Chiller self-correction model system

Abstract

The invention discloses a self-correcting model system for a chiller, comprising: a chiller data module, for storing historical operation data of the chiller; a cooling tower data module, for storing historical operation data of the cooling tower, wherein different cooling tower inlet water temperatures in the historical operation data have corresponding outlet water temperatures and cooling efficiencies; a dynamic optimization module, for determining the dynamic equilibrium relationship between the cooling tower input power and the chiller input power according to the correction model in combination with outdoor weather and a characteristic curve of the cooling tower, and completing dynamic optimization; a self-adjusting module, for formulating a control strategy according to the dynamic equilibrium relationship and the optimization result fed back by the dynamic optimization module; the beneficial effects of the invention are as follows: determining the dynamic equilibrium relationship between the cooling tower input power and the chiller input power in combination with the outdoor weather and the characteristic curve of the cooling tower, not only can realize the dynamic optimization of the chiller, but also help to improve the energy efficiency, stability and service life of the system.

Description

Technical Field

[0001] The invention relates to the field of chiller models, in particular to a chiller self-correcting model system. Background Art

[0002] A chiller is a refrigeration equipment used in air conditioning, industrial refrigeration and other occasions. It can absorb heat to cool water to achieve the purpose of lowering the air temperature. A cooling tower is a device used to dissipate heat in a refrigeration system. Its main function is to release the heat absorbed in the refrigeration system through the transfer of water and air, so as to maintain the normal operation of the refrigeration system. Conventional chillers often use a fixed cooling water temperature under design conditions, that is, the cooling water inlet temperature of the chiller is a constant value, usually 30°C or 32°C. The maximum heat dissipation of the cooling tower matched with it is also based on ensuring that the cooling water inlet temperature is 30°C or 32°C when the chiller is fully loaded. However, in actual applications, the chiller is mostly not fully loaded, and its configured cooling tower still has surplus, and the cooling water temperature that can be achieved is also lower than the design value. The reduction in cooling water temperature can improve the energy efficiency of the chiller. Therefore, when the cooling water temperature is controlled according to the design water temperature, the operating energy consumption of the chiller is large, which is not conducive to the energy saving of the unit. Summary of the invention

[0003] The purpose of the present invention is to solve the problem that the cooling water temperature is designed to be high, resulting in high operating energy consumption of the chiller, which is not conducive to energy saving of the unit. The system lacks relevant cooling efficiency evaluation function and cannot timely discover the cooling efficiency problem of the cooling tower. Therefore, a chiller self-correction model system is proposed.

[0004] The purpose of the present invention can be achieved by the following technical solution: A chiller self-correction model system, comprising:

[0005] The chiller data module is used to store the historical operation data of the chiller. Different cooling water temperatures in the historical operation data have corresponding unit load percentages and unit COPs. The historical records of unit load percentages and unit COPs at the same cooling water temperature are divided into the same group of cooling water temperatures. According to each group of historical data, the corresponding load percentage unit COP curve is fitted;

[0006] The cooling tower data module is used to store the historical operation data of the cooling tower. In the historical operation data, different cooling tower inlet water temperatures have corresponding outlet water temperatures and cooling efficiency.

[0007] The dynamic optimization module determines the dynamic balance relationship between the cooling tower input power and the chiller input power based on the modified model, combined with the outdoor weather and the characteristic curve of the cooling tower, and completes the dynamic optimization;

[0008] The self-adjusting module formulates the control strategy according to the dynamic balance relationship and optimization results fed back by the dynamic optimization module.

[0009] Furthermore, the dynamic optimization expression is based on the relationship curve between the unit COP and the unit load range under a certain cooling water temperature of the chiller, and the mathematical model structure of the curve is Y=A*X 2 +B*X+C, where Y is the unit COP, X is the unit load percentage, and A, B, and C are correction factors;

[0010] By adjusting the number and frequency of cooling towers, the cooling water temperature is controlled to be stable at different temperatures. The constant chilled water temperature, temperature difference and flow rate are all fixed values. The number of chillers or the air conditioning terminal load is adjusted to make the chiller obtain the unit COP at a fixed cooling water temperature under three different load percentages. That is, when X has three different values, it corresponds to three Y values. Through three sets of data, Y=A*X is obtained. 2 +B*X+C, the specific values ​​of A, B, and C, and the final Y=A*X at different cooling water temperatures 2 +B*X+C is the preliminary mathematical model of the chiller at different cooling water temperatures;

[0011] According to the preliminary mathematical model, the preliminary energy-saving control of the chiller is carried out. Combined with the outdoor temperature and humidity and the proximity of the cooling tower, the optimal cooling water temperature of the refrigeration unit under different weather conditions is determined, and then Y=A*X 2 +B*X+C preliminary mathematical model to control the operating load of the unit;

[0012] According to the historical operation data of the chiller, the corresponding load percentage COP curve is fitted, and according to each set of curves, Y=A*X is corrected at different cooling water temperatures. 2 The A, B, and C coefficients of +B*X+C are self-corrected based on the data combined with the imported preliminary mathematical model.

[0013] According to the revised model, combined with the outdoor weather and the characteristic curve of the cooling tower, the dynamic balance relationship between the cooling tower input power and the chiller input power is determined to complete the dynamic optimization of the chiller.

[0014] Furthermore, the dynamic equilibrium relationship is determined as follows:

[0015] Data collection, collecting outdoor weather data, including temperature and humidity, and cooling tower characteristic curve data including cooling tower inlet water temperature, outlet water temperature, and cooling efficiency;

[0016] Data processing, based on the collected data, calculate the cooling efficiency of the cooling tower under different weather conditions, and at the same time, calculate the COP of the chiller under different weather conditions;

[0017] 3) According to the cooling efficiency of the cooling tower and the COP of the chiller under different weather conditions, determine the dynamic balance relationship between the cooling tower input power and the chiller input power.

[0018] Furthermore, the control strategy formulated by the self-regulating module is as follows:

[0019] Adjust the chiller's operating status according to load changes;

[0020] Set the cooling tower fan to run at a fixed speed;

[0021] Obtain the working status of chillers and cooling towers in real time, and perform alarm processing based on temperature, pressure, and flow;

[0022] The simulated annealing optimization algorithm is used to find the parameters for the minimum energy consumption and optimal performance of the system under a specific load.

[0023] Furthermore, the method of performing alarm processing based on temperature, pressure and flow is to obtain the temperature, pressure and flow values ​​of the chiller and the cooling tower in real time, and perform corresponding numerical comparison according to the preset warning values ​​of the chiller and the cooling tower; if the temperature value of the chiller exceeds the temperature warning value of the chiller, a chiller temperature alarm is issued; if the pressure value of the chiller exceeds the pressure warning value of the chiller, a chiller pressure alarm is issued; if the flow value of the chiller exceeds the flow warning value of the chiller, a chiller flow alarm is issued; if the temperature value of the cooling tower exceeds the temperature warning value of the cooling tower, a cooling tower temperature alarm is issued; if the pressure value of the cooling tower exceeds the pressure warning value of the cooling tower, a cooling tower pressure alarm is issued; if the flow value of the cooling tower exceeds the flow warning value of the cooling tower, a cooling tower flow alarm is issued.

[0024] Furthermore, the system also includes a data backup module, which is used to back up data of the chiller data module and the cooling tower data module.

[0025] Furthermore, the data backup module is also used to record the alarm content fed back by the self-adjusting module, and the alarm content includes chiller temperature alarm, chiller pressure alarm, chiller flow alarm, cooling tower temperature alarm, cooling tower pressure alarm, and cooling tower flow alarm.

[0026] Furthermore, the system also includes a cooling efficiency evaluation module, which is used for cooling efficiency evaluation processing of the cooling tower. Specifically, the temperature of the cooling water before entering the cooling tower, the water inlet flow rate, the temperature of the cooling water when leaving the cooling tower, and the water outlet flow rate are obtained, and the cooling efficiency is calculated according to the formula. The formula is cooling efficiency = [(water inlet temperature - water outlet temperature) / water inlet temperature] × 100%, and the above steps are repeated N times to obtain an average value U of the N times of data. The average value U is determined according to a preset standard K. If U < K, the cooling efficiency of the cooling tower is determined to be in an abnormal state. If U ≥ K, the efficiency of the cooling tower is determined to be in a normal state.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] It can determine the dynamic balance relationship between the chiller and the cooling tower according to the outdoor weather and the characteristic curve of the cooling tower, and can effectively adjust the input power of the chiller, so as to achieve the optimal energy-saving effect of the system and improve the performance of the chiller. Through dynamic optimization, it can ensure that the cooling tower provides sufficient cooling water, so as to improve the cooling efficiency and performance of the chiller. The dynamic balance relationship can ensure that the energy consumption of the cooling tower matches the chiller, avoiding system instability or equipment damage caused by power imbalance. It is flexible and adaptable, and can be combined with outdoor weather conditions. The system can flexibly adjust the power of the chiller and the cooling tower according to actual needs, making it more adaptable to different working environments and application needs;

[0029] Extending the life of the equipment. Through dynamic optimization, excessive operation or frequent start and stop of the equipment can be avoided, thereby extending the service life of the equipment and reducing maintenance costs. By optimizing the model and accurately controlling the input power of the chiller and cooling tower, a more accurate and efficient model can be established, making the operation of the entire system more stable and economical. Therefore, by combining the outdoor weather and the characteristic curve of the cooling tower to determine the dynamic balance relationship between the input power of the cooling tower and the input power of the chiller, not only can the dynamic optimization of the chiller be achieved, but it also helps to improve the energy efficiency, stability and service life of the system;

[0030] It is possible to obtain the working status of the chiller and the cooling tower in real time, and perform alarm processing based on temperature, pressure, and flow, in the following way: obtain the temperature, pressure, and flow values ​​of the chiller and the cooling tower in real time, and perform corresponding numerical comparison according to the preset warning values ​​of the chiller and the cooling tower. If the temperature value of the chiller exceeds the temperature warning value of the chiller, a chiller temperature alarm is issued; if the pressure value of the chiller exceeds the pressure warning value of the chiller, a chiller pressure alarm is issued; if the flow value of the chiller exceeds the flow warning value of the chiller, a chiller flow alarm is issued; if the temperature value of the cooling tower exceeds the temperature warning value of the cooling tower, a cooling tower temperature alarm is issued; if the pressure value of the cooling tower exceeds the pressure warning value of the cooling tower, a cooling tower pressure alarm is issued; if the flow value of the cooling tower exceeds the flow warning value of the cooling tower, a cooling tower flow alarm is issued;

[0031] The system also includes a cooling efficiency evaluation module, which is used for cooling efficiency evaluation processing of the cooling tower. According to the evaluation results, the operating parameters of the cooling tower can be adjusted or optimized to improve its cooling efficiency, save energy and improve the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0033] Figure 1 It is a system composition diagram of an embodiment of the chiller self-correcting model system of the present invention. DETAILED DESCRIPTION

[0034] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] See also Figure 1 As shown, the chiller self-correction model system includes:

[0036] The chiller data module is used to store the historical operation data of the chiller. Different cooling water temperatures in the historical operation data have corresponding unit load percentages and unit COPs. The historical records of unit load percentages and unit COPs at the same cooling water temperature are divided into the same group of cooling water temperatures. According to each group of historical data, the corresponding load percentage unit COP curve is fitted;

[0037] The cooling tower data module is used to store the historical operation data of the cooling tower. In the historical operation data, different cooling tower inlet water temperatures have corresponding outlet water temperatures and cooling efficiency.

[0038] The dynamic optimization module determines the dynamic balance relationship between the cooling tower input power and the chiller input power based on the modified model, combined with the outdoor weather and the characteristic curve of the cooling tower, and completes the dynamic optimization;

[0039] The self-adjusting module formulates the control strategy according to the dynamic balance relationship and optimization results fed back by the dynamic optimization module.

[0040] Through the above scheme, the dynamic balance relationship between the chiller and the cooling tower can be determined according to the outdoor weather and the characteristic curve of the cooling tower, and the input power of the chiller can be effectively adjusted to achieve the optimal energy-saving effect of the system and improve the performance of the chiller.

[0041] The dynamic optimization expression is based on the relationship curve between the unit COP and the unit load range under the condition of constant cooling water temperature, that is, when the chilled water temperature, temperature difference and flow rate are all constant values, the mathematical model structure of the curve is Y=A*X 2 +B*X+C, where Y is the unit COP, X is the unit load percentage, and A, B, and C are correction factors;

[0042] By adjusting the number and frequency of cooling towers, the cooling water temperature is controlled to be stable at different temperatures. The constant chilled water temperature, temperature difference and flow rate are all fixed values. The number of chillers or the air conditioning terminal load is adjusted to make the chiller obtain the unit COP at a fixed cooling water temperature under three different load percentages. That is, when X has three different values, it corresponds to three Y values. Through three sets of data, Y=A*X is obtained. 2 +B*X+C, the specific values ​​of A, B, and C, and the final Y=A*X at different cooling water temperatures 2 +B*X+C is the preliminary mathematical model of the chiller at different cooling water temperatures;

[0043] According to the preliminary mathematical model, the preliminary energy-saving control of the chiller is carried out. Combined with the outdoor temperature and humidity and the proximity of the cooling tower, the optimal cooling water temperature of the refrigeration unit under different weather conditions is determined, and then Y=A*X 2 +B*X+C preliminary mathematical model, controls the unit's operating load in the high energy efficiency range, that is, not less than 3.6, so as to achieve the purpose of energy saving;

[0044] According to the historical operation data of the chiller, the corresponding load percentage COP curve is fitted, and according to each set of curves, Y=A*X is corrected at different cooling water temperatures. 2 The A, B, and C coefficients of +B*X+C are self-corrected based on the data combined with the imported preliminary mathematical model.

[0045] According to the revised model, combined with the outdoor weather and the characteristic curve of the cooling tower, the dynamic balance relationship between the input power of the cooling tower and the input power of the chiller is determined to complete the dynamic optimization of the chiller. Once the dynamic balance relationship is determined, the input power of the cooling tower can be adjusted in real time to achieve the dynamic optimization of the chiller. When the outdoor temperature rises, the input power of the cooling tower can be increased to ensure the stable operation and efficient cooling of the chiller. Conversely, when the outdoor temperature drops, the input power of the cooling tower can be reduced to save energy.

[0046] The dynamic equilibrium relationship is determined as follows:

[0047] Data collection, collecting outdoor weather data, including temperature and humidity, and cooling tower characteristic curve data including cooling tower inlet water temperature, outlet water temperature, and cooling efficiency;

[0048] Data processing, based on the collected data, calculate the cooling efficiency of the cooling tower under different weather conditions, and at the same time, calculate the COP of the chiller under different weather conditions;

[0049] 3) According to the cooling efficiency of the cooling tower and the COP of the chiller under different weather conditions, determine the dynamic balance relationship between the cooling tower input power and the chiller input power.

[0050] The control strategy formulated by the self-regulating module is as follows:

[0051] Adjust the chiller's operating status according to load changes;

[0052] Set the cooling tower fan to run at a fixed speed;

[0053] Obtain the working status of chillers and cooling towers in real time, and perform alarm processing based on temperature, pressure, and flow;

[0054] Use simulated annealing optimization algorithm to find the parameters of minimum energy consumption and optimal performance of the system under specific load;

[0055] Through the above scheme, the dynamic balance relationship between the chiller and the cooling tower can be determined according to the outdoor weather and the characteristic curve of the cooling tower, and the input power of the chiller can be effectively adjusted to achieve the optimal energy-saving effect of the system and improve the performance of the chiller. Through dynamic optimization, it can be ensured that the cooling tower provides sufficient cooling water, thereby improving the cooling efficiency and performance of the chiller. The dynamic balance relationship can ensure that the energy consumption of the cooling tower matches the chiller, avoiding system instability or equipment damage caused by power imbalance. It has flexibility and adaptability. Combined with outdoor weather conditions, the system can flexibly adjust the power of the chiller and the cooling tower according to actual needs, making it more adaptable to different working environments and application needs.

[0056] The life of the equipment is extended. Through dynamic optimization, excessive operation or frequent start and stop of the equipment can be avoided, thereby extending the service life of the equipment and reducing maintenance costs. The model is optimized. By accurately controlling the input power of the chiller and the cooling tower, a more accurate and efficient model can be established to make the operation of the entire system more stable and economical. Therefore, by combining the outdoor weather and the characteristic curve of the cooling tower to determine the dynamic balance relationship between the cooling tower input power and the chiller input power, not only can the dynamic optimization of the chiller be achieved, but it also helps to improve the energy efficiency, stability and service life of the system.

[0057] The method of performing alarm processing based on temperature, pressure and flow is to obtain the temperature, pressure and flow values ​​of the chiller and the cooling tower in real time, and perform corresponding numerical comparison according to the preset warning values ​​of the chiller and the cooling tower. If the temperature value of the chiller exceeds the temperature warning value of the chiller, a chiller temperature alarm is issued, if the pressure value of the chiller exceeds the pressure warning value of the chiller, a chiller pressure alarm is issued, if the flow value of the chiller exceeds the flow warning value of the chiller, a chiller flow alarm is issued, if the temperature value of the cooling tower exceeds the temperature warning value of the cooling tower, a cooling tower temperature alarm is issued, if the pressure value of the cooling tower exceeds the pressure warning value of the cooling tower, a cooling tower pressure alarm is issued, if the flow value of the cooling tower exceeds the flow warning value of the cooling tower, a cooling tower flow alarm is issued, and the alarm method includes local system alarm and alarm to the mobile terminal through the network. The alarm method can be selected and defined according to actual usage needs.

[0058] The system also includes a data backup module, which is used to back up the data of the chiller data module and the cooling tower data module. The data is saved for nearly one year and will be automatically overwritten after the time limit is exceeded.

[0059] The data backup module is also used to record the alarm content fed back by the self-regulating module, and the alarm content includes chiller temperature alarm, chiller pressure alarm, chiller flow alarm, cooling tower temperature alarm, cooling tower pressure alarm, and cooling tower flow alarm.

[0060] The system also includes a cooling efficiency evaluation module, which is used for evaluating the cooling efficiency of the cooling tower. Specifically, the temperature of the cooling water before it enters the cooling tower is obtained, the water inlet flow rate is obtained, the temperature of the cooling water when it leaves the cooling tower is obtained, and the water outlet flow rate is obtained. The cooling efficiency is calculated according to the formula, which is cooling efficiency = [(water inlet temperature - water outlet temperature) / water inlet temperature] × 100%. The above steps are repeated N times to obtain the average value U of the N data. The average value U is determined according to the preset standard K. If U < K, the cooling efficiency of the cooling tower is determined to be in an abnormal state. If U ≥ K, the cooling tower efficiency is determined to be in a normal state. According to the evaluation results, the operating parameters of the cooling tower can be adjusted or optimized to improve its cooling efficiency, save energy, and improve the overall performance of the system.

[0061] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The chiller self-correction model system is characterized by: include: The chiller data module is used to store the historical operation data of the chiller. Different cooling water temperatures in the historical operation data have corresponding unit load percentages and unit COPs. The historical records of unit load percentages and unit COPs at the same cooling water temperature are divided into the same group of cooling water temperatures. According to each group of historical data, the corresponding load percentage unit COP curve is fitted; The cooling tower data module is used to store the historical operation data of the cooling tower. In the historical operation data, different cooling tower inlet water temperatures have corresponding outlet water temperatures and cooling efficiency. The dynamic optimization module determines the dynamic balance relationship between the cooling tower input power and the chiller input power based on the modified model, combined with the outdoor weather and the characteristic curve of the cooling tower, and completes the dynamic optimization; The self-adjusting module formulates the control strategy according to the dynamic balance relationship and optimization results fed back by the dynamic optimization module; The dynamic optimization expression is based on the relationship curve between the unit COP and the unit load range under a certain cooling water temperature of the chiller, and the mathematical model structure of the curve is Y=A*X 2 +B*X+C, where Y is the unit COP, X is the unit load percentage, and A, B, and C are correction factors; By adjusting the number and frequency of cooling towers, the cooling water temperature is controlled to be stable at different temperatures. The constant chilled water temperature, temperature difference and flow rate are all fixed values. The number of chillers or the air conditioning terminal load is adjusted to make the chiller obtain the unit COP at a fixed cooling water temperature under three different load percentages. That is, when X has three different values, it corresponds to three Y values. Through three sets of data, Y=A*X is obtained. 2 +B*X+C, the specific values ​​of A, B, and C, and the final Y=A*X at different cooling water temperatures 2 +B*X+C is the preliminary mathematical model of the chiller at different cooling water temperatures; According to the preliminary mathematical model, the preliminary energy-saving control of the chiller is carried out. Combined with the outdoor temperature and humidity and the proximity of the cooling tower, the optimal cooling water temperature of the refrigeration unit under different weather conditions is determined, and then Y=A*X 2 +B*X+C preliminary mathematical model to control the operating load of the unit; According to the historical operation data of the chiller, the corresponding load percentage COP curve is fitted, and according to each set of curves, Y=A*X is corrected at different cooling water temperatures. 2 +B*X+C's A, B, and C coefficients are self-corrected based on the data combined with the imported preliminary mathematical model; According to the revised model, combined with the outdoor weather and the characteristic curve of the cooling tower, the dynamic balance relationship between the cooling tower input power and the chiller input power is determined to complete the dynamic optimization of the chiller.

2. The chiller self-correction model system according to claim 1, characterized in that: The dynamic equilibrium relationship is determined as follows: (1) Data collection: collect outdoor weather data, including temperature and humidity, and cooling tower characteristic curve data, including cooling tower inlet water temperature, outlet water temperature, and cooling efficiency; (2) Data processing: Based on the collected data, the cooling efficiency of the cooling tower under different weather conditions is calculated. At the same time, the COP of the chiller under different weather conditions is calculated. (3) Determine the dynamic balance relationship between the cooling tower input power and the chiller input power based on the cooling efficiency of the cooling tower and the COP of the chiller under different weather conditions.

3. The chiller self-correcting model system according to claim 2, characterized in that: The control strategy formulated by the self-regulating module is as follows: Adjust the working status of the chiller according to load changes; Set the cooling tower fan to run at a fixed speed; Obtain the working status of chillers and cooling towers in real time, and perform alarm processing based on temperature, pressure, and flow; The simulated annealing optimization algorithm is used to find the parameters for the minimum energy consumption and optimal performance of the system under a specific load.

4. The chiller self-correcting model system according to claim 3, characterized in that: The method for performing alarm processing based on temperature, pressure and flow is to obtain the temperature, pressure and flow values ​​of the chiller and the cooling tower in real time, and perform corresponding numerical comparison according to the preset warning values ​​of the chiller and the cooling tower. If the temperature value of the chiller exceeds the temperature warning value of the chiller, a chiller temperature alarm is issued; if the pressure value of the chiller exceeds the pressure warning value of the chiller, a chiller pressure alarm is issued; if the flow value of the chiller exceeds the flow warning value of the chiller, a chiller flow alarm is issued; if the temperature value of the cooling tower exceeds the temperature warning value of the cooling tower, a cooling tower temperature alarm is issued; if the pressure value of the cooling tower exceeds the pressure warning value of the cooling tower, a cooling tower pressure alarm is issued; if the flow value of the cooling tower exceeds the flow warning value of the cooling tower, a cooling tower flow alarm is issued.

5. The chiller self-correcting model system according to claim 4, characterized in that: The system also includes a data backup module, which is used to back up data in the chiller data module and the cooling tower data module.

6. The chiller self-correcting model system according to claim 5, characterized in that: The data backup module is also used to record the alarm content fed back by the self-regulating module, and the alarm content includes chiller temperature alarm, chiller pressure alarm, chiller flow alarm, cooling tower temperature alarm, cooling tower pressure alarm, and cooling tower flow alarm.

7. The chiller self-correcting model system according to claim 6, characterized in that: The system also includes a cooling efficiency evaluation module, which is used for evaluating the cooling efficiency of the cooling tower. Specifically, the temperature of the cooling water before it enters the cooling tower, the water inlet flow rate, the temperature of the cooling water when it leaves the cooling tower, and the water outlet flow rate are obtained. The cooling efficiency is calculated according to the formula: cooling efficiency = [(water inlet temperature - water outlet temperature) / water inlet temperature] × 100%. The above steps are repeated N times to obtain the average value U of the N times of data. The average value U is determined according to the preset standard K. If U < K, the cooling efficiency of the cooling tower is determined to be in an abnormal state. If U ≥ K, the efficiency of the cooling tower is determined to be in a normal state.

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