Circulating cooling water system integrating big data and artificial intelligence

Abstract

The invention discloses a circulating cooling water system integrating big data and artificial intelligence, and relates to the technical field of big data and artificial intelligence, and the circulating cooling water system comprises an information monitoring module which is used for monitoring the parameter change condition during the operation of a unit; the data processing module is used for cleaning and integrating original data; the artificial intelligence prediction module is used for predicting a future cooling demand based on historical data and a current condition; the optimization control module is used for formulating an optimal system operation strategy according to the prediction result; the execution module is used for applying the optimized control strategy to an actual system; the monitoring and alarming module is used for monitoring the running state of the system in real time and giving an alarm in time when an abnormality is found; and the performance evaluation module is used for evaluating the energy efficiency of the system and providing a basis for long-term optimization. According to the invention, comprehensive monitoring, diagnosis, prediction and optimization functions are realized, the operation efficiency and reliability of the system are greatly improved, and the intelligent and adaptive characteristics of the system enable the system to maintain optimal performance under various working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of big data and artificial intelligence, and in particular to a circulating cooling water system integrating big data and artificial intelligence. Background Art

[0002] In modern industrial production and commercial buildings, circulating cooling water systems are an important part of maintaining efficient equipment operation and energy conservation and emission reduction. With the continuous advancement of science and technology, traditional circulating cooling water systems have evolved from simple mechanical control in the early days to today's complex automated control systems.

[0003] In recent years, with the rapid rise of big data technology and artificial intelligence technology, more and more research and application attempts have been made to integrate these advanced technologies into circulating cooling water systems to further improve their performance and reliability. For example, through real-time monitoring and data analysis, the system can adjust operating parameters in a timely manner and optimize energy consumption. However, the existing technology still has many shortcomings in practical applications, especially in terms of intelligent prediction and optimization control, and has not yet achieved the desired effect.

[0004] Existing circulating cooling water systems usually rely on preset control parameters and rules, which are difficult to cope with complex and changing working conditions. This fixed control strategy is prone to problems such as energy waste and increased equipment wear in actual operation. In addition, although some systems have tried to introduce data analysis and prediction technologies, most of them are still at the basic data monitoring and recording stage, lacking in-depth data mining and intelligent analysis capabilities, and unable to achieve accurate prediction and optimized control of future demand. Summary of the invention

[0005] In view of the above problems existing in the existing circulating cooling water system, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is how to provide a system that can realize comprehensive monitoring, diagnosis, prediction and optimization functions.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a circulating cooling water system integrating big data and artificial intelligence, which includes: an information monitoring module for monitoring parameter changes during unit operation; a data processing module for cleaning and integrating raw data; an artificial intelligence prediction module for predicting future cooling needs based on historical data and current conditions; an optimization control module for formulating an optimal system operation strategy based on the prediction results; an execution module for applying the optimized control strategy to the actual system; a monitoring and alarm module for real-time monitoring of the system operation status and timely alarming when an abnormality is found; a performance evaluation module for evaluating the energy efficiency of the system to provide a basis for long-term optimization; the output of the information monitoring module is connected to the input of the data processing module, the output of the data processing module is connected to the input of the artificial intelligence prediction module, the output of the artificial intelligence prediction module is connected to the input of the optimization control module, the output of the optimization control module is connected to the input of the execution module, the output of the execution module is connected to the input of the monitoring and alarm module, and the output of the monitoring and alarm module is connected to the input of the performance evaluation module.

[0009] As a preferred solution of the circulating cooling water system integrating big data and artificial intelligence described in the present invention, the information monitoring module includes: installing a variety of water quality sensors, temperature sensors, flow meters and energy consumption sensors; setting the sampling frequency to convert analog signals into digital signals; setting the normal operating range for each parameter, and triggering an alarm when the parameter exceeds the range.

[0010] As a preferred solution of the circulating cooling water system integrating big data and artificial intelligence described in the present invention, the data processing module includes the following contents: detecting abnormal values: defining normal ranges for different types of sensor data, checking whether the sensor data is within the predefined normal range, if not, marking it as abnormal; if it is, using the interquartile range IQR method for secondary detection, if the data point falls below Q1-1.5IQR or above Q3+1.5IQR, it is considered abnormal, wherein Q3 is the upper quartile, Q1 is the lower quartile, and IQR is the inner moment; merging data from different sensors and devices into a unified data set, using timestamps as keywords during the merging process, and adopting the approximate merge merge_asof method to process the data collection time differences of different sensors.

[0011] As a preferred solution of the circulating cooling water system integrating big data and artificial intelligence described in the present invention, the artificial intelligence prediction module includes the following contents: collecting the historical operation data of the system and the transmission data of the current data processing module, and normalizing all the collected data to ensure the quality and consistency of the data:

[0012]

[0013] Among them, X is the original data, X min and X max are the minimum and maximum values ​​of the data respectively; the change rate feature is extracted from the preprocessed data, where the calculation formula of the change rate is as follows:

[0014]

[0015] Among them, X (t) is the data value at time t1, X (t1-1) is the data value at the previous time point; train a separate XGBoost model for each prediction time range and each output parameter; use the Bayesian optimization method to find the best hyperparameters, use the gradient boosting decision tree algorithm, and the objective function is the mean square error; use the early stopping method to prevent overfitting; the early stopping method to prevent overfitting includes: setting the initial best validation loss best val is positive infinity, sets the initial patience count to 0, and sets the patience value pat; after each training round: calculate the current verification loss val loss ; if val loss <best val , then update the best validation loss best val =

[0016] val loss , and reset the patience count; otherwise, reduce the patience count; the verification loss formula is:

[0017]

[0018] Among them, n val is the number of samples in the validation set, L is the loss function, y i is the true value of the i-th validation set sample, is the predicted value of the i-th validation set sample.

[0019] As a preferred solution of the circulating cooling water system integrating big data and artificial intelligence described in the present invention, the optimization control module includes the following contents: defining a multi-objective optimization function, including minimization of energy consumption, water quality maintenance and temperature control accuracy; the energy consumption minimization is:

[0020] E=∑P F ×T 0 +P P ×T 0

[0021] Among them, P F is the fan power, P P is the pump power, T 0 is the running time; the water quality maintenance is:

[0022] WQ=ω1×|pH-pH tar |+ω2×|EC-EC tar |

[0023] Wherein, pH is pH value, EC is conductivity, ω1 and ω2 are weight coefficients; the temperature control accuracy is:

[0024] TC=∑|T act -T set |

[0025] Among them, T act is the actual water temperature, T set is the set temperature; the comprehensive objective function is:

[0026] F=αE+βWQ+γTC

[0027] Among them, α, β, and γ are the weight coefficients of each sub-goal; the set constraints include: cooling tower fan speed range, circulating water pump flow range, water temperature setting range, and system cooling capacity constraints.

[0028] As a preferred solution of the circulating cooling water system integrating big data and artificial intelligence described in the present invention, the optimization control module also includes: using model predictive control MPC combined with particle swarm optimization algorithm PSO optimization control strategy, including the following contents: MPC prediction model is:

[0029] x(k+1)=Ax(k)+Bu(k)

[0030] y(k)=Cx(k)

[0031] PSO update formula:

[0032] v i (t+1)=ω0.v i (t)+c1.r1.(p i -x i (t))+c2.r2.(gx i (t))

[0033] x i (t+1)=x i (t)+v i (t+1)

[0034] Among them, x is the system state, u is the control input, y is the system output, A, B, C are the system matrices, and v i is the particle velocity, x i is the particle position, p iis the individual optimal solution, g is the global optimal solution, ω0 is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random numbers; the control strategy optimization process is: initialize the particle swarm, each particle represents a set of control parameters, including fan speed, water pump flow and temperature set point, for each time step in the prediction time domain: use the MPC model to predict the system response; calculate the objective function value; update the individual optimal solution and the global optimal solution; use the PSO algorithm to update the particle position and velocity, and repeat the above steps until the maximum number of iterations is reached or the convergence condition is met.

[0035] As a preferred solution of the circulating cooling water system integrating big data and artificial intelligence described in the present invention, the optimization control module further includes: checking whether the optimization result meets all constraints, if not, using the penalty function method to correct the result:

[0036]

[0037] Among them, F is the original objective function, μ is the penalty parameter, and gi(x) is the i-th constraint.

[0038] As a preferred solution of the circulating cooling water system integrating big data and artificial intelligence described in the present invention, wherein: the performance evaluation module includes calculating energy utilization efficiency indicators and generating optimization effect reports;

[0039] The energy utilization efficiency index includes an energy efficiency index and a temperature control accuracy.

[0040] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the circulating cooling water system integrating big data and artificial intelligence as described in the first aspect of the present invention is implemented.

[0041] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the circulating cooling water system integrating big data and artificial intelligence as described in the first aspect of the present invention is implemented.

[0042] The beneficial effect of the present invention is that the circulating cooling water system integrating big data and artificial intelligence can realize comprehensive monitoring, diagnosis, prediction and optimization functions, greatly improve the operating efficiency and reliability of the system, and the intelligent and adaptive characteristics of the system enable it to maintain optimal performance under various working conditions, while providing powerful decision-making support for operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0044] Figure 1 Structural diagram of a circulating cooling water system that integrates big data and artificial intelligence.

[0045] Figure 2 It is a schematic diagram of the structure of the terminal of the present invention. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0049] Example 1

[0050] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a circulating cooling water system integrating big data and artificial intelligence, which is applied to Figure 2 The terminal in is used as an example to illustrate the following contents:

[0051] The information monitoring module is used to monitor the changes in operating parameters such as water quality, temperature, flow rate and energy consumption during the operation of the unit; the data processing module is used to clean and integrate the original data in preparation for subsequent analysis; the artificial intelligence prediction module is used to predict future cooling needs based on historical data and current conditions; the optimization control module is used to formulate the optimal system operation strategy according to the prediction results; the execution module is used to apply the optimized control strategy to the actual system; the monitoring and alarm module is used to monitor the system operation status in real time and promptly alarm when abnormalities are found; the performance evaluation module is used to evaluate the energy efficiency of the system and provide a basis for long-term optimization.

[0052] The output of the information monitoring module is connected to the input of the data processing module, the output of the data processing module is connected to the input of the artificial intelligence prediction module, the output of the artificial intelligence prediction module is connected to the input of the optimization control module, the output of the optimization control module is connected to the input of the execution module, the output of the execution module is connected to the input of the monitoring and alarm module, and the output of the monitoring and alarm module is connected to the input of the performance evaluation module.

[0053] Furthermore, the information monitoring module includes: installing a variety of water quality sensors at key locations of the system, including pH sensors for monitoring the acidity and alkalinity of water; conductivity sensors for detecting the ion content in water; dissolved oxygen sensors for measuring the dissolved oxygen content in water; deploying temperature sensors at multiple key locations of the system, such as: cooling water inlet, cooling water outlet and cooling tower; installing flow meters in the main pipeline and bypass; installing energy consumption sensors to monitor the energy consumption of the water pump and the energy consumption of the cooling tower.

[0054] Set the sampling frequency (e.g. once every 5 minutes), use a high-precision analog-to-digital converter to convert the analog signal into a digital signal, develop a real-time monitoring dashboard to display the real-time values ​​and trend charts of key parameters, set the normal operating range for each parameter, and trigger an alarm when the parameter exceeds the range.

[0055] Furthermore, the data processing module includes the following contents: processing data points beyond the normal range, detecting outliers, and deciding whether to delete or replace outliers according to specific circumstances.

[0056] Furthermore, the process of detecting outliers includes:

[0057] First, define normal ranges for different types of sensor data, such as pH (6.0-9.0), conductivity (100-1000μS / cm), temperature (0-50℃), flow (0-1000L / min), etc. Then, use multiple judgment criteria to identify abnormal values:

[0058] First, check whether it is within the predefined normal range. If not, it is marked as an abnormality. If it is, the interquartile range (IQR) method is further used. If the data point falls below Q1-1.5IQR or above Q3+1.5IQR, it is considered an abnormality, where Q3 is the upper quartile, Q1 is the lower quartile, and IQR is the inner moment.

[0059] The data processing module also includes: unifying the timestamp format to ensure the consistency of time series data, normalizing field names so that data from different sources can be seamlessly integrated; converting categorical variables into numerical form, and normalizing or standardizing numerical variables to make their distribution more suitable for subsequent analysis;

[0060] Merge data from different sensors and devices into a unified dataset. First, merge data from different sensors (pH value, conductivity, temperature, flow, etc.) into a unified dataset. During the merging process, use the timestamp as the keyword and adopt the approximate merge_asof method to handle the situation where the data collection time of different sensors may be slightly different.

[0061] Artificial intelligence prediction module, including the following:

[0062] Collect the historical operation data of the system and the transmission data of the current data processing module, and normalize all the collected data to ensure the quality and consistency of the data:

[0063]

[0064] Among them, X is the original data, X min and X max are the minimum and maximum values ​​of the data respectively.

[0065] Extract key features from the preprocessed data, such as temperature change rate, humidity change rate, equipment load rate, etc., and use Fourier transform to extract the periodic characteristics of the time series. The calculation formula for the change rate is as follows:

[0066]

[0067] Among them, X (t) is the data value at time t1, X (t1-1) is the data value at the previous time point.

[0068] The historical data is trained using a machine learning algorithm to generate a prediction model. The algorithm of the present invention uses XGBoost to train a separate XGBoost model for each prediction time range (24 hours, 48 ​​hours, 7 days) and each output parameter (cooling water flow, required temperature).

[0069] Furthermore, the data set is divided into a training set of 70%, a validation set of 15%, and a test set of 15%. The Bayesian optimization method is used to find the best hyperparameters, including the maximum depth of the tree, the minimum child node weight, the feature sampling ratio, etc. The gradient boosting decision tree algorithm is used, and the objective function is the mean square error. The early stopping method is used to prevent overfitting, including the following steps:

[0070] Set the initial best validation loss best val is positive infinity, sets the initial patience count to 0, and sets the patience value pat; after each training round: calculate the current verification loss val loss ; if val loss <best val, then update the best validation loss best val =val loss , and reset the patience count; otherwise, decrement the patience count.

[0071] Furthermore, the validation loss formula is:

[0072]

[0073] Among them, n val is the number of samples in the validation set, L is the loss function, y i is the true value of the i-th validation set sample, is the predicted value of the i-th validation set sample.

[0074] It should be noted that by introducing the early stopping method, the model can be effectively prevented from overfitting during the training process, thereby improving the generalization ability and prediction accuracy of the model.

[0075] The model performance was evaluated using root mean square error (RMSE) and mean absolute percentage error (MAPE).

[0076] Optimize the control module, including the following:

[0077] Obtain cooling water flow forecasts for the next 24 hours, 48 ​​hours and 7 days and required temperature forecasts for the next 24 hours, 48 ​​hours and 7 days from the artificial intelligence prediction module; obtain current system status, including cooling tower fan speed, circulating water pump flow, current water temperature and system load; obtain external environmental data, including ambient temperature, relative humidity and atmospheric pressure.

[0078] Define a multi-objective optimization function including minimization of energy consumption, maintenance of water quality, and temperature control accuracy.

[0079] Specifically, energy consumption is minimized as follows:

[0080] E=∑P F ×T 0 +P P ×T 0

[0081] Among them, P F is the fan power, P P is the pump power, T 0 For the running time.

[0082] Water quality maintenance is:

[0083] WQ=ω1×|pH-pH tar |+ω2×|EC-EC tar |

[0084] Among them, pH is the pH value, EC is the electrical conductivity, and ω1 and ω2 are weight coefficients.

[0085] Temperature control accuracy:

[0086] TC=∑|T act -T set |

[0087] Among them, T act is the actual water temperature, T set To set the temperature.

[0088] The comprehensive objective function is:

[0089] F=αE+βWQ+γTC

[0090] Among them, α, β, and γ are the weight coefficients of each sub-goal.

[0091] The set constraints include: cooling tower fan speed range, circulating water pump flow range, water temperature set point range and system cooling capacity constraints.

[0092] Furthermore, the model predictive control MPC combined with the particle swarm optimization algorithm PSO is used to optimize the control strategy, including the following contents:

[0093] The MPC prediction model is:

[0094] x(k+1)=Ax(k)+Bu(k)

[0095] y(k)=Cx(k)

[0096] PSO update formula:

[0097] v i (t+1)=ω0.v i (t)+c1.r1.(p i -x i (t))+c2.r2.(gx i (t))

[0098] x i (t+1)=x i (t)+v i (t+1)

[0099] Among them, x is the system state, u is the control input, y is the system output, A, B, C are the system matrices, and v i is the particle velocity, x i is the particle position, p i is the individual optimal solution, g is the global optimal solution, ω0 is the inertia weight, c1 and c2 are acceleration constants, and r1 and r2 are random numbers.

[0100] Preferably, the control strategy optimization process is:

[0101] Initialize the particle swarm, each particle represents a set of control parameters (fan speed, pump flow, temperature set point), for each time step in the prediction time domain:

[0102] Use MPC models to predict system responses;

[0103] Calculate the objective function value;

[0104] Update individual optimal solutions and global optimal solutions;

[0105] Use the PSO algorithm to update the particle position and velocity, and repeat the above steps until the maximum number of iterations is reached or the convergence condition is met.

[0106] And use Kalman filter algorithm to update MPC model parameters.

[0107] Check whether the optimization result meets all constraints. If not, use the penalty function method to correct the result:

[0108]

[0109] Among them, F is the original objective function, μ is the penalty parameter, and gi(x) is the i-th constraint.

[0110] Generate control instructions, including fan inverter setpoint, water pump inverter setpoint and PLC temperature setpoint, and send the control instructions to the execution module.

[0111] It should be noted that the prediction results provided by XGBoost, such as cooling water flow and temperature requirements for the next 24 hours, 48 ​​hours and 7 days, are used as inputs to MPC; MPC uses these predictions to plan future control strategies, while PSO is used to find the optimal control parameters within the MPC framework.

[0112] Execution module, including the following:

[0113] The execution module receives the optimized control strategy from the optimization control module, and parses the overall control strategy into specific execution instructions, such as the pump start instruction, the fan speed setting, etc., and sends the control instructions to each actuator through the network or bus system. Each actuator will receive a specific control instruction, and the actuator will perform corresponding operations according to the received instructions, such as pump start, fan speed adjustment, etc.

[0114] Monitoring and alarm modules, including:

[0115] The main function of the monitoring and alarm module is to monitor the key parameters of the system in real time to ensure that these parameters operate within the normal range. If any abnormal situation is found, the module will immediately notify the relevant personnel via SMS or email to ensure that the problem can be dealt with quickly, including:

[0116] Monitor key parameters in the system, such as temperature, pressure, flow, current, etc., and set the normal range value of each parameter; compare the monitored parameters with their normal range values ​​in real time, and if the parameters are out of the normal range, mark them as abnormal; when an abnormality is detected, trigger the alarm mechanism and generate alarm information, including abnormal parameters, current values, timestamps, etc., and notify relevant personnel through preset communication methods (such as SMS, email).

[0117] Record the system's operating status, including real-time values ​​of all key parameters, and record alarm information for subsequent analysis and troubleshooting.

[0118] Performance evaluation module, including calculation of energy efficiency indicators:

[0119] Calculate the energy efficiency index:

[0120]

[0121] Among them, EER opt is the optimized energy efficiency index; Q cooling is the cooling capacity provided, in kilowatts (kW); PLR is the part load ratio, P fan is the power consumption of the fan, in kilowatts (kW); P pump is the power consumption of the pump, in kilowatts (kW); P c_tower The power consumption of the cooling tower in kilowatts (kW).

[0122] Calculate temperature control accuracy:

[0123]

[0124] Among them, RMSE te_opt is the optimized temperature control accuracy, T act,i is the actual temperature at the i-th time point, T set,i is the set temperature at the i-th time point, N is the total number of measurement points, ω i is the weight of the ith time point.

[0125] Furthermore, optimization effect reports are generated, including: daily report: 24-hour energy consumption curve, daily average EER and SEC, comparison with the previous day, and abnormal event marking; weekly report: daily energy consumption and EER trend, weekly average EER and SEC, comparison with last week, and identification of the best and worst performance days; monthly report: monthly total energy consumption and distribution, monthly average EER and SEC, comparison with the previous month and the same period last year, performance trend analysis, and energy saving potential estimation, etc.

[0126] This embodiment also provides a computer device, which is suitable for the case of a circulating cooling water system integrating big data and artificial intelligence, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to realize the circulating cooling water system integrating big data and artificial intelligence proposed in the above embodiment.

[0127] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0128] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by the processor, a circulating cooling water system integrating big data and artificial intelligence is implemented as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0129] In summary, the circulating cooling water system integrating big data and artificial intelligence of the present invention can realize comprehensive monitoring, diagnosis, prediction and optimization functions, greatly improve the operating efficiency and reliability of the system, and the intelligent and adaptive characteristics of the system enable it to maintain optimal performance under various working conditions, while providing powerful decision-making support for operators.

[0130] Example 2

[0131] Reference Figure 1-2 , which is the second embodiment of the present invention, in order to further verify the beneficial effects of the present invention, experimental simulation comparison data of a circulating cooling water system integrating big data and artificial intelligence and a traditional method are provided.

[0132] The experimental scenario of the present invention is selected in the cooling water system of a large industrial park. The system includes multiple key components, including a cooling tower, a circulating water pump, a sensor network, etc.

[0133] The experiment is divided into two phases: the traditional system operation phase and the invented system operation phase. In order to ensure the accuracy and consistency of the data, the experimental preparation work includes installing sensors, setting monitoring parameters, and configuring the data acquisition and processing system.

[0134] First, a variety of water quality sensors are installed at key locations in the cooling system, including pH sensors, conductivity sensors, dissolved oxygen sensors, etc. In addition, temperature sensors are installed at the inlet, outlet and cooling tower of the cooling water, flow meters are installed in the main pipeline and bypass, and energy consumption sensors are installed on the water pump and cooling tower. The sampling frequency of all sensors is set to once every 5 minutes to ensure high-frequency data collection and real-time performance. A high-precision analog-to-digital converter is used to convert the analog signal of the sensor into a digital signal. The real-time monitoring dashboard displays the real-time values ​​and trend graphs of key parameters, and sets the normal operating range and alarm mechanism.

[0135] After collecting and preprocessing the data, the data from different sensors are merged into a unified dataset, and the differences in data collection time of different sensors are handled by the timestamp approximate merging method.

[0136] After the data processing is completed, the artificial intelligence prediction module is started. In order to improve the prediction accuracy of the model, the Bayesian optimization method is used to find the optimal hyperparameters, and the gradient boosting decision tree algorithm is used for optimization to prevent overfitting.

[0137] Finally, based on the future cooling demand forecast provided by the artificial intelligence prediction module, a multi-objective optimization function is defined, including minimization of energy consumption, water quality maintenance, and temperature control accuracy. The control strategy is optimized by using model predictive control (MPC) combined with particle swarm optimization (PSO); the optimized control strategy is parsed into specific execution instructions, and the control instructions are sent to each actuator through a network or bus system. The actuator performs corresponding operations according to the received instructions. Some experimental data of the present invention are as follows:

[0138] Table 1 Experimental data comparison table

[0139]

[0140] Based on the above data analysis, it can be concluded that the present invention has shown significant advantages in water quality maintenance, temperature control, flow stability and energy consumption reduction; compared with traditional systems, the invented system improves the intelligence level and operation efficiency of the system by integrating big data and artificial intelligence technology, and significantly improves the performance and reliability of the system.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A circulating cooling water system integrating big data and artificial intelligence, characterized in that: include: Information monitoring module, used to monitor parameter changes during unit operation; Data processing module, used to clean and integrate raw data; The artificial intelligence prediction module is used to predict future cooling needs based on historical data and current conditions; the optimization control module is used to formulate the optimal system operation strategy based on the prediction results; the execution module is used to apply the optimized control strategy to the actual system; the monitoring and alarm module is used to monitor the system operation status in real time and promptly alarm when abnormalities are found; Performance evaluation module, used to evaluate the energy efficiency of the system and provide a basis for long-term optimization; The output of the information monitoring module is connected to the input of the data processing module, the output of the data processing module is connected to the input of the artificial intelligence prediction module, the output of the artificial intelligence prediction module is connected to the input of the optimization control module, the output of the optimization control module is connected to the input of the execution module, the output of the execution module is connected to the input of the monitoring and alarm module, and the output of the monitoring and alarm module is connected to the input of the performance evaluation module.

2. The circulating cooling water system integrating big data and artificial intelligence as claimed in claim 1, characterized in that: The information monitoring module includes: Install various water quality sensors, temperature sensors, flow meters, and energy consumption sensors; set sampling frequencies and convert analog signals into digital signals; Set normal operating ranges for each parameter and trigger alarms when the parameters are out of range.

3. The circulating cooling water system integrating big data and artificial intelligence as claimed in claim 2, characterized in that: The data processing module includes the following contents: Detect outliers: Define normal ranges for different types of sensor data and check whether the sensor data is within the predefined normal range. If not, mark it as an anomaly. If it is, the interquartile range (IQR) method is used for secondary detection. If the data point falls below Q1-1.5IQR or above Q3+1.5IQR, it is considered abnormal, where Q3 is the upper quartile, Q1 is the lower quartile, and IQR is the inner moment; Merge data from different sensors and devices into a unified dataset. During the merging process, use the timestamp as the keyword and use the approximate merge_asof method to handle the differences in data collection time between different sensors.

4. The circulating cooling water system integrating big data and artificial intelligence as claimed in claim 3, characterized in that: The artificial intelligence prediction module includes the following contents: Collect the historical operation data of the system and the transmission data of the current data processing module, and normalize all the collected data to ensure the quality and consistency of the data: Among them, X is the original data, X min and X max are the minimum and maximum values ​​of the data respectively; The change rate feature is extracted from the preprocessed data, where the change rate is calculated as follows: Among them, X (t) is the data value at time t1, X (t1-1) is the data value at the previous time point; Train a separate XGBoost model for each prediction time horizon and each output parameter; Use Bayesian optimization method to find the best hyperparameters, use gradient boosting decision tree algorithm, and the objective function is mean square error; use early stopping method to prevent overfitting; The early stopping method to prevent overfitting includes: Set the initial best validation loss best val is positive infinity, sets the initial patience count to 0, and sets the patience value pat; after each training round: calculate the current verification loss val loss ; if val loss <best val , then update the best validation loss best val =val loss , and reset the patience count; otherwise, decrement the patience count; The validation loss formula is: Among them, n val is the number of samples in the validation set, L is the loss function, y i is the true value of the i-th validation set sample, is the predicted value of the i-th validation set sample.

5. The circulating cooling water system integrating big data and artificial intelligence as claimed in claim 4, characterized in that: The optimization control module includes the following contents: Define multi-objective optimization functions, including minimization of energy consumption, maintenance of water quality, and temperature control accuracy; The energy consumption is minimized as: E=∑P F ×T 0 +P P ×T 0 Among them, P F is the fan power, P P is the pump power, T 0 is the running time; The water quality maintenance is: WQ=ω1×|pH-pH tar |+ω2×|EC-EC tar | Where pH is pH value, EC is conductivity, ω1 and ω2 are weight coefficients; The temperature control accuracy is: TC=∑|T act -T set | Among them, T act is the actual water temperature, T set To set the temperature; The comprehensive objective function is: F=αE+βWQ+γTC Among them, α, β, and γ are the weight coefficients of each sub-goal; The set constraints include: cooling tower fan speed range, circulating water pump flow range, water temperature set point range and system cooling capacity constraints.

6. The circulating cooling water system integrating big data and artificial intelligence as claimed in claim 5, characterized in that: The optimization control module also includes: The control strategy is optimized by using model predictive control (MPC) combined with particle swarm optimization (PSO), including the following: The MPC prediction model is: x(k+1)=Ax(k)+Bu(k) y(k)=Cx(k) PSO update formula: v i (t+1)=ω0.v i (t)+c1.r1.(p i -x i (t))+c2.r2.(g-x i (t)) x i (t+1)=x i (t)+v i (t+1) Among them, x is the system state, u is the control input, y is the system output, A, B, C are the system matrices, and v i is the particle velocity, x i is the particle position, p i is the individual optimal solution, g is the global optimal solution, ω0 is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random numbers; The control strategy optimization process is: Initialize the particle swarm, where each particle represents a set of control parameters, including fan speed, pump flow, and temperature set point. For each time step in the prediction domain: Use MPC models to predict system responses; Calculate the objective function value; Update individual optimal solutions and global optimal solutions; Use the PSO algorithm to update the particle position and velocity, and repeat the above steps until the maximum number of iterations is reached or the convergence condition is met.

7. The circulating cooling water system integrating big data and artificial intelligence as claimed in claim 6, characterized in that: The optimization control module also includes: Check whether the optimization result meets all constraints. If not, use the penalty function method to correct the result: Among them, F is the original objective function, μ is the penalty parameter, and gi(x) is the i-th constraint.

8. The circulating cooling water system integrating big data and artificial intelligence as claimed in claim 6, characterized in that: The performance evaluation module includes calculating energy efficiency indicators and generating optimization effect reports; The energy utilization efficiency index includes an energy efficiency index and a temperature control accuracy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the circulating cooling water system integrating big data and artificial intelligence described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the circulating cooling water system integrating big data and artificial intelligence described in any one of claims 1 to 8 are implemented.

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