Efficient machine room water pump frequency conversion control system and method

Abstract

The invention relates to the field of machine room water pump control methods, in particular to an efficient machine room water pump frequency conversion control system and method. Modules of intelligent prediction and optimization, dynamic pressure and flow control, multi-sensor fusion, fault prediction and adaptive control, energy management and optimization and the like are integrated; and prospective and self-adaptive operation of the cooling system is realized. The system not only can predict the cooling demand according to historical data and real-time environmental parameters and adjust the working state in advance, but also can monitor and accurately control the pressure and flow of the cooling water system in real time and ensure the cooling efficiency. Through multi-sensor fusion, the system comprehensively monitors the operation state, and in combination with fault prediction and adaptive control, potential faults can be prevented, and the system stability is guaranteed. The energy management and optimization module intelligently dispatches operation of a cooling water tower fan and a cooling water pump, efficient utilization of energy is achieved, and the energy efficiency and reliability of the cooling system are remarkably improved on the whole.

Description

Technical Field

[0001] The present application relates to the field of computer room water pump control methods, and in particular to a high-efficiency computer room water pump variable frequency control system and method. Background Art

[0002] In the cooling system of a data center or large computer room, cooling water pumps and cooling water tower fans are key cooling components. The cooling water pump is responsible for circulating cooling water to the cooling tower, while the cooling water tower fan reduces the temperature of the cooling water through the evaporative cooling principle.

[0003] Traditional control methods often control these two components independently, resulting in low energy efficiency and unstable cooling effect. How to achieve efficient coordination between the two under variable frequency control remains to be solved. Summary of the invention

[0004] In order to improve the energy utilization efficiency of the cooling system, reduce operating costs, and ensure the stability and reliability of the cooling effect, the present application provides a high-efficiency machine room water pump variable frequency control system and method.

[0005] In a first aspect, the present application provides a high-efficiency machine room water pump variable frequency control system, comprising: Intelligent prediction and optimization module predicts the cooling demand of the computer room based on historical data, adjusts the working status of the cooling system in advance, and predicts the cooling demand in the future by analyzing parameters such as the heat load of the computer room, external ambient temperature and humidity, thereby guiding the inverter to adjust the frequency in advance; Dynamic pressure and flow control module monitors the pressure and flow in the cooling water system in real time, dynamically adjusts according to system requirements, and automatically adjusts the speed of the water pump according to the operating status of the cooling tower fan and the cooling water demand to achieve precise control of pressure and flow; Multi-sensor fusion module combines temperature, humidity, pressure, flow and other sensors to comprehensively monitor the operating status of the cooling system; Fault prediction and adaptive control module, which predicts potential system failures based on historical fault data and takes preventive measures in advance; The energy management and optimization module optimizes energy distribution by analyzing the energy consumption data of the cooling system and intelligently schedules the operation of cooling tower fans and cooling water pumps.

[0006] In a second aspect, a high-efficiency machine room water pump variable frequency control method is implemented using the above-mentioned high-efficiency machine room water pump variable frequency control system, comprising the following steps: S1: Predict cooling demand and learn the periodic change patterns of the environment inside and outside the computer room, including the temperature difference between day and night and seasonal temperature fluctuations; S2: Through intelligent hydraulic balancing technology, the pump speed is adjusted and the valve opening in the cooling water system is automatically adjusted to ensure the hydraulic balance of the entire system and reduce local overheating or insufficient cooling; S3: The multi-sensor fusion module introduces anomaly detection algorithms that can identify abnormal conditions such as temperature, humidity, and pressure that are beyond the normal range and provide immediate warnings; S4: When the system detects that the water pump motor is overheated, it reduces the operating frequency and automatically adjusts the circulation path of the cooling water to ensure the continuous operation of the cooling system through the backup cooling equipment; S5: Convert waste heat generated during the cooling process into reusable energy for heating the computer room or supplying hot water; S6: Through the integrated intelligent maintenance module, the maintenance needs of the equipment are predicted and the maintenance plan is automatically planned based on the equipment operation data and historical maintenance records.

[0007] Optionally, the predicting of cooling demand and learning of periodic variation patterns of the environment inside and outside the computer room, including day and night temperature differences and seasonal temperature fluctuations, comprises the following steps: S11: The system collects environmental parameters inside and outside the computer room through an integrated sensor network, including operating status sensors of cooling tower fans and cooling water pumps, and cleans and pre-processes the data; S12: Use machine learning algorithms to identify the correlation between the operation modes of cooling tower fans and cooling water pumps and environmental parameters, and learn the periodic change pattern of cooling demand; S13: Based on the learned patterns, a prediction model is constructed to predict the coordinated operation requirements of cooling tower fans and cooling water pumps, providing forward-looking guidance for system adjustment; S14: Based on the prediction results, the system automatically generates a coordinated control strategy for the cooling tower fan and the cooling water pump, including pre-adjustment of the inverter frequency.

[0008] Optionally, the method of adjusting the pump speed and automatically adjusting the valve opening in the cooling water system through intelligent hydraulic balancing technology to ensure the hydraulic balance of the entire system and reduce local overheating or insufficient cooling includes the following steps: S21: Monitor the pressure, flow and temperature of the cooling water system in real time, and pay attention to the operating status of the cooling tower fan and cooling water pump; S22: Analyze the operating data of cooling tower fans and cooling water pumps through intelligent algorithms to identify areas of hydraulic imbalance, including local overpressure and underpressure; S23: Automatically adjust the valve opening in the cooling water system to achieve hydraulic balance; S24: Evaluate the effect of hydraulic balance adjustment and optimize the control strategy of cooling tower fan and cooling water pump based on the evaluation results.

[0009] Optionally, the multi-sensor fusion module introduces an anomaly detection algorithm, which can identify abnormal conditions such as temperature, humidity, pressure, etc. that are beyond the normal range and issue an immediate warning, including the following steps: S31: Fusing sensor data from cooling tower fans and cooling water pumps; S32: using an anomaly detection algorithm to identify abnormalities in the operating status of the cooling tower fan and the cooling water pump; S33: Once an abnormality is detected, the early warning mechanism is immediately activated to notify the system administrator and automatically adjust the operating parameters of the cooling tower fan and cooling water pump; S34: According to the warning type, the system automatically executes the coordinated control strategy of the cooling tower fan and the cooling water pump, including increasing the operating frequency of the cooling tower fan and quickly responding to abnormal situations.

[0010] Optionally, when the system detects that the water pump motor is overheated, the operating frequency is reduced, the circulation path of the cooling water is automatically adjusted, and the continuous operation of the cooling system is ensured through the backup cooling equipment, including the following steps: S41: Collect historical fault data of cooling tower fans and cooling water pumps, including fault type, occurrence time and impact range; S42: Identify common failure modes and potential causes of cooling tower fans and cooling water pumps through data analysis; S43: Utilize a self-learning algorithm to optimize the fault recovery strategy of cooling tower fans and cooling water pumps based on historical data; S44: When a fault is detected, the optimized recovery strategy is automatically executed, and the coordinated control strategy of the cooling tower fan and the cooling water pump is further optimized according to the execution result.

[0011] Optionally, the step of converting waste heat generated during the cooling process into reusable energy for heating the machine room or supplying hot water comprises the following steps: S51: Real-time monitoring of energy consumption of cooling tower fans and cooling water pumps, analysis of energy efficiency and potential recovery opportunities; S52: Integrate energy recovery technologies such as heat recovery to convert waste heat generated during the operation of cooling tower fans and cooling water pumps into reusable energy; S53: Plan energy reuse paths, including heating or hot water supply for computer rooms, to achieve energy recycling; S54: According to the energy recovery and reuse situation, intelligently schedule the operation of cooling tower fans and cooling water pumps to achieve low energy consumption.

[0012] Optionally, the integrated intelligent maintenance module predicts the maintenance needs of the equipment according to the equipment operation data and historical maintenance records, and automatically plans the maintenance plan, including the following steps: S61: Real-time monitoring of the operating status of the cooling tower fan and cooling water pump, including temperature, vibration, current and other parameters; S62: Use historical maintenance records and equipment operation data to build a predictive maintenance model to predict the maintenance needs of cooling tower fans and cooling water pumps; S63: Based on the prediction results, the system automatically plans the maintenance plan for the cooling tower fan and cooling water pump, including the maintenance time and maintenance content; S64: Evaluate the effectiveness of the maintenance plan and optimize the maintenance strategy for the cooling tower fan and cooling water pump based on the evaluation results.

[0013] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a high-efficiency frequency conversion control method for a machine room water pump as described in any one of the above items are implemented.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a high-efficiency variable frequency control method for a machine room water pump as described in any one of the above items are implemented.

[0015] In summary, this application includes the following beneficial technical effects: 1. The system can adjust the working status of the cooling system in advance through the intelligent prediction and optimization module, avoiding unnecessary energy waste. The dynamic pressure and flow control module ensures the efficient operation of the cooling water system, reduces overcooling or insufficient cooling, and thus reduces energy consumption and operating costs. The energy management and optimization module further realizes refined energy management through intelligent scheduling, ensuring efficient use of energy; 2. The introduction of the multi-sensor fusion module enables the system to comprehensively monitor the operating status of the cooling system and detect potential problems in a timely manner. The fault prediction and adaptive control module can predict and prevent potential system failures based on historical failure data, take preventive measures in advance, significantly improve the stability and reliability of the system, reduce downtime caused by failures, and ensure the normal operation of the computer room; 3. The design of the entire system emphasizes intelligence and automation. Through the integration of multiple modules, adaptive control and intelligent management of the cooling system are achieved, which not only reduces the workload of manual monitoring and maintenance, but also improves the system's response speed and control accuracy through data-driven decision-making, allowing the cooling system to respond more flexibly to changes in the environment inside and outside the computer room, thereby improving the overall level of intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a principle block diagram of a high-efficiency machine room water pump frequency conversion control system in an embodiment of the present application.

[0017] Figure 2 It is a flow chart of a high-efficiency machine room water pump variable frequency control method in an embodiment of the present application.

[0018] Figure 3 It is a flowchart of the sub-steps of step S1 in the embodiment of the present application.

[0019] Figure 4 It is a flowchart of the sub-steps of step S2 in the embodiment of the present application.

[0020] Figure 5 It is a flowchart of the sub-steps of step S3 in the embodiment of the present application.

[0021] Figure 6 It is a flowchart of the sub-steps of step S4 in the embodiment of the present application.

[0022] Figure 7 It is a flowchart of the sub-steps of step S5 in the embodiment of the present application.

[0023] Figure 8 It is a flowchart of the sub-steps of step S6 in the embodiment of the present application. DETAILED DESCRIPTION

[0024] The following is combined with Figure 1-Figure 8 This application is described in further detail.

[0025] Reference Figure 1 The embodiment of the present application discloses a high-efficiency machine room water pump variable frequency control system, including the following modules: Intelligent prediction and optimization module predicts the cooling demand of the computer room based on historical data, adjusts the working status of the cooling system in advance, and predicts the cooling demand in the future by analyzing parameters such as the heat load of the computer room, external ambient temperature and humidity, thereby guiding the inverter to adjust the frequency in advance; Dynamic pressure and flow control module monitors the pressure and flow in the cooling water system in real time, dynamically adjusts according to system requirements, and automatically adjusts the speed of the water pump according to the operating status of the cooling tower fan and the cooling water demand to achieve precise control of pressure and flow; Multi-sensor fusion module combines temperature, humidity, pressure, flow and other sensors to comprehensively monitor the operating status of the cooling system; Fault prediction and adaptive control module, which predicts potential system failures based on historical fault data and takes preventive measures in advance; The energy management and optimization module optimizes energy distribution by analyzing the energy consumption data of the cooling system and intelligently schedules the operation of cooling tower fans and cooling water pumps.

[0026] Among them, the intelligent prediction and optimization module is the core, guiding the inverter to adjust the frequency according to the predicted cooling demand; the dynamic pressure and flow control module monitors and adjusts the cooling water system status in real time to ensure efficient circulation; the multi-sensor fusion module comprehensively monitors the system operation to improve data accuracy and reliability; the fault prediction and adaptive control module warns in advance and automatically adjusts the control strategy to enhance system stability and safety; the energy management and optimization module intelligently dispatches energy to achieve the lowest energy consumption. The information interaction and collaborative work between the modules ensure the novelty, creativity and efficient operation of the entire system.

[0027] In the high-efficiency machine room cooling system, the high-efficiency machine room water pump variable frequency control system proposed in this application realizes the intelligent, efficient and stable operation of the cooling system through the following specific examples: Historical data analysis: The system collects the cooling demand data of the computer room in the past year, including parameters such as heat load, external ambient temperature and humidity, and analyzes this data through machine learning algorithms to identify the cyclical change pattern of cooling demand.

[0028] Prediction model construction: Based on the analysis results, a prediction model is constructed to predict the cooling demand in the next 24 hours, including peak and trough periods.

[0029] Inverter frequency pre-adjustment: Based on the predicted cooling demand, the system adjusts the frequency of the inverter in advance to ensure that the cooling system reaches the optimal working state before the peak demand period, avoiding system response lag caused by sudden increase in demand.

[0030] Real-time monitoring and adjustment: The system monitors the pressure and flow in the cooling water system in real time. Once it detects that the pressure or flow deviates from the set value, it immediately adjusts the speed of the water pump to ensure uniform distribution and efficient circulation of the cooling water.

[0031] Cooling tower fan coordinated control: The system automatically adjusts the speed of the water pump according to the operating status of the cooling tower fan and the cooling water demand, realizes the coordinated work of the cooling tower fan and the cooling water pump, and optimizes the cooling efficiency.

[0032] Comprehensive monitoring: The system integrates multiple sensors such as temperature, humidity, pressure, flow, etc. to monitor the operating status of the cooling system in real time to ensure the comprehensiveness and accuracy of the data.

[0033] Data fusion and analysis: Through data fusion technology, data from different sensors are comprehensively analyzed to identify abnormal conditions in the operation of the cooling system, such as local overheating or hydraulic imbalance.

[0034] Historical fault data learning: The system learns the fault data of the cooling system in the past five years, including fault type, occurrence time, impact range, etc., and identifies common fault modes.

[0035] Early warning and prevention: Once an operating state similar to a historical failure mode is detected, the system immediately activates the early warning mechanism and automatically adjusts the operating parameters of the cooling tower fan and cooling water pump to prevent failures.

[0036] Energy consumption data analysis: The system analyzes the energy consumption data of the cooling system in real time and identifies peak and trough periods of energy consumption.

[0037] Intelligent scheduling: Based on energy consumption data, the system intelligently schedules the operation of cooling tower fans and cooling water pumps, such as pre-cooling cooling water during off-peak periods to reduce energy consumption during peak periods. Reference Figure 2 This embodiment also provides a high-efficiency machine room water pump variable frequency control method, which is executed by using the above-mentioned high-efficiency machine room water pump variable frequency control system, and includes the following steps: S1: Predict cooling demand and learn the periodic changing patterns of the environment inside and outside the computer room, including the temperature difference between day and night and seasonal temperature fluctuations.

[0038] Reference Figure 3 , step S1 correspondingly includes the following sub-steps: S11: The system collects environmental parameters inside and outside the computer room through an integrated sensor network, including operating status sensors of cooling tower fans and cooling water pumps, and cleans and pre-processes the data; S12: Use machine learning algorithms to identify the correlation between the operation modes of cooling tower fans and cooling water pumps and environmental parameters, and learn the periodic change pattern of cooling demand; S13: Based on the learned patterns, a prediction model is constructed to predict the coordinated operation requirements of cooling tower fans and cooling water pumps, providing forward-looking guidance for system adjustment; S14: Based on the prediction results, the system automatically generates a coordinated control strategy for the cooling tower fan and the cooling water pump, including pre-adjustment of the inverter frequency.

[0039] Correspondingly, in the efficient computer room cooling system, the intelligent prediction and optimization module realizes the coordinated operation demand prediction and control strategy generation of the cooling tower fan and cooling water pump through the following specific steps and examples: Sensor network deployment: Deploy temperature, humidity, pressure, and flow sensors inside and outside the computer room, as well as operating status sensors of cooling tower fans and cooling water pumps, such as current sensors and speed sensors.

[0040] Data collection: The system collects sensor data regularly (for example, every 5 minutes), including temperature and humidity inside and outside the computer room, pressure and flow of cooling water, and operating status of cooling tower fans and cooling water pumps (such as current and speed).

[0041] Data cleaning and preprocessing: Clean the collected data, remove outliers and missing values, and normalize the data to ensure data quality. For example, use the Z-score standardization method to preprocess the temperature data, the formula is: [Z = \frac{x - \mu}{\sigma}], where (x) is the original temperature value, (\mu) is the average temperature, and (\sigma) is the standard deviation of the temperature.

[0042] Feature selection: From the collected data, select the features most relevant to the cooling demand, such as the temperature and humidity inside and outside the computer room, the pressure and flow of cooling water, and the operating status of the cooling tower fan and cooling water pump.

[0043] Application of machine learning algorithms: Use time series analysis algorithms, such as ARIMA (autoregressive integrated moving average model) or LSTM (long short-term memory network), to identify the correlation between the operation mode of cooling tower fans and cooling water pumps and environmental parameters, and learn the periodic change pattern of cooling demand. For example, use the LSTM model to learn the pattern of cooling demand changes over time through the training data set.

[0044] Model training: Based on the learned patterns, use historical data to train a prediction model, such as using an LSTM model to predict cooling demand in the next 24 hours.

[0045] Forecast result output: The model outputs the coordinated operation requirements of the cooling tower fan and cooling water pump in the future, including the peak and trough periods of cooling demand.

[0046] Strategy generation: Based on the prediction results, the system automatically generates a coordinated control strategy for the cooling tower fan and cooling water pump, including pre-adjustment of the inverter frequency. For example, when the peak cooling demand period is predicted, the system adjusts the inverter frequency of the cooling tower fan and cooling water pump in advance to ensure that they reach the best working state before the peak demand period.

[0047] Dynamic adjustment: The system dynamically adjusts the control strategy based on the real-time monitoring of the cooling water system status to ensure efficient operation of the cooling system.

[0048] Replay Figure 2 , after step 1, the following steps are included: S2: Through intelligent hydraulic balancing technology, the water pump speed is adjusted and the valve opening in the cooling water system is automatically adjusted to ensure the hydraulic balance of the entire system and reduce local overheating or insufficient cooling.

[0049] Reference Figure 4 , step S2 correspondingly includes the following sub-steps: S21: Monitor the pressure, flow and temperature of the cooling water system in real time, and pay attention to the operating status of the cooling tower fan and cooling water pump; S22: Analyze the operating data of cooling tower fans and cooling water pumps through intelligent algorithms to identify areas of hydraulic imbalance, including local overpressure and underpressure; S23: Automatically adjust the valve opening in the cooling water system to achieve hydraulic balance; S24: Evaluate the effect of hydraulic balance adjustment and optimize the control strategy of cooling tower fan and cooling water pump based on the evaluation results.

[0050] Correspondingly, in the efficient computer room cooling system, the dynamic pressure and flow control module realizes the hydraulic balance of the cooling water system and the optimal control of the cooling tower fan and cooling water pump through the following specific steps and examples: Monitoring network: The system deploys pressure sensors, flow meters and temperature sensors, as well as sensors to monitor the operating status of cooling tower fans and cooling water pumps, such as current sensors and speed sensors.

[0051] Real-time monitoring: The system monitors the pressure, flow and temperature of the cooling water system, as well as the operating status of the cooling tower fan and cooling water pump, such as current and speed, in real time.

[0052] Data transmission and storage: Monitoring data is transmitted to the central control system in real time via wireless or wired networks for storage and analysis.

[0053] Data processing: The system processes the collected pressure, flow and temperature data in real time to identify areas of hydraulic imbalance, such as local overpressure or underpressure.

[0054] Intelligent algorithm application: Use anomaly detection algorithms, such as statistical-based anomaly detection methods or machine learning-based anomaly detection models (such as Isolation Forest), to identify data points that are significantly different from normal operating conditions and may indicate hydraulic imbalance.

[0055] Anomaly location: By analyzing the distribution of abnormal data points, the system can locate specific areas of hydraulic imbalance.

[0056] Valve control: The system automatically adjusts the valve opening in the cooling water system to achieve hydraulic balance. For example, if it detects that the pressure in a certain area is too high, the system will automatically reduce the valve opening in that area, and vice versa.

[0057] Dynamic adjustment strategy: The system dynamically adjusts the valve opening according to the real-time monitored hydraulic status to ensure the hydraulic balance of the entire cooling water system.

[0058] Effect evaluation: The system evaluates the effect of hydraulic balance adjustment, including the stability of pressure, flow and temperature, as well as the operating efficiency of cooling tower fans and cooling water pumps.

[0059] Strategy optimization: Based on the evaluation results, the system optimizes the control strategy of the cooling tower fan and cooling water pump, such as adjusting the frequency of the inverter, to further improve cooling efficiency and system stability.

[0060] Continuous learning and improvement: The system continuously learns the operating data of the cooling water system, continuously optimizes the hydraulic balance adjustment algorithm and the control strategy of the cooling equipment, and realizes the long-term and efficient operation of the cooling system.

[0061] Replay Figure 2 , after step S2, the following steps are included.

[0062] S3: The multi-sensor fusion module introduces anomaly detection algorithms that can identify abnormal conditions such as temperature, humidity, and pressure that are beyond the normal range and provide immediate warnings.

[0063] Reference Figure 5 , step S3 correspondingly includes the following sub-steps: S31: Fusing sensor data from cooling tower fans and cooling water pumps; S32: using an anomaly detection algorithm to identify abnormalities in the operating status of the cooling tower fan and the cooling water pump; S33: Once an abnormality is detected, the early warning mechanism is immediately activated to notify the system administrator and automatically adjust the operating parameters of the cooling tower fan and cooling water pump; S34: According to the warning type, the system automatically executes the coordinated control strategy of the cooling tower fan and the cooling water pump, including increasing the operating frequency of the cooling tower fan and quickly responding to abnormal situations.

[0064] Correspondingly, in the efficient computer room cooling system, the multi-sensor fusion and abnormal response module realizes comprehensive monitoring, abnormal detection and rapid response of the operating status of the cooling tower fan and cooling water pump through the following specific steps and examples: Data collection: The system collects sensor data from cooling tower fans and cooling water pumps, including but not limited to temperature, humidity, pressure, flow, current, and speed.

[0065] Data fusion: Use data fusion techniques, such as Kalman filtering or particle filtering, to combine data from different sensors to improve data accuracy and reliability. For example, Kalman filtering can effectively process the noise in sensor data and provide more accurate estimates of the operating status of cooling tower fans and cooling water pumps.

[0066] Anomaly detection algorithm: Apply anomaly detection algorithms, such as statistical anomaly detection methods (such as the 3σ principle) or machine learning-based anomaly detection models (such as Isolation Forest, One-Class SVM), to identify abnormal conditions in the operating status of cooling tower fans and cooling water pumps.

[0067] Historical data learning: The system learns historical operating data to establish a baseline for normal operating status, which is used to compare current operating data and identify anomalies.

[0068] Early warning mechanism: Once an abnormality is detected, the system immediately activates the early warning mechanism and promptly notifies the system administrator via SMS, email or system notification.

[0069] Automatic adjustment: The system automatically adjusts the operating parameters of the cooling tower fan and cooling water pump, such as increasing the operating frequency of the cooling tower fan and reducing the speed of the cooling water pump to quickly respond to abnormal situations and prevent the expansion of faults.

[0070] Warning type analysis: According to the warning type, the system automatically executes the coordinated control strategy of the cooling tower fan and the cooling water pump. For example, when the cooling tower fan is overloaded, the operating frequency of the cooling tower fan is increased, and the speed of the cooling water pump is adjusted to ensure the stable operation of the cooling system.

[0071] Quick response: The system can respond quickly to abnormal situations and automatically adjust the operating parameters of the cooling equipment to effectively prevent failures and ensure the normal operation of the cooling system.

[0072] Replay Figure 2 , after step S3, the following steps are also included: S4: When the system detects that the water pump motor is overheated, it reduces the operating frequency and automatically adjusts the circulation path of the cooling water to ensure the continuous operation of the cooling system through the backup cooling equipment.

[0073] Reference Figure 6 , step S4 correspondingly includes the following sub-steps: S41: Collect historical fault data of cooling tower fans and cooling water pumps, including fault type, occurrence time and impact range; S42: Identify common failure modes and potential causes of cooling tower fans and cooling water pumps through data analysis; S43: Utilize a self-learning algorithm to optimize the fault recovery strategy of cooling tower fans and cooling water pumps based on historical data; S44: When a fault is detected, the optimized recovery strategy is automatically executed, and the coordinated control strategy of the cooling tower fan and the cooling water pump is further optimized according to the execution result.

[0074] Correspondingly, in the efficient computer room cooling system, the fault prediction and recovery optimization module realizes the fault mode identification, fault recovery strategy optimization and automatic execution of the cooling tower fan and cooling water pump through the following specific steps and examples: Data collection: The system collects historical fault data of cooling tower fans and cooling water pumps, including fault type, occurrence time, impact scope, and fault handling process and results.

[0075] Data collation: Sort and classify the collected fault data, establish a fault database, and provide data support for subsequent fault pattern identification and recovery strategy optimization.

[0076] Data Analysis: Use data mining techniques such as cluster analysis, association rule analysis or decision tree algorithms to identify common failure modes and potential causes of failure of cooling tower fans and cooling water pumps.

[0077] Pattern recognition: By analyzing historical fault data, the system can identify specific fault modes, such as overload faults of cooling tower fans, blockage faults of cooling water pumps, etc., as well as common causes of these faults, such as dust accumulation on cooling tower fan blades and blockage of cooling water pump filters.

[0078] Self-learning algorithm application: Use self-learning algorithms, such as reinforcement learning or genetic algorithms, to optimize the fault recovery strategy of cooling tower fans and cooling water pumps based on historical fault data.

[0079] Strategy optimization: The system automatically adjusts and optimizes fault recovery strategies by learning historical fault recovery processes. For example, when a cooling tower fan overload is detected, the system automatically executes a recovery strategy of reducing the cooling tower fan operating frequency and increasing the cooling water pump speed to quickly restore normal operation of the cooling system.

[0080] Automatic execution: When a fault is detected, the system automatically executes the optimized recovery strategy, such as adjusting the operating parameters of the cooling tower fan and cooling water pump to quickly respond to the fault and reduce the impact of the fault.

[0081] Strategy iteration: Based on the execution results of fault recovery, the system continuously iterates and optimizes the collaborative control strategy of the cooling tower fan and cooling water pump to improve the efficiency and effectiveness of fault recovery.

[0082] Replay Figure 2 , after step S4, the following steps are also included: S5: Convert waste heat generated during the cooling process into reusable energy for heating the computer room or supplying hot water.

[0083] Reference Figure 7 , step S5 correspondingly includes the following sub-steps: S51: Real-time monitoring of energy consumption of cooling tower fans and cooling water pumps, analysis of energy efficiency and potential recovery opportunities; S52: Integrate energy recovery technologies such as heat recovery to convert waste heat generated during the operation of cooling tower fans and cooling water pumps into reusable energy; S53: Plan energy reuse paths, including heating or hot water supply for computer rooms, to achieve energy recycling; S54: According to the energy recovery and reuse situation, intelligently schedule the operation of cooling tower fans and cooling water pumps to achieve low energy consumption.

[0084] Correspondingly, in the high-efficiency computer room cooling system, the energy efficiency optimization and recovery module implements energy consumption monitoring, energy recovery and reuse, and intelligent scheduling of cooling tower fans and cooling water pumps through the following specific steps and examples to achieve low energy consumption and energy recycling: Real-time monitoring: The system monitors the energy consumption of cooling tower fans and cooling water pumps in real time, including electricity consumption and heat energy consumption.

[0085] Energy efficiency analysis: By analyzing energy consumption data, the system evaluates the energy efficiency of cooling tower fans and cooling water pumps and identifies energy waste.

[0086] Identification of potential recycling opportunities: The system identifies recoverable energy such as waste heat generated during the operation of cooling tower fans and cooling water pumps, and evaluates their recycling potential.

[0087] Heat recovery technology: Integrate heat recovery technology, such as heat pump systems or heat exchangers, to convert waste heat generated during the operation of cooling tower fans and cooling water pumps into reusable energy.

[0088] Energy recovery system design: Design energy recovery system to ensure effective collection and conversion of waste heat and improve energy recovery efficiency.

[0089] Reuse path planning: Planning energy reuse paths, including using recycled energy for heating or hot water supply in the computer room to achieve energy recycling.

[0090] System integration: Integrate the energy recovery system with the heating or hot water supply system of the machine room to ensure the effective use of the recovered energy.

[0091] Intelligent scheduling: According to the energy recovery and reuse situation, the system intelligently schedules the operation of the cooling tower fan and cooling water pump. For example, when the energy recovery efficiency is high, the operating frequency of the cooling tower fan and cooling water pump is appropriately reduced to reduce energy consumption.

[0092] Low energy consumption operation: Through intelligent scheduling, the system realizes low energy consumption operation of cooling tower fans and cooling water pumps, while ensuring the normal operation of the cooling system and temperature control of the machine room.

[0093] Replay Figure 2 After step S5, the following steps are included: S6: Through the integrated intelligent maintenance module, the maintenance needs of the equipment are predicted and the maintenance plan is automatically planned based on the equipment operation data and historical maintenance records.

[0094] Reference Figure 8 , step S6 correspondingly includes the following sub-steps: S61: Real-time monitoring of the operating status of the cooling tower fan and cooling water pump, including temperature, vibration, current and other parameters; S62: Use historical maintenance records and equipment operation data to build a predictive maintenance model to predict the maintenance needs of cooling tower fans and cooling water pumps; S63: Based on the prediction results, the system automatically plans the maintenance plan for the cooling tower fan and cooling water pump, including the maintenance time and maintenance content; S64: Evaluate the effectiveness of the maintenance plan and optimize the maintenance strategy for the cooling tower fan and cooling water pump based on the evaluation results.

[0095] Correspondingly, in the efficient computer room cooling system, the predictive maintenance and intelligent scheduling module realizes real-time monitoring of the operating status of the cooling tower fan and cooling water pump, construction of the predictive maintenance model, automatic planning of the maintenance plan, and optimization of the maintenance strategy through the following specific steps and examples: Parameter monitoring: The system monitors the operating status of cooling tower fans and cooling water pumps in real time, including key parameters such as temperature, vibration, and current, to assess the health of the equipment.

[0096] Data collection: Through the sensor network, the system collects equipment operation data and provides real-time data support for subsequent predictive maintenance models.

[0097] Historical data integration: The system integrates historical maintenance records and equipment operation data, including fault records, maintenance logs, operating parameters, etc.

[0098] Model building: Use machine learning algorithms such as time series analysis, support vector machines, or deep learning models to build predictive maintenance models to predict the maintenance needs of cooling tower fans and cooling water pumps.

[0099] Model training and verification: Train the model through historical data to verify the model's predictive accuracy and reliability, and ensure that the model can accurately predict the maintenance needs of the equipment.

[0100] Prediction result analysis: The system analyzes the maintenance requirements of the cooling tower fan and cooling water pump based on the prediction results of the predictive maintenance model, including the maintenance time point and maintenance content.

[0101] Automatic planning: The system automatically plans the maintenance plan, including determining the maintenance time, maintenance content and required resources, to ensure efficient execution of maintenance work.

[0102] Execution effect evaluation: The system evaluates the execution effect of the maintenance plan, including the operating status of the equipment after maintenance, maintenance cost and maintenance efficiency.

[0103] Strategy optimization: Based on the evaluation results, the system optimizes the maintenance strategy of cooling tower fans and cooling water pumps, such as adjusting maintenance cycles and optimizing maintenance processes to improve maintenance efficiency and equipment reliability.

[0104] This embodiment also provides an electronic device. The electronic device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, an efficient frequency conversion control method for a machine room water pump is implemented: S1: Predict cooling demand and learn the periodic change patterns of the environment inside and outside the computer room, including the temperature difference between day and night and seasonal temperature fluctuations; S2: Through intelligent hydraulic balancing technology, the pump speed is adjusted and the valve opening in the cooling water system is automatically adjusted to ensure the hydraulic balance of the entire system and reduce local overheating or insufficient cooling; S3: The multi-sensor fusion module introduces anomaly detection algorithms that can identify abnormal conditions such as temperature, humidity, and pressure that are beyond the normal range and provide immediate warnings; S4: When the system detects that the water pump motor is overheated, it reduces the operating frequency and automatically adjusts the circulation path of the cooling water to ensure the continuous operation of the cooling system through the backup cooling equipment; S5: Convert waste heat generated during the cooling process into reusable energy for heating the computer room or supplying hot water; S6: Through the integrated intelligent maintenance module, the maintenance needs of the equipment are predicted and the maintenance plan is automatically planned based on the equipment operation data and historical maintenance records.

[0105] When the computer program is executed by a processor, it can implement any one of the high-efficiency machine room water pump variable frequency control methods in the above method embodiments.

[0106] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: S1: Predict cooling demand and learn the periodic change patterns of the environment inside and outside the computer room, including the temperature difference between day and night and seasonal temperature fluctuations; S2: Through intelligent hydraulic balancing technology, the pump speed is adjusted and the valve opening in the cooling water system is automatically adjusted to ensure the hydraulic balance of the entire system and reduce local overheating or insufficient cooling; S3: The multi-sensor fusion module introduces anomaly detection algorithms that can identify abnormal conditions such as temperature, humidity, and pressure that are beyond the normal range and provide immediate warnings; S4: When the system detects that the water pump motor is overheated, it reduces the operating frequency and automatically adjusts the circulation path of the cooling water to ensure the continuous operation of the cooling system through the backup cooling equipment; S5: Convert waste heat generated during the cooling process into reusable energy for heating the computer room or supplying hot water; S6: Through the integrated intelligent maintenance module, the maintenance needs of the equipment are predicted and the maintenance plan is automatically planned based on the equipment operation data and historical maintenance records.

[0107] When the computer program is executed by a processor, it can implement any one of the high-efficiency machine room water pump variable frequency control methods in the above method embodiments.

[0108] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0109] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0110] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail in the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A high-efficiency machine room water pump frequency conversion control system, characterized in that: include: Intelligent prediction and optimization module predicts the cooling demand of the computer room based on historical data, adjusts the working status of the cooling system in advance, and predicts the cooling demand in the future by analyzing parameters such as the heat load of the computer room, external ambient temperature and humidity, thereby guiding the inverter to adjust the frequency in advance; Dynamic pressure and flow control module monitors the pressure and flow in the cooling water system in real time, dynamically adjusts according to system requirements, and automatically adjusts the speed of the water pump according to the operating status of the cooling tower fan and the cooling water demand to achieve precise control of pressure and flow; Multi-sensor fusion module combines temperature, humidity, pressure, flow and other sensors to comprehensively monitor the operating status of the cooling system; Fault prediction and adaptive control module, which predicts potential system failures based on historical fault data and takes preventive measures in advance; The energy management and optimization module optimizes energy distribution by analyzing the energy consumption data of the cooling system and intelligently schedules the operation of cooling tower fans and cooling water pumps.

2. A high-efficiency machine room water pump variable frequency control method, implemented by a high-efficiency machine room water pump variable frequency control system as claimed in claim 1, characterized in that: The following steps are involved: S1: Predict cooling demand and learn the periodic change patterns of the environment inside and outside the computer room, including the temperature difference between day and night and seasonal temperature fluctuations; S2: Through intelligent hydraulic balancing technology, the pump speed is adjusted and the valve opening in the cooling water system is automatically adjusted to ensure the hydraulic balance of the entire system and reduce local overheating or insufficient cooling; S3: The multi-sensor fusion module introduces anomaly detection algorithms that can identify abnormal conditions such as temperature, humidity, and pressure that are beyond the normal range and provide immediate warnings; S4: When the system detects that the water pump motor is overheated, it reduces the operating frequency and automatically adjusts the circulation path of the cooling water to ensure the continuous operation of the cooling system through the backup cooling equipment; S5: Convert waste heat generated during the cooling process into reusable energy for heating the computer room or supplying hot water; S6: Through the integrated intelligent maintenance module, the maintenance needs of the equipment are predicted and the maintenance plan is automatically planned based on the equipment operation data and historical maintenance records.

3. A high-efficiency machine room water pump frequency conversion control method according to claim 2, characterized in that: The method of predicting cooling demand and learning the periodic change patterns of the environment inside and outside the computer room, including the temperature difference between day and night and seasonal temperature fluctuations, includes the following steps: S11: The system collects environmental parameters inside and outside the computer room through an integrated sensor network, including operating status sensors of cooling tower fans and cooling water pumps, and cleans and pre-processes the data; S12: Use machine learning algorithms to identify the correlation between the operation modes of cooling tower fans and cooling water pumps and environmental parameters, and learn the periodic change pattern of cooling demand; S13: Based on the learned patterns, a prediction model is constructed to predict the coordinated operation requirements of cooling tower fans and cooling water pumps, providing forward-looking guidance for system adjustment; S14: Based on the prediction results, the system automatically generates a coordinated control strategy for the cooling tower fan and the cooling water pump, including pre-adjustment of the inverter frequency.

4. A high-efficiency machine room water pump frequency conversion control method according to claim 2, characterized in that: The method of adjusting the pump speed and automatically adjusting the valve opening in the cooling water system through intelligent hydraulic balancing technology to ensure the hydraulic balance of the entire system and reduce local overheating or insufficient cooling includes the following steps: S21: Monitor the pressure, flow and temperature of the cooling water system in real time, and pay attention to the operating status of the cooling tower fan and cooling water pump; S22: Analyze the operating data of cooling tower fans and cooling water pumps through intelligent algorithms to identify areas of hydraulic imbalance, including local overpressure and underpressure; S23: Automatically adjust the valve opening in the cooling water system to achieve hydraulic balance; S24: Evaluate the effect of hydraulic balance adjustment and optimize the control strategy of cooling tower fan and cooling water pump based on the evaluation results.

5. A high-efficiency machine room water pump frequency conversion control method according to claim 2, characterized in that: The multi-sensor fusion module introduces an anomaly detection algorithm, which can identify abnormal conditions such as temperature, humidity, pressure, etc. that are beyond the normal range and issue an immediate warning, including the following steps: S31: Fusing sensor data from cooling tower fans and cooling water pumps; S32: using an anomaly detection algorithm to identify abnormalities in the operating status of the cooling tower fan and the cooling water pump; S33: Once an abnormality is detected, the early warning mechanism is immediately activated to notify the system administrator and automatically adjust the operating parameters of the cooling tower fan and cooling water pump; S34: According to the warning type, the system automatically executes the coordinated control strategy of the cooling tower fan and the cooling water pump, including increasing the operating frequency of the cooling tower fan and quickly responding to abnormal situations.

6. A high-efficiency machine room water pump variable frequency control method according to claim 2, characterized in that: When the system detects that the water pump motor is overheated, the operating frequency is reduced, the circulation path of the cooling water is automatically adjusted, and the continuous operation of the cooling system is ensured through the backup cooling equipment, including the following steps: S41: Collect historical fault data of cooling tower fans and cooling water pumps, including fault type, occurrence time and impact range; S42: Identify common failure modes and potential causes of cooling tower fans and cooling water pumps through data analysis; S43: Utilize a self-learning algorithm to optimize the fault recovery strategy of cooling tower fans and cooling water pumps based on historical data; S44: When a fault is detected, the optimized recovery strategy is automatically executed, and the coordinated control strategy of the cooling tower fan and the cooling water pump is further optimized according to the execution result.

7. A high-efficiency machine room water pump frequency conversion control method according to claim 2, characterized in that: The method of converting the waste heat generated during the cooling process into reusable energy for heating the machine room or supplying hot water comprises the following steps: S51: Real-time monitoring of energy consumption of cooling tower fans and cooling water pumps, analysis of energy efficiency and potential recovery opportunities; S52: Integrate energy recovery technologies such as heat recovery to convert waste heat generated during the operation of cooling tower fans and cooling water pumps into reusable energy; S53: Plan energy reuse paths, including heating or hot water supply for computer rooms, to achieve energy recycling; S54: According to the energy recovery and reuse situation, intelligently schedule the operation of cooling tower fans and cooling water pumps to achieve low energy consumption.

8. A high-efficiency machine room water pump frequency conversion control method according to claim 2, characterized in that: The integrated intelligent maintenance module predicts the maintenance needs of the equipment based on the equipment operation data and historical maintenance records, and automatically plans the maintenance plan, including the following steps: S61: Real-time monitoring of the operating status of the cooling tower fan and cooling water pump, including temperature, vibration, current and other parameters; S62: Use historical maintenance records and equipment operation data to build a predictive maintenance model to predict the maintenance needs of cooling tower fans and cooling water pumps; S63: Based on the prediction results, the system automatically plans the maintenance plan for the cooling tower fan and cooling water pump, including the maintenance time and maintenance content; S64: Evaluate the effectiveness of the maintenance plan and optimize the maintenance strategy for the cooling tower fan and cooling water pump based on the evaluation results.

9. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a high-efficiency machine room water pump variable frequency control method as described in any one of claims 2 to 8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a high-efficiency machine room water pump variable frequency control method as described in any one of claims 2 to 8 are implemented.

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