A digital twin control system for cooling tower circulating water

Abstract

The present invention relates to the field of digital twin technology, specifically a cooling tower circulating water digital twin control system, the system including a data collection module, a data processing module, an impact analysis module, a predictive maintenance module, a strategy construction module, and a monitoring and feedback module. In the present invention, accurate stress monitoring data is generated by real-time monitoring of wind speed, water flow velocity and temperature difference, and then the reliability and accuracy of the data are optimized through time series analysis and anomaly detection. Combined with three-dimensional modeling and coupling analysis, the system can more accurately evaluate the impact of wind load and water flow on the cooling tower structure, improve the system's responsiveness and prediction accuracy, and can adjust the water flow velocity and inlet water temperature in real time to optimize operating parameters, significantly improve energy efficiency and reduce energy consumption. Through continuous status monitoring and performance evaluation, the system further ensures the optimization of structural stability and operating efficiency.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a cooling tower circulating water digital twin control system. Background Art

[0002] The field of digital twin technology involves creating a virtual model of a physical entity to simulate and analyze its performance and behavior in real time. This technology leverages the Internet of Things, big data, machine learning, and simulation technologies to collect real-time data from devices and construct a digital replica that updates synchronously with the actual physical entity. Digital twins can be used for predictive maintenance, optimized operations, and improved product design, and are widely used in industries such as manufacturing, automotive, construction, and healthcare. Through digital twins, companies can test changes without impacting actual operations, predict equipment failures, and improve overall system efficiency and performance by simulating different operating scenarios.

[0003] The cooling tower circulating water digital twin control system utilizes digital twin technology to monitor and control the performance of the cooling tower's circulating water system. This system enables real-time monitoring, analysis, and optimization of the cooling tower's operating status. By simulating different operating conditions, it predicts and adjusts key parameters such as circulating water temperature and flow rate, improving energy efficiency and reducing energy consumption and maintenance costs. This control system is primarily intended to enhance the operational efficiency and reliability of industrial cooling systems, ensuring optimal cooling tower performance under varying environmental and load conditions.

[0004] Existing digital twin technology primarily focuses on the synchronization and simulation of real-time data, but lacks in-depth analysis and prediction of the long-term impacts on structural health. This makes it difficult for existing systems to adjust and optimize in a timely manner when faced with complex and changing environmental and load conditions, resulting in increased operation and maintenance costs and shortened equipment lifespan. For example, under varying environmental conditions, the failure to adjust operating parameters in real time can lead to inefficient cooling or overcooling, impacting the energy efficiency and safe operation of the entire system. Existing data analysis techniques fail to fully utilize collected multi-source data, resulting in deficiencies in decision support and fault prediction. This is particularly evident under high loads or extreme operating conditions, leading to premature system failure or operational malfunctions. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose a cooling tower circulating water digital twin control system.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: a cooling tower circulating water digital twin control system comprising:

[0007] The data collection module uses multiple sensors built into the cooling tower to monitor and record wind speed, water flow rate, and temperature difference data in real time, perform digital twinning and formatting, and generate real-time stress monitoring data;

[0008] The data processing module uses the real-time stress monitoring data to perform synchronous detection and identify abnormal data points, and performs time series analysis on normal data to obtain calibrated stress data;

[0009] The impact analysis module uses the calibrated stress data to perform three-dimensional modeling of the cooling tower, calculates the combined impact of wind load and water flow on the structure, performs stress and strain distribution calculations, and obtains coupling analysis results;

[0010] The predictive maintenance module performs failure mode analysis based on the coupling analysis results, identifies the trend of structural stress changes, evaluates the structural health of the cooling tower circulating water, and obtains a structural health prediction result;

[0011] The strategy building module uses the structural health prediction results to adjust the cooling water flow rate and inlet water temperature in real time. By simulating differentiated operating parameters and performing digital twin control, it obtains a control strategy adjustment plan.

[0012] The monitoring and feedback module implements the control strategy adjustment plan, continuously monitors the status of the cooling tower circulating water, analyzes the performance and structural stability, and evaluates the effect of the adjustment plan to generate health status monitoring results.

[0013] As a further solution of the present invention, the real-time stress monitoring data includes monitoring data of the internal pressure, vibration level and temperature gradient of the cooling tower, the calibrated stress data includes wind speed, water flow velocity and temperature difference data adjusted after time series analysis, the coupling analysis results include a quantitative assessment of the impact on the structural stability of the cooling tower and an analysis of the interaction of factors, the structural health prediction results include life expectancy prediction, fatigue damage assessment and failure probability calculation of key components, the control strategy adjustment scheme includes optimized setting of operating parameters, flow rate adjustment, temperature adjustment range and operating time window, and the health status monitoring results include real-time performance indicators, failure frequency and repair time statistics.

[0014] As a further solution of the present invention, the data collection module includes:

[0015] The data logging submodule monitors wind speed, water flow rate, and temperature difference data in real time from multiple sensors built into the cooling tower, smoothes wind speed fluctuations, and adjusts water flow rate to obtain a sensor data set;

[0016] The data integration submodule uses data twin technology to mirror the sensor data set, and simultaneously performs data formatting, transforms the unstructured data, and integrates the data to obtain a formatted data set;

[0017] The structural stress analysis submodule uses the formatted data set to perform stress distribution analysis on the structured data, predicts stress anomalies in key pressure-bearing parts based on the model, analyzes and calculates structural stress, and obtains real-time stress monitoring data.

[0018] As a further solution of the present invention, the data processing module includes:

[0019] The anomaly identification submodule uses the real-time stress monitoring data to identify abnormal peaks in the frequency of data points, determines data points that deviate from the normal range in combination with threshold settings, marks them as abnormal, and generates an abnormal data index;

[0020] The data screening submodule screens normal data based on the abnormal data index and performs trend analysis, identifies seasonal adjustment factors, and evaluates the impact of cyclical fluctuations to obtain time series analysis results;

[0021] The data adjustment submodule utilizes the time series analysis results to eliminate errors by smoothing the data, calibrate and optimize the consistency of the data, and obtain calibrated stress data.

[0022] As a further solution of the present invention, the impact analysis module includes:

[0023] The model building submodule optimizes the support structure and contact surface based on the calibrated stress data and the real-time structural size and shape of the cooling tower to generate a three-dimensional structural model;

[0024] The dynamic response analysis submodule simulates the force of wind load on the three-dimensional structural model through the three-dimensional structural model, and analyzes the influence of water flow on the structure in combination with the direction of water flow. It integrates wind load and water flow data to analyze the dynamic response of the cooling tower and obtains the combined load impact analysis results.

[0025] The stress-strain analysis submodule utilizes the combined load impact analysis results and adopts finite element analysis technology to mesh the three-dimensional model, calculate the stress response of each mesh, analyze the distribution of stress and strain, and obtain coupling analysis results.

[0026] As a further solution of the present invention, the formula of the finite element analysis technology is as follows:

[0027]

[0028] Among them, σ is the stress distribution value in the three-dimensional model, E represents the elastic modulus of the material, ν represents the proportional relationship between the lateral contraction and longitudinal elongation of the material under stress, ∈ x represents the strain along the x-axis, ∈ y represents the strain along the y-axis.

[0029] As a further solution of the present invention, the predictive maintenance module includes:

[0030] The mode analysis submodule identifies potential failure points of the cooling tower structure based on the coupling analysis results, determines the frequency and impact of the failure mode through data comparison and analysis, and generates failure mode identification results;

[0031] The trend identification submodule uses the failure mode identification results to track and evaluate the change trend of structural stress, calculate the speed and acceleration of stress change, and obtain stress change trend analysis results;

[0032] The structural integrity assessment submodule quantitatively assesses the integrity of the cooling tower circulating water structure based on the stress change trend analysis results, and predicts the service life in combination with environmental factors and operating conditions to obtain structural health prediction results.

[0033] As a further solution of the present invention, the strategy building module includes:

[0034] The parameter adjustment submodule adjusts the cooling water flow rate and inlet water temperature in real time based on the structural health prediction results, and adjusts the parameter settings by monitoring feedback data to match the cyclically changing environment and cooling tower conditions to obtain optimized parameter settings;

[0035] The scheme comparison submodule uses the optimized parameter settings to simulate differentiated operating parameters in a virtual environment, analyzes the impact of the differentiated parameter settings on cooling efficiency and structural safety, determines the optimal operating scheme through multi-scheme comparison, and generates operation simulation analysis results;

[0036] The virtual model synchronization submodule uses a genetic algorithm to perform real-time synchronization between the cooling tower and the virtual model through the operation simulation analysis results, executes real-time control strategy testing, and obtains a control strategy adjustment plan.

[0037] As a further solution of the present invention, the formula of the genetic algorithm is as follows:

[0038]

[0039] Where f is the performance optimization coefficient of the cooling tower, T i Represents the real-time temperature inside the cooling tower, T set Represents the set target temperature, C p represents specific heat capacity, P cool Represents cooling power, W rate stands for water flow rate and ABS stands for absolute value.

[0040] As a further solution of the present invention, the monitoring and feedback module includes:

[0041] The operating parameter application submodule adopts the control strategy adjustment scheme to adjust the flow rate and inlet water temperature of the cooling tower circulating water in actual operation. The new operating parameters are applied in real time through digital twin control to verify that the strategy is executed according to the predetermined goals and obtain the strategy implementation results.

[0042] Based on the results of the strategy implementation, the status analysis submodule continuously monitors the temperature, flow rate and key indicators of the cooling tower circulating water, analyzes the real-time status of the water cycle, identifies signs of deviation from normal operation, and generates water cycle monitoring data;

[0043] The effect evaluation submodule evaluates the performance and structural stability of the water circulation cooling through the water circulation monitoring data, analyzes the impact of the strategy adjustment on the operation efficiency and operation health, and obtains the health status monitoring results.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In this invention, advanced data collection and processing logic is used to conduct real-time monitoring and analysis, significantly improving the system's responsiveness and predictive accuracy. Real-time monitoring of wind speed, water flow rate, and temperature differentials generates precise stress monitoring data, which is then optimized through time series analysis and anomaly detection. Combined with three-dimensional modeling and coupling analysis, the system can more accurately assess the impact of wind loads and water flow on the cooling tower structure, improving the effectiveness of predictive maintenance. It can also adjust water flow rate and inlet water temperature in real time to optimize operating parameters, significantly improving energy efficiency and reducing energy consumption. Through continuous condition monitoring and performance evaluation, the system further ensures optimal structural stability and operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a system flow chart of the present invention;

[0047] Figure 2 Schematic diagram of the system framework of the present invention;

[0048] Figure 3 This is a flow chart of the data collection module of the present invention;

[0049] Figure 4 This is a flow chart of the data processing module of the present invention;

[0050] Figure 5 This is a flow chart of the impact analysis module of the present invention;

[0051] Figure 6 This is a flow chart of the predictive maintenance module of the present invention;

[0052] Figure 7 Flowchart of the strategy building blocks of the present invention;

[0053] Figure 8 This is a flow chart of the monitoring and feedback module of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0056] See also Figures 1 to 2 , a cooling tower circulating water digital twin control system includes:

[0057] The data collection module uses multiple sensors built into the cooling tower to monitor and record wind speed, water flow rate, and temperature difference data in real time, perform digital twinning, data verification and formatting, and perform signal filtering to eliminate noise, generating real-time stress monitoring data.

[0058] The data processing module uses real-time stress monitoring data to perform synchronous detection and identify abnormal data points, and conducts time series analysis on normal data to obtain calibrated stress data;

[0059] The impact analysis module uses calibrated stress data to perform 3D modeling of the cooling tower, calculates the combined effects of wind load and water flow on the structure, records the structural response through iterative solution, performs stress and strain distribution calculations, and obtains coupled analysis results;

[0060] The predictive maintenance module performs failure mode analysis based on the coupling analysis results, identifies the changing trend of structural stress, evaluates the structural health of the cooling tower circulating water, and obtains structural health prediction results;

[0061] The strategy building module uses the structural health prediction results to make real-time adjustments to the cooling water flow rate and inlet water temperature. By simulating differentiated operating parameters, it quantifies the impact on the structure and performs digital twin control to obtain a control strategy adjustment plan.

[0062] The monitoring and feedback module implements the control strategy adjustment plan, continuously monitors the status of the cooling tower circulating water, analyzes performance and structural stability through real-time data, and evaluates the effectiveness of the adjustment plan to generate health status monitoring results.

[0063] Real-time stress monitoring data includes monitoring data on the internal pressure, vibration level and temperature gradient of the cooling tower. The calibrated stress data includes wind speed, water flow velocity and temperature difference data adjusted through time series analysis. The coupling analysis results include quantitative evaluation of the impact on the stability of the cooling tower structure and analysis of factor interactions. The structural health prediction results include life expectancy prediction, fatigue damage assessment and failure probability calculation of key components. The control strategy adjustment plan includes the optimization setting of operating parameters, flow rate adjustment, temperature adjustment range and operating time window. The health status monitoring results include real-time performance indicators, failure frequency and repair time statistics.

[0064] See also Figure 2 、 3 , the data collection module includes:

[0065] The data logging submodule monitors wind speed, water flow rate, and temperature difference data from various sensors built into the cooling tower in real time, smoothes wind speed fluctuations, and adjusts water flow rate to obtain the sensor data set. The execution process is as follows:

[0066] Monitor the internal environment of the cooling tower, collect wind speed, water flow rate and temperature difference data in real time, collect data based on multiple sensors, smooth out wind speed fluctuations, and adjust water flow rate to optimize performance. For each monitored parameter, such as wind speed, water flow rate, and temperature difference, perform real-time data processing, use smoothing algorithms to reduce random fluctuations in data, improve data stability and reliability, and adjust water flow rate to cope with different working environments to ensure cooling efficiency. Through a series of processing, a sensor data set is obtained.

[0067] The data integration submodule uses data twin technology to mirror the data by sensing the data set, and at the same time performs data formatting, transforms the unstructured data, and integrates the data to obtain the formatted data set. The execution process is as follows;

[0068] Responsible for processing the collected sensor data sets, using data twin technology to create a digital mirror of the data, performing data replication, enhancing data reliability and accessibility, performing data formatting, and converting the collected unstructured data into a structured format to facilitate further data analysis and integration. In this way, the data is reorganized and optimized, enhancing the efficiency and accuracy of data processing, and a formatted data set is obtained after integration.

[0069] The structural stress analysis submodule uses formatted data sets to perform stress distribution analysis on structured data, predicts stress anomalies in key pressure-bearing parts based on the model, analyzes and calculates structural stress, and obtains real-time stress monitoring data. The execution process is as follows;

[0070] Use the formatted data set to perform stress distribution analysis, according to the formula Calculate the stress anomaly of the key pressure-bearing parts. a Represents stress, F a Represents the force, A a Represents the stress area. Detailed explanation of the formula and the calculation process of the formula: Considering the stress σ a is the force F a and the force area A a The ratio of the force F a The structural response data obtained by sensor monitoring can be calculated, and the force area A a is a known structural parameter. For example, if F is measured under experimental conditions a =500 Newtons and A a =0.1 square meters, then the stress σ a Can be achieved through Such calculations not only provide a quantitative description of the stress state in the pressure-bearing parts of the structure, but also help predict stress anomalies so that appropriate maintenance measures can be taken.

[0071] See also Figure 2 、 4 , the data processing module includes:

[0072] The anomaly identification submodule uses real-time stress monitoring data to identify abnormal peaks in the frequency of data points. It then uses threshold settings to determine data points that deviate from the normal range, mark them as abnormal, and generate an abnormal data index. The execution process is as follows:

[0073] Using real-time stress monitoring data, by identifying abnormal peaks in the frequency of data points, according to the formula Calculate the data points that deviate from the normal range. b represents frequency, T b Represents the time interval. Formula explanation and formula calculation derivation process: Abnormal frequency F b The data point time interval T b For example, if the interval between two consecutive data points is T b The measured value is 0.02 seconds, so the abnormal frequency F b Can be achieved through Calculation. Frequencies above the normal operating frequency, such as 10 Hz, are flagged as anomalies. The formula helps identify and flag frequency anomalies in the data for further processing.

[0074] The data screening submodule filters normal data based on abnormal data indexes and performs trend analysis, identifies seasonal adjustment factors, and evaluates the impact of cyclical fluctuations to obtain time series analysis results. The execution process is as follows;

[0075] Based on the abnormal data index, data points marked as abnormal are eliminated, and then trend analysis is performed on the remaining normal data to identify the seasonal adjustment factors in the data. The adjustment factors reflect the specific patterns of data changes with seasons, evaluate how seasonal factors affect the cyclical fluctuations of the data, and reflect the true trend and cyclical changes of the data after considering seasonal adjustments, provide support for subsequent decision-making, and obtain time series analysis results.

[0076] The data adjustment submodule uses the time series analysis results to eliminate errors by smoothing the data, calibrating and optimizing the consistency of the data to obtain the calibrated stress data. The execution process is as follows:

[0077] Based on the results of time series analysis, random errors in the recording process are eliminated through data smoothing. The processing method reduces data noise by applying moving average or smoothing technology, and then calibrates the data, improves data consistency, and more accurately reflects the actual stress state. It optimizes the quality and availability of the data, making it more suitable for further analysis and application, and obtains calibrated stress data.

[0078] See also Figure 2 、 5 , the impact analysis module includes:

[0079] The model building submodule optimizes the support structure and contact surface based on the calibrated stress data and the real-time structural size and shape of the cooling tower to generate a three-dimensional structural model. The execution process is as follows:

[0080] Based on the calibrated stress data, the real-time structural size and shape of the cooling tower are taken into consideration to optimize the structure, and the supporting structure and contact surface are improved. Guided by data, the process involves comprehensive consideration of various structural parameters and actual working conditions to ensure that the model can truly reflect the physical properties and working environment of the structure, improve the application value and engineering applicability of the model, and generate a three-dimensional structural model.

[0081] The dynamic response analysis submodule simulates the force exerted by wind loads on the 3D structural model. It also analyzes the effect of water flow on the structure based on the direction of water flow. It integrates wind load and water flow data to analyze the dynamic response of the cooling tower. The execution process for obtaining the combined load impact analysis results is as follows:

[0082] The three-dimensional structural model is used to simulate the force of wind load on the model, and the influence of water flow on the structure is analyzed. According to the formula F a=ma, calculate the dynamic response of the cooling tower. a Represents the force, m represents the mass, and a represents the acceleration. Detailed explanation of the formula and the calculation process of the formula: In actual operation, the force F a The dynamic effects of wind loads and water flow on the structure can be quantified. For example, if the mass m of the 3D model is 5000 kg and the average acceleration a obtained from the wind load and water flow data analysis is 0.5 m / s2, then the force F a F a =5000×0.5=2500 Newtons. In this way, the maximum and minimum forces on the cooling tower under different environmental conditions can be calculated to evaluate the stability and safety of the structure.

[0083] The stress-strain analysis submodule uses the combined load impact analysis results and finite element analysis technology to mesh the three-dimensional model, calculate the stress response of each mesh, analyze the distribution of stress and strain, and obtain the coupled analysis results. The execution process is as follows;

[0084] The formula for the finite element analysis technique is as follows:

[0085]

[0086] Among them, σ is the stress distribution value in the three-dimensional model, E represents the elastic modulus of the material, ν represents the proportional relationship between the lateral contraction and longitudinal elongation of the material under stress, ∈ x represents the strain along the x-axis, ∈ y represents the strain along the y-axis.

[0087] formula:

[0088]

[0089] Among them, the determination of each parameter is as follows:

[0090] E (elastic modulus): obtained through standard material testing, for example, steel is approximately 210 GPa.

[0091] ν (Poisson's ratio): obtained through material testing. For most metal materials, the Poisson's ratio is between 0.25 and 0.35.

[0092] ∈ x (strain along the x-axis) and ∈ y (Strain along the y-axis): In finite element analysis, the strain value is calculated from the node displacement results.

[0093] The parameters of the steel are set as follows: E = 210 GPa, ν = 0.3, ∈ x = 0.001 (tensile strain of 1 thousandth), ∈ y=-0.0003 (compressive strain along the y-axis due to Poisson effect).

[0094] Substitute into the formula for calculation:

[0095]

[0096] The results show that, given the material properties and strain conditions, the calculated stress of 210 MPa indicates that the material is in a safe working state under this loading condition, as the yield strength of most steel materials is much higher than this value. This stress level can help engineers determine the safety and potential risk points of the structure, providing a basis for structural design and optimization.

[0097] See also Figure 2 、 6 , the predictive maintenance module includes:

[0098] The mode analysis submodule identifies potential failure points of the cooling tower structure based on the coupling analysis results. Through data comparison and analysis, the frequency and impact of the failure mode are determined. The execution process of generating the failure mode identification results is as follows;

[0099] The potential failure points of the cooling tower structure are identified by coupling analysis results. According to the formula Calculate the frequency of the failure mode. Where, f b Represents the frequency of the failure mode, k represents the structural stiffness, and m represents the mass. Detailed explanation of the formula and the formula calculation process: The formula is based on vibration analysis and relates the failure frequency of the structure to the stiffness and mass of the structure. Assuming the stiffness of the cooling tower failure point k = 10,000 Newtons / meter and the mass m = 5,000 kilograms, the failure mode frequency f b Can be achieved through Calculated in Hertz. The frequency indicates the natural vibration frequency of the failure mode under normal conditions. By comparing the frequency with the monitored frequency in actual operation, the impact and characteristics of the failure mode can be further confirmed.

[0100] The trend identification submodule uses the failure mode identification results to track and evaluate the change trend of structural stress, calculate the speed and acceleration of stress change, and obtain the stress change trend analysis results. The execution process is as follows;

[0101] By using the failure mode identification results, tracking and evaluating the structural stress data, the speed and acceleration of stress change are calculated. In particular, by comparing the stress change values ​​in different time periods, the rate of stress change is obtained. Based on the trend of stress change over time, the acceleration is further calculated to identify the trend of rapid stress changes in the short term or gradual accumulation in the long term. The dynamic characteristics of the pressure changes that the cooling tower structure is subjected to during operation are revealed, providing a reliable basis for further predicting the long-term health status of the structure and obtaining the stress change trend analysis results.

[0102] The structural integrity assessment submodule quantitatively assesses the integrity of the cooling tower's circulating water structure based on the stress change trend analysis results. It also predicts the service life based on environmental factors and operating conditions. The execution process for obtaining the structural health prediction results is as follows:

[0103] Based on the stress change trend analysis results, combined with the operating conditions and environmental factors of the cooling tower, the overall stress distribution of the circulating water system of the structure is quantitatively evaluated. The stress peak and fatigue limit of the key components of the cooling tower under different operating conditions are calculated. Based on the stress change data, the future change trend is predicted and the service life of the cooling tower is estimated. The structural health prediction results are obtained through analysis.

[0104] See also Figure 2 、 7 , the strategy building blocks include:

[0105] The parameter adjustment submodule adjusts the cooling water flow rate and inlet water temperature in real time based on the structural health prediction results. It tunes the parameter settings by monitoring feedback data to match the cyclically changing environment and cooling tower conditions. The execution process of the optimized parameter settings is as follows;

[0106] Based on the structural health prediction results, the cooling water flow rate and inlet water temperature are adjusted in real time, monitored through feedback data, and parameter settings are adjusted according to the monitoring data to better adapt to the current environmental conditions and the operating status of the cooling tower. During the real-time adjustment process, the system will use the intelligent control mechanism to continuously optimize the parameter settings based on different feedback indicators, such as changes in water flow rate and temperature increases or decreases, to ensure maximum cooling efficiency and structural stability, and obtain optimized parameter settings.

[0107] The scheme comparison submodule uses optimized parameter settings to simulate differentiated operating parameters in a virtual environment, analyzes the impact of differentiated parameter settings on cooling efficiency and structural safety, and determines the optimal operating scheme through multi-scheme comparison. The execution process of generating operation simulation analysis results is as follows;

[0108] By utilizing optimized parameter settings, different operating parameter combinations are tested in a virtual simulation environment, focusing on analyzing the impact of each operating parameter on cooling efficiency and structural safety. Each set of differentiated parameters such as flow rate and temperature changes will affect the overall performance of the cooling tower. Through the simulation results obtained, the advantages and disadvantages of each scheme are compared, especially under extreme conditions, to analyze whether the adjustment of parameters will affect the long-term stability of the structure or lead to a decrease in cooling efficiency. After comparing a series of schemes, the optimal operating scheme is determined and the operation simulation analysis results are generated.

[0109] The virtual model synchronization submodule uses genetic algorithms to perform real-time synchronization between the cooling tower and the virtual model through the operation simulation analysis results, performs real-time control strategy testing, and obtains the execution process of the control strategy adjustment plan as follows;

[0110] The formula of the genetic algorithm is as follows:

[0111]

[0112] Where f is the performance optimization coefficient of the cooling tower, T i Represents the real-time temperature inside the cooling tower, T set Represents the set target temperature, C p represents specific heat capacity, P cool Represents cooling power, W rate stands for water flow rate and ABS stands for absolute value.

[0113] formula:

[0114]

[0115] The various parameters in the formula are obtained through precise measurement and data collection, as follows:

[0116] T i (Current Internal Temperature): Use the temperature sensor to monitor the temperature inside the cooling tower in real time. Set the temperature measured at a certain point in time to 35°C.

[0117] T set (Target set temperature): The temperature set according to cooling demand, set the set temperature to 30℃.

[0118] C p (Specific heat capacity of water): The standard value is 4.186 kJ / kg / K, which is obtained through the Material Properties Handbook.

[0119] P cool (Cooling power): The power consumption of the cooling system is measured through the energy consumption monitoring system, and the current power is set to 5kW.

[0120] W rate(Water flow rate): Measure the water flow rate using a flow meter and set the flow rate to 0.05m 3 / s.

[0121] Substituting specific values, the calculation is as follows:

[0122] 1. Calculate the absolute value of the temperature difference and find the square root:

[0123]

[0124] 2. Calculate the numerator:

[0125]

[0126] 3. Calculate the denominator:

[0127] P cool W rate =5·0.05=0.25kW / s

[0128] 4. Divide the numerator by the denominator to get the optimized value:

[0129]

[0130] The calculated results represent the cooling tower's optimized performance coefficient for the given setup and environmental conditions. Numerical results show that, based on the current control parameters, an energy conversion efficiency of 37.452 kJ / K can be achieved for every 1 kW of cooling power consumed. This demonstrates that the adjusted control strategy can achieve more efficient energy utilization, playing a key role in cooling tower performance management and optimization.

[0131] See also Figure 2 、 8 , the monitoring and feedback modules include:

[0132] The operating parameter application submodule uses a control strategy adjustment scheme to adjust the cooling tower circulating water flow rate and inlet water temperature in actual operation. The new operating parameters are applied in real time through digital twin control to verify that the strategy is executed according to the predetermined goals. The execution process of the strategy implementation results is as follows;

[0133] Based on the control strategy adjustment plan, in actual operation, the circulating water flow rate and inlet water temperature of the cooling tower are adjusted through the automation system, and the new operating parameters are monitored and fed back in real time using digital twin technology. Digital twin technology can replicate and analyze the operating status of the cooling tower in real time to ensure that the new parameter settings match the actual operating conditions. The control strategy will verify whether it is executed according to the predetermined goals based on the feedback data, and make immediate adjustments based on the difference between the goals and actual operations to obtain the results of the strategy implementation.

[0134] Based on the results of the strategy implementation, the status analysis submodule continuously monitors the temperature, flow rate, and key indicators of the cooling tower circulating water, analyzes the real-time status of the water cycle, identifies signs of deviation from normal operation, and generates water cycle monitoring data. The execution process is as follows;

[0135] Based on the results of strategy implementation, the temperature, flow rate and key operating indicators of the cooling tower circulating water are continuously monitored, and the real-time status of the system is analyzed, with a focus on detecting fluctuations in temperature and flow rate during the water circulation process, as well as potential abnormal signs. By comparing operating data and real-time monitoring data, signals that deviate from normal operating conditions are identified to ensure that the cooling tower can be adjusted in time to cope with changes in operation, and to determine whether the current operating status meets the expected operating standards, and to generate water circulation monitoring data.

[0136] The effect evaluation submodule uses water cycle monitoring data to evaluate the performance and structural stability of water cycle cooling, analyze the impact of strategy adjustments on operational efficiency and operational health, and obtain health status monitoring results. The execution process is as follows:

[0137] Through water circulation monitoring data, the performance of water circulation cooling and the stability of the cooling tower structure are evaluated, focusing on analyzing the impact of current strategy adjustments on system operating efficiency, especially the long-term impact on cooling effect and equipment health. By evaluating key indicators such as stress distribution and temperature changes, it is confirmed whether there are potential hidden dangers. Combined with the analysis results, the health status monitoring data of the cooling tower is obtained to ensure that the system can maintain long-term stable operation and achieve optimal performance status, and obtain health status monitoring results.

[0138] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A cooling tower circulating water digital twin control system, characterized in that: The system comprises: The data collection module uses multiple sensors built into the cooling tower to monitor and record wind speed, water flow rate, and temperature difference data in real time, perform digital twinning and formatting, and generate real-time stress monitoring data; The data processing module uses the real-time stress monitoring data to perform synchronous detection and identify abnormal data points, and performs time series analysis on normal data to obtain calibrated stress data; The impact analysis module uses the calibrated stress data to perform three-dimensional modeling of the cooling tower, calculates the combined impact of wind load and water flow on the structure, performs stress and strain distribution calculations, and obtains coupling analysis results; The predictive maintenance module performs failure mode analysis based on the coupling analysis results, identifies the trend of structural stress changes, evaluates the structural health of the cooling tower circulating water, and obtains a structural health prediction result; The strategy building module uses the structural health prediction results to adjust the cooling water flow rate and inlet water temperature in real time. By simulating differentiated operating parameters and performing digital twin control, it obtains a control strategy adjustment plan. The monitoring and feedback module implements the control strategy adjustment plan, continuously monitors the status of the cooling tower circulating water, analyzes the performance and structural stability, and evaluates the effect of the adjustment plan to generate health status monitoring results; The strategy building module includes: The parameter adjustment submodule adjusts the cooling water flow rate and inlet water temperature in real time based on the structural health prediction results, and adjusts the parameter settings by monitoring feedback data to match the cyclically changing environment and cooling tower conditions to obtain optimized parameter settings; The scheme comparison submodule uses the optimized parameter settings to simulate differentiated operating parameters in a virtual environment, analyzes the impact of the differentiated parameter settings on cooling efficiency and structural safety, determines the optimal operating scheme through multi-scheme comparison, and generates operation simulation analysis results; The virtual model synchronization submodule uses a genetic algorithm to perform real-time synchronization between the cooling tower and the virtual model through the operation simulation analysis results, executes real-time control strategy testing, and obtains a control strategy adjustment plan.

2. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The real-time stress monitoring data includes monitoring data of the cooling tower's internal pressure, vibration level, and temperature gradient; the calibrated stress data includes wind speed, water flow velocity, and temperature difference data adjusted through time series analysis; the coupling analysis results include a quantitative assessment of the impact on the cooling tower's structural stability and an analysis of factor interactions; the structural health prediction results include life expectancy prediction, fatigue damage assessment, and failure probability calculation of key components; the control strategy adjustment plan includes optimized settings of operating parameters, flow rate adjustment, temperature adjustment range, and operating time window; and the health status monitoring results include real-time performance indicators, failure frequency, and statistical data on repair time.

3. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The data collection module includes: The data logging submodule monitors wind speed, water flow rate, and temperature difference data in real time from multiple sensors built into the cooling tower, smoothes wind speed fluctuations, and adjusts water flow rate to obtain a sensor data set; The data integration submodule uses data twin technology to mirror the sensor data set, and simultaneously performs data formatting, transforms the unstructured data, and integrates the data to obtain a formatted data set; The structural stress analysis submodule uses the formatted data set to perform stress distribution analysis on the structured data, predicts stress anomalies in key pressure-bearing parts based on the model, analyzes and calculates structural stress, and obtains real-time stress monitoring data.

4. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The data processing module includes: The anomaly identification submodule uses the real-time stress monitoring data to identify abnormal peaks in the frequency of data points, determines data points that deviate from the normal range in combination with threshold settings, marks them as abnormal, and generates an abnormal data index; The data screening submodule screens normal data based on the abnormal data index and performs trend analysis, identifies seasonal adjustment factors, and evaluates the impact of cyclical fluctuations to obtain time series analysis results; The data adjustment submodule utilizes the time series analysis results to eliminate errors by smoothing the data, calibrate and optimize the consistency of the data, and obtain calibrated stress data.

5. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The impact analysis module includes: The model building submodule optimizes the support structure and contact surface based on the calibrated stress data and the real-time structural size and shape of the cooling tower to generate a three-dimensional structural model; The dynamic response analysis submodule simulates the force of wind load on the three-dimensional structural model through the three-dimensional structural model, and analyzes the influence of water flow on the structure in combination with the direction of water flow. It integrates wind load and water flow data to analyze the dynamic response of the cooling tower and obtains the combined load impact analysis results. The stress-strain analysis submodule utilizes the combined load impact analysis results and adopts finite element analysis technology to mesh the three-dimensional model, calculate the stress response of each mesh, analyze the distribution of stress and strain, and obtain coupling analysis results.

6. The cooling tower circulating water digital twin control system according to claim 5, characterized in that: The formula for the finite element analysis technique is as follows: ； in, is the stress distribution value in the three-dimensional model, represents the elastic modulus of the material, Represents the proportional relationship between the lateral contraction and longitudinal extension of the material when subjected to stress. Representatives along The strain of the axis, Representatives along Strain of the axis.

7. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The predictive maintenance module includes: The mode analysis submodule identifies potential failure points of the cooling tower structure based on the coupling analysis results, determines the frequency and impact of the failure mode through data comparison and analysis, and generates failure mode identification results; The trend identification submodule uses the failure mode identification results to track and evaluate the change trend of structural stress, calculate the speed and acceleration of stress change, and obtain stress change trend analysis results; The structural integrity assessment submodule quantitatively assesses the integrity of the cooling tower circulating water structure based on the stress change trend analysis results, and predicts the service life in combination with environmental factors and operating conditions to obtain structural health prediction results.

8. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The formula of the genetic algorithm is as follows: ； in, is the performance optimization coefficient of the cooling tower, Represents the real-time temperature inside the cooling tower. Represents the set target temperature, represents the specific heat capacity, Represents the cooling power, represents the water flow rate, Represents absolute value.

9. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The monitoring and feedback module includes: The operating parameter application submodule adopts the control strategy adjustment scheme to adjust the flow rate and inlet water temperature of the cooling tower circulating water in actual operation. The new operating parameters are applied in real time through digital twin control to verify that the strategy is executed according to the predetermined goals and obtain the strategy implementation results. Based on the results of the strategy implementation, the status analysis submodule continuously monitors the temperature, flow rate and key indicators of the cooling tower circulating water, analyzes the real-time status of the water cycle, identifies signs of deviation from normal operation, and generates water cycle monitoring data; The effect evaluation submodule evaluates the performance and structural stability of the water circulation cooling through the water circulation monitoring data, analyzes the impact of the strategy adjustment on the operation efficiency and operation health, and obtains the health status monitoring results.

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