure values, a threshold range of 0-100 is set. Values ​​outside this range are replaced with the average of the previous 5 minutes. For example, the abnormal value 9999 is replaced with the average of the previous 5 minutes, 85.3. Vibration frequency noise is processed using a moving average filter with a window size of 5. The average of the 5 data points before and after each point is calculated to smooth the noise. For example, if the frequency at a certain point is 10.2, and the data within the window are 9.8, 10.0, 10.2, 10.4, and 10.1, the smoothed value is 10.1. Next, data standardization is performed to ensure consistency across parameters with different dimensions. Z-score standardization is used to convert each parameter value into a distribution with a mean of 0 and a standard deviation of 1. For example, with a temperature mean of 25.0°C and a standard deviation of 2.0, a temperature of 26.0°C standardized to (26.0-25.0) / 2.0 = 0.5. Pressure and vibration frequency are processed in the same way, ultimately yielding a standardized parameter dataset. Through these cleaning and standardization operations, missing values, outliers, and noise interference are eliminated, ensuring data consistency and laying the foundation for subsequent analysis. Logically, the cleaned, complete data provides reliable input for standardization, while the standardized dataset facilitates comparison and modeling between multiple parameters, forming a complete processing chain from raw data to a usable dataset.

[0015] Steps S103 and S3: Based on the standardized parameter dataset, construct a coupling relationship model between parameters, use a pre-established correlation analysis method to identify the interactive influence between parameters, and obtain the quantitative results of parameter coupling relationship.

[0016] The standardized parameter dataset is initially organized to obtain the basic characteristic distribution of each parameter, ensuring its completeness and consistency. If missing or outlier values ​​exist in the characteristic distribution, a pre-defined interpolation method is used to complete the data, resulting in a processed parameter dataset. For this processed dataset, correlation analysis is employed to calculate the correlation coefficients between parameters, obtaining the initial correlation strength and determining if significant interactive effects exist. Based on the initial correlation strength, a coupling relationship model is constructed, and the linear relationship between parameters is analyzed using a pre-established Pearson correlation coefficient method, yielding the initial structure of the coupling relationship. For the coupling relationships in the initial structure, the model parameters are adjusted based on the significance of the interactive effects, resulting in an optimized relationship model and determining the specific form of parameter coupling. Using the optimized relationship model, the interactive effects between parameters are quantified, and statistical tools are used to calculate the influence weights, obtaining the quantified results of the parameter coupling relationship. Based on the quantification results, the influence of key parameters on the overall model is identified. If the influence weight of a certain parameter exceeds a pre-defined threshold, it is marked as a major driving factor, determining the final set of key parameters. For the set of key parameters, a detailed mapping table of parameter coupling relationships is generated, and the interaction effects between parameters are presented through data visualization tools to obtain the final analysis output.

[0017] For example, the specific implementation method for constructing a parameter coupling relationship model and performing correlation analysis based on a standardized parameter dataset is as follows: First, assume we have a standardized dataset containing multiple parameters, such as the operating data of an industrial device, including three parameters: temperature (mean 0, standard deviation 1 after standardization), pressure (mean 0, standard deviation 1), and flow rate (mean 0, standard deviation 1), with a data sample size of 1000 records. Next, when constructing the parameter coupling relationship model, the Pearson correlation coefficient method is used to quantify the linear correlation between parameters. The calculation formula is r = Σ[(x_i x_mean)(y_i y_mean)] / [n * σ_x * σ_y], where x_i and y_i are the observed values ​​of the two parameters, x_mean and y_mean are the means, σ_x and σ_y are the standard deviations, and n is the sample size. Calculations revealed correlation coefficients of 0.75 between temperature and pressure, 0.32 between temperature and flow rate, and 0.68 between pressure and flow rate, indicating a strong positive correlation between temperature and pressure, while the correlation between temperature and flow rate was weak. Subsequently, based on the correlation coefficient matrix, Principal Component Analysis (PCA) was used to further identify the interactive effects between parameters, extracting the contribution rate of the principal components. Assuming the first principal component explains 85% of the dataset variance, this indicates that temperature and pressure are the main coupling factors. To quantify the coupling relationship, a regression model was constructed, assuming pressure = 0.6 * temperature + 0.2 * flow rate + ε (error term). The model was fitted using the least squares method, yielding an R² value of 0.82, indicating a strong explanatory power for the coupling relationship. Finally, the correlation coefficients and regression results were integrated to form a quantitative result of the parameter coupling relationship, output as a matrix and stored in a database for subsequent equipment optimization analysis. Through these methods, the interactive effects between parameters were clearly identified, providing data support for system optimization.

[0018] Steps S104 and S4: Based on the quantification results of parameter coupling relationship and combined with historical operating data, train a long short-term memory network model to predict the energy consumption change trend of the system under different operating conditions and obtain the energy consumption distribution prediction characteristics.

[0019] By collecting data on the parameter coupling relationships during system operation and employing quantitative analysis methods to process the correlations between parameters, a parameter coupling feature matrix is ​​obtained. Based on this feature matrix, historical data and operational data are integrated to construct a comprehensive dataset, determining the system's operational status characteristics across different time periods. For these operational status characteristics, a Long Short-Term Memory (LSTM) network model is constructed and trained to capture the time-series characteristics of energy consumption changes, obtaining preliminary trends in energy consumption. Based on these preliminary trends and considering the diversity of system operating conditions, model parameters are adjusted to predict energy consumption trends under various operating conditions, resulting in a condition-related trend distribution. Features are extracted from this condition-related trend distribution, and the predicted characteristics of the energy consumption distribution are analyzed. It is determined whether the distribution characteristics meet a preset threshold range; if they are below the threshold, the model input data is readjusted to obtain more accurate distribution characteristics. Based on these distribution characteristics, the predicted energy consumption distribution results are mapped to generate energy consumption distribution maps for the system under different operating conditions, determining the final energy consumption distribution prediction result.

[0020] For example, in the process of predicting system energy consumption, the first step is to quantify the results based on parameter coupling relationships and then perform data preprocessing and feature extraction using historical operating data. Assuming the system operating data includes three parameters: temperature, pressure, and flow rate, correlation analysis reveals that the correlation coefficients between temperature and energy consumption are 0.85, pressure is 0.62, and flow rate is 0.73, indicating that temperature has the greatest impact on energy consumption. Next, these parameter data are organized according to time series, selecting 8760 hourly data points from the past year. After removing missing and outlier values, 8000 valid samples are obtained for subsequent modeling. Then, a Long Short-Term Memory (LSTM) network model is trained using these data to predict energy consumption trends under different operating conditions. In the specific implementation, the data was divided into a training set (70%, i.e., 5600 samples) and a test set (30%, i.e., 2400 samples). The input layer was set with three features (temperature, pressure, and flow rate), and the hidden layer contained 50 neurons. The Adam optimization algorithm was used with a learning rate of 0.001, 100 training epochs, and the loss function was mean squared error (MSE). The final model's prediction error on the test set was 5.2%. The model predicted the energy consumption distribution characteristics for the next 24 hours, finding that the average energy consumption during peak periods was 120.5 kWh, fluctuating between 110.3 and 130.7 kWh. Finally, based on the prediction results, energy consumption distribution characteristic analysis, combined with business needs (such as production scheduling), revealed a high correlation between peak energy consumption and production load, with a correlation coefficient of 0.88. Further optimization of scheduling strategies can reduce peak energy consumption to below 115 kWh, reducing energy costs by approximately 5%. The entire process, including data processing, model training, and result analysis, was automated using scripts to ensure prediction accuracy and business relevance.

[0021] Steps S105 and S5: If the energy consumption distribution prediction characteristics exceed the preset threshold range, a rule-based dynamic adjustment mechanism is triggered to adjust the control parameters for the energy consumption distribution prediction characteristics to optimize the operating state and obtain the adjusted operating parameter configuration.

[0022] Step 1: Acquire energy consumption distribution data. Extract features from historical operation records to analyze predicted feature values ​​of energy consumption distribution, obtaining a preliminary predicted feature set. Step 2: Compare the predicted feature set with preset thresholds. If the predicted feature value exceeds the preset threshold range, an anomaly flag is triggered, indicating the need to initiate an adjustment process. Step 3: Based on the anomaly flag, a rule-based dynamic adjustment mechanism is employed, combined with a preset rule base, to match the control parameter adjustment strategy corresponding to the predicted feature value, obtaining a preliminary adjustment plan. Step 4: Simulate and verify the preliminary adjustment plan, analyzing the impact of the adjustment strategy on the operating status, determining whether the adjustment plan meets the optimization objectives, and obtaining a verified adjustment plan. Step 5: If the verified adjustment plan meets the optimization objectives, apply the control parameters in the adjustment plan to the operation adjustment process, obtaining updated parameter configuration data. Step 6: Based on the updated parameter configuration data, monitor changes in the operating status in real time, analyze the optimization effect of energy consumption distribution, and determine whether the operation adjustment has achieved the expected state. Step 7: If the operation adjustment does not achieve the expected state, the energy consumption distribution data is extracted a second time through regression analysis model, the predicted feature set is updated, and the above comparison and adjustment process is repeated to obtain the final operating parameter configuration.

[0023] For example, in the scenario of energy consumption distribution prediction and optimization, the first step in obtaining the energy consumption distribution prediction characteristics can be achieved by combining historical data with a machine learning model.

[0024] For example, using energy consumption data from the past 30 days, collected hourly (720 data points in total), this data is input into a prediction model based on a Long Short-Term Memory (LSTM) network. The model is trained to predict energy consumption distribution characteristics for the next 24 hours, resulting in hourly energy consumption values ​​between 500 and 800 kWh, with a prediction error controlled within 5%. Then, if the predicted characteristics exceed a preset threshold range (e.g., 400 to 900 kWh), the system automatically triggers a rule-based dynamic adjustment mechanism. Specifically, when the predicted energy consumption exceeds 900 kWh, the system reduces the equipment operating power in the energy consumption control parameters by 10%, from 1000 kW to 900 kW. Simultaneously, it records the energy consumption trend before and after the adjustment. By comparing and analyzing whether the adjusted energy consumption returns to the threshold range, if it still exceeds it, the power is further reduced by 5% until the requirements are met. Subsequently, the system adjusts control parameters based on the predicted energy consumption distribution characteristics to optimize operation. Based on the adjusted power parameters and real-time environmental data (e.g., temperature 25 degrees Celsius, humidity 60%), the system calculates the optimal operating parameter configuration using a preset optimization algorithm (e.g., a genetic algorithm). For example, the air conditioner's operating frequency is adjusted from 50 Hz to 45 Hz, and the fan speed is reduced from 3000 rpm to 2800 rpm. The system then generates an adjusted operating parameter configuration table, which is stored in the database for later retrieval and analysis. Finally, the system automatically sends the adjusted parameter configuration to the equipment control module, ensuring the equipment operates according to the optimized parameters. Simultaneously, it monitors energy consumption data in real time. If a deviation exceeds 3%, the system re-enters the prediction and adjustment cycle, forming a closed-loop control logic to ensure that energy consumption distribution remains within a reasonable range. Through this process, from prediction to adjustment to optimization, a complete technical chain is formed, ensuring the system's high efficiency and stability.

[0025] Steps S106 and S6: Based on the adjusted operating parameter configuration, update the system control commands in real time, continuously monitor the equipment status, collect performance data of core components, and obtain real-time feedback information on the equipment status.

[0026] For data on operational parameters and configuration adjustments, the system obtains current operational parameter records and compares them with preset threshold ranges to determine if the parameters meet expected standards, thus obtaining parameter adjustment requirements. Based on these requirements, a pre-established mapping rule is used to generate a corresponding system control instruction set, determining the priority and execution order of the control instructions. The generated control instruction set is then transmitted to the device control module via a real-time transmission channel, and the device response data after instruction execution is obtained to determine if the instructions were successfully applied. If successfully applied, the device status is continuously monitored, and operational signals of core components are acquired from device sensors to obtain performance data records. Based on these performance data records, a support vector machine algorithm is used for anomaly detection to determine if potential performance anomalies exist, and the anomaly detection results are obtained. If a performance anomaly is detected, a preset alarm mechanism generates status information feedback, obtaining real-time feedback data to determine the device status adjustment requirements. Based on the real-time feedback data and adjustment requirements, the system control instruction set is updated to generate a new operational parameter configuration scheme, completing the dynamic optimization of the device status.

[0027] For example, regarding real-time updates of control commands and equipment status monitoring after system operating parameters are adjusted, after parameter configuration, the system automatically calculates the specific control command output value using built-in control algorithms, such as PID control, with the proportional coefficient P set to 2.0, the integral coefficient I to 0.5, and the derivative coefficient D to 0.1, ensuring the stability and accuracy of the command output. This is then transmitted in real-time to the equipment controller to complete the command update. Simultaneously, the system collects data every second using sensors deployed on core equipment components, such as temperature and vibration sensors, obtaining real-time values ​​for the motor bearing temperature (e.g., 26.3 degrees Celsius) and vibration frequency (e.g., 5.2 Hz), and uploads this data to a cloud database for storage. Next, the system processes the collected data using a pre-defined performance analysis model. Employing a threshold-based anomaly detection algorithm, with a temperature threshold set at 30.0 degrees Celsius and a vibration frequency threshold at 6.0 Hz, if the current value approaches or exceeds the threshold, an automatic warning signal is generated. Combined with historical data trend analysis (e.g., the average temperature over the past 24 hours is 25.8 degrees Celsius), the system assesses whether there are potential equipment failure risks. Finally, the system updates the real-time feedback information to the monitoring platform in the form of visual charts, displaying the fluctuation curves of temperature and vibration data, facilitating subsequent optimization of control strategies. Through this series of automated processes, from parameter updates to data collection, analysis, and feedback, a closed-loop management logic is formed, ensuring the stability and reliability of equipment operation. If business expansion is involved, such as association with energy management, the system can further calculate the equipment's operating power (e.g., current power is 5.5 kW), combine it with historical energy consumption data (average power 5.0 kW), analyze the energy efficiency ratio, optimize operating parameters, and reduce energy waste.

[0028] Steps S107 and S7: Based on the real-time feedback information of equipment status, apply a time series regression model to analyze the contamination trend and performance degradation characteristics of core components, assess the potential performance degradation risk, and obtain the performance risk assessment results.

[0029] Real-time feedback data on equipment status is collected by sensors, and various indicators during the operation of core components are continuously monitored to obtain raw operational datasets. Data preprocessing techniques are used to clean and format the raw operational datasets, imputing outliers and missing values ​​to obtain standardized operational datasets. Time series regression models are used to analyze the standardized operational datasets, modeling the contamination trends and performance degradation characteristics of core components to determine their changing trends. Based on the trend analysis results and preset threshold ranges, the degree of performance degradation of core components is judged. If the performance degradation index exceeds the threshold range, it is marked as a high-risk state, resulting in a risk-labeled dataset. Statistical analysis tools are used to quantify the distribution and influencing factors of the risk of degradation in the risk-labeled dataset, obtaining the probability distribution results of potential degradation risks. Based on the probability distribution results, combined with historical operational data and current equipment status, the performance degradation risk level of core components is determined. If the risk level is higher than the preset standard, an early warning mechanism is triggered, determining the final risk assessment result. Based on the final risk assessment results, a maintenance priority ranking for core components is generated, resulting in an optimized maintenance scheduling plan.

[0030] For example, regarding the processing of real-time feedback information on equipment status, firstly, operational data of the core components of the equipment, such as temperature, pressure, and vibration frequency, are collected every minute using IoT sensors. Assuming that the temperature data of a certain component is collected at one reading per minute over five consecutive hours, 300 data points are obtained, with a temperature range between 50.3 and 78.9 degrees Celsius. Next, a time-series regression model is applied to analyze the pollution trend and performance degradation characteristics of the core components. Specifically, an ARIMA model (Autoregressive Integral Moving Average) is used, with parameters set to (p=2, d=1, q=1). By fitting historical temperature data, the temperature trend for the next 24 hours is predicted, yielding a predicted range of 52.1 to 80.4 degrees Celsius. Combined with pollution indicators (such as particulate matter concentration data, assuming a current value of 0.25 mg / m³ and a historical average of 0.18 mg / m³), the abnormal temperature rise trend caused by pollution accumulation is analyzed, and the pollution-temperature impact coefficient is calculated to be 0.15. Further assessment of potential performance degradation risk was conducted. Using the regression model output and combined with equipment operating power data (assuming current power of 85.6kW and rated power of 100kW), the performance degradation rate was calculated to be 14.4%, with a threshold of 20%, classifying the current risk level as medium. Finally, the performance risk assessment results were obtained, and the system automatically generated a risk report, displaying a performance degradation rate of 14.4%, a risk level of medium, and predicting that if the pollution concentration continues to rise to 0.30mg / m³, the degradation rate may reach 18.2%, approaching the threshold, requiring an early warning mechanism to be triggered. This process, through data acquisition, model analysis, and risk calculation, forms a closed-loop logic, ensuring the real-time nature and accuracy of equipment status monitoring. The system automatically executes all steps without manual intervention.

[0031] Steps S108 and S8: If the performance risk assessment results show that the risk of decline exceeds the preset threshold range, an early warning signal is generated, and the maintenance scheduling module is linked to adjust the operating load to obtain an optimized load allocation scheme.

[0032] Step 1: Obtain performance risk-related indicators from system operation data. Analyze historical data and compare it with current data to determine the performance risk assessment results. Step 2: If the performance risk assessment results show that the risk decline exceeds a preset threshold, the information processing module classifies and grades the risk decline data to obtain a specific risk level classification. Step 3: Based on the risk decline level classification, trigger the corresponding early warning signal generation process. Generate a specific level of early warning signal using pre-established signal mapping rules. Step 4: After the early warning signal is generated, transmit the signal data to the load adjustment unit through the linkage maintenance scheduling module to obtain the current load distribution status. Step 5: Based on the load distribution status, use a preset load allocation model and a linear regression algorithm to optimize the load data, obtaining a preliminary adjusted load allocation scheme. Step 6: For the preliminary adjusted load allocation scheme, use system simulation tools to verify the scheme in multiple scenarios to determine its stability performance under different scenarios. Step 7: If the stability performance meets preset standards, the verified load allocation scheme is used as the final optimized scheme and transmitted to the execution module for load adjustment.

[0033] For example, in the performance risk assessment process, the system first calculates the performance degradation risk value of the device using historical operating data and real-time monitoring data.

[0034] For example, suppose a piece of equipment has a normal operating power of 1000 kW, and the current monitored power drops to 900 kW, a decrease of 10%. The system sets a preset threshold of 5%. By comparing the current power and finding that the decrease risk exceeds the threshold range, a risk assessment algorithm is triggered. The algorithm uses a weighted average method, combining the equipment's operating time, ambient temperature (assuming the current temperature is 35 degrees Celsius, exceeding the suitable range by 2 degrees Celsius), and load rate (current load rate is 85%), to calculate a comprehensive risk index of 7.5 (out of 10), which is higher than the warning threshold of 6.0. Subsequently, the system automatically generates an early warning signal and transmits the signal to the maintenance scheduling module through an internal communication interface. Based on the warning signal, the maintenance scheduling module calls a load optimization algorithm, such as using linear programming, to adjust the current equipment's operating load from 85% to 70%, and allocates the reduced 15% load to standby equipment (increasing the standby equipment's load rate from 50% to 65%). During the optimization process, the system analyzes changes in total energy consumption in real time to ensure that total energy consumption is controlled within 102% of the original plan, while verifying the operating parameters of backup equipment (such as voltage stability within 220V ± 5V). Finally, an optimized load allocation scheme is generated, recorded as 70% load for equipment A and 65% load for equipment B, and stored in the database for subsequent operational monitoring. Through this series of automated processes, the system achieves closed-loop management of the entire process from risk identification to load adjustment, ensuring equipment operational stability and overall system efficiency.

[0035] Steps S109 and S9: Based on the optimized load distribution scheme, update the system operation strategy to reduce the risk of equipment wear and tear. At the same time, store all processing data and adjustment records from this operation into the database for subsequent model iteration and parameter optimization to obtain a complete system operation optimization record.

[0036] By analyzing the load allocation scheme, the current system's operating status data and load distribution are obtained. Nodes with uneven distribution are initially screened to obtain a priority list for load adjustment. Based on this priority list, the operating strategy is dynamically adjusted using preset rules, reducing the operating intensity of high-load nodes to determine the adjusted operating strategy. Using the adjusted operating strategy, the changing trends of equipment operating parameters are obtained. Parameters exceeding preset thresholds are monitored in real time to identify potential points of equipment wear risk. Based on these potential points, the operating logs and historical data of relevant nodes are obtained. A support vector machine algorithm is used to classify risk points and determine the distribution of high-risk areas. Based on the distribution of high-risk areas, corresponding processing data and adjustment records are obtained. Key operations in the adjustment records are archived and stored in a database, forming a structured dataset. Based on this structured dataset, historical optimization records and complete records are obtained. Pattern characteristics in the records are analyzed to determine the direction of subsequent model iterations and the focus of parameter optimization. Based on the direction of model iterations and the focus of parameter optimization, system operation feedback data is obtained. Anomalies in the feedback data are corrected to determine the final system operation optimization record.

[0037] For example, regarding the updated system operation strategy and related data processing for the optimized load allocation scheme, the intelligent scheduling system first adjusts the equipment operating parameters according to the optimized load allocation scheme. For instance, the load of a certain piece of equipment is reduced from 80% to 60% to reduce the risk of overload. Specifically, historical operating data analysis shows that the equipment's loss rate is only 0.002 at 60% load, while it is as high as 0.005 at 80% load. Given that the current total load demand is 5000 kW, the system automatically allocates 3000 kW of load to this equipment, with the remaining 2000 kW shared by other equipment, ensuring that overall losses are minimized. Next, the system automatically generates an adjusted operation strategy file, including equipment number, adjustment time, load value, etc., and verifies the feasibility of the strategy through internal algorithms, such as using linear programming algorithms to find the optimal solution, ensuring that the total loss is controlled below 0.01. Subsequently, all processed data from this operation, including values ​​before and after load adjustment, equipment status parameters such as temperature (45 degrees Celsius) and current (300 amps), and adjustment records such as the timestamp 2023-10-01 14:30:00 and the adjustment range of 20%, are automatically stored in a distributed database through the database management system. The data is stored in a structured format for easy subsequent querying and analysis. Next, the system initiates a data analysis module to iterate the model using the stored data. Specifically, machine learning algorithms such as the random forest model are employed to perform regression analysis on the historical load distribution and loss relationship, deriving new optimization parameters. For example, the predicted load distribution ratio error is reduced from 5% to 3%, thus updating the model weights. Finally, the system generates a complete operation optimization record, including all adjustment steps, data trend charts, and an optimization effect evaluation report. For example, the report shows that the loss rate decreased from 0.005 to 0.002, and the energy saving rate increased by 10%. The record is automatically archived to cloud storage for future system upgrades and strategy optimization references, ensuring a closed-loop logic throughout the process and a close integration of data processing with business needs.

[0038] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An intelligent control system and method based on multi-parameter coupling analysis and predictive maintenance, characterized in that, Includes the following steps: S1: Collect flow rate, temperature and water quality parameters in real time through sensor network to construct an initial multi-parameter dataset; S2: Clean and standardize the initial multi-parameter dataset to eliminate noise and missing data, and generate a standardized parameter dataset. S3: Construct a parameter coupling relationship model based on the standardized parameter dataset, quantify the interaction influence between parameters through correlation analysis, and generate quantitative results of parameter coupling relationship; S4: Combine historical operating data and the quantification results of the parameter coupling relationship to train a long short-term memory network model, predict the energy consumption change trend under different operating conditions, and generate energy consumption distribution prediction features. S5: If the predicted energy consumption distribution characteristics exceed the preset threshold range, a dynamic adjustment mechanism is triggered to adjust the control parameters and generate the adjusted operating parameter configuration. S6: Configure and update system control commands according to the adjusted operating parameters, and monitor equipment status in real time to collect performance data of core components and generate real-time feedback information on equipment status; S7: Based on the real-time feedback information of the equipment status, apply a time series regression model to analyze the pollution trend and performance degradation characteristics of the core components, assess the risk of performance degradation, and generate a performance risk assessment result. S8: If the performance risk assessment result exceeds the preset risk threshold, an early warning signal is generated and the maintenance scheduling module is linked to adjust the operating load and generate an optimized load allocation scheme. S9: Update the system operation strategy according to the optimized load distribution scheme, and store the processed data and adjustment records into the database to iterate the model parameters.

2. The method according to claim 1, characterized in that, S1 includes: collecting multi-dimensional monitoring results of flow rate, temperature, and water quality indicators through a sensor network; cleaning and formatting the multi-dimensional monitoring results to generate a standardized data set; detecting the dynamic change characteristics and anomalies of the standardized data set based on time series analysis; triggering an alarm mechanism and analyzing the source of the anomaly in conjunction with historical data if the distribution of anomalies exceeds a preset threshold; classifying the anomalies using a support vector machine algorithm and generating adjustment strategies to optimize the configuration of operating parameters.

3. The method according to claim 1, characterized in that, S2 includes: performing median filtering on the initial multi-parameter dataset to remove noise outliers; filling in missing data through linear interpolation to generate a complete dataset; normalizing parameters of different dimensions using standard deviation to generate a standardized dataset; verifying data consistency through cluster analysis; and extracting time series change trends based on a sliding window.

4. The method according to claim 1, characterized in that, S3 includes: using the Pearson correlation coefficient method to calculate the linear relationship between parameters and constructing an initial model of coupling relationship; optimizing model parameters based on the significance of interaction effects and determining the specific form of coupling; quantifying the interaction effect weights using statistical tools and identifying key parameters whose influence weights exceed the threshold; and generating a parameter coupling relationship mapping table and visual analysis output.

5. The method according to claim 1, characterized in that, S4 includes: constructing a comprehensive dataset by fusing the parameter coupling feature matrix with historical operating data; capturing energy consumption time series characteristics using a long short-term memory network model; adjusting model parameters by combining multi-condition data to generate an energy consumption trend distribution related to the operating conditions; extracting energy consumption distribution features and verifying threshold compliance to generate an energy consumption distribution map.

6. The method according to claim 1, characterized in that, S5 includes: comparing the predicted energy consumption distribution features with the preset threshold and triggering an anomaly flag; generating a preliminary adjustment plan based on the rule base matching control parameter adjustment strategy; verifying the impact of the adjustment plan on the operating status through simulation; applying the verified control parameters and monitoring the optimization effect in real time, and iteratively correcting until the target is met.

7. The method according to claim 1, characterized in that, S6 includes: generating adjustment requirement information by comparing the configuration of operating parameters with preset thresholds; generating a priority control instruction set based on mapping rules and sending it to the device; detecting abnormal performance data of core components through a support vector machine algorithm; and dynamically updating the control instruction set based on the abnormality detection results.

8. The method according to claim 1, characterized in that, The S7 includes: real-time monitoring and data cleaning of the operating indicators of the core components of the equipment; establishing a pollution trend and performance degradation characteristic model through a time series regression model; marking high-risk state datasets in combination with threshold ranges; quantifying the probability distribution of decline risk and generating a maintenance priority ranking.

9. The method according to claim 1, characterized in that, S8 includes: classifying and grading performance risk indicators; triggering corresponding level warning signals according to risk levels; optimizing the load allocation scheme through linear regression algorithm; and performing load adjustment after verifying the stability of the scheme through multi-scenario simulation.

10. The method according to claim 1, characterized in that, S9 includes: filtering high-load nodes and generating a priority list according to the load allocation scheme; dynamically adjusting the operation strategy to reduce the operating intensity of high-load nodes; classifying equipment loss risk points through the support vector machine algorithm; and analyzing the model iteration direction based on historical optimization records and correcting feedback anomalies.

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