Cooling tower operation state monitoring method and system based on big data analysis

Abstract

The invention discloses a cooling tower operation state monitoring method and system based on big data analysis, and relates to the technical field of data processing, and the method comprises the steps: obtaining water temperature data of an inlet and an outlet of a cooling tower, calculating a cooling temperature difference, and carrying out the cooling efficiency evaluation based on a time sequence temperature relation; analyzing the relationship between the control parameters of each level of the cooling tower in combination with historical operation data and a field big data sample, and establishing a level control analysis path; and on the basis of the cooling target of the heat source equipment, analyzing control parameters of each level through the time sequence cooling efficiency, identifying the running state of the cooling tower, and sending state feedback information. The technical problems that existing cooling tower operation state monitoring depends on simple static measurement, dynamic optimization and real-time adjustment of control parameters of all levels are lacked, and the operation efficiency of the cooling tower is affected are solved, and the purposes that the control parameters are dynamically optimized in real time through big data analysis and multi-level control analysis, and the operation efficiency of the cooling tower is improved are achieved. And the operation efficiency of the cooling tower is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method and system for monitoring the operating state of a cooling tower based on big data analysis. Background Art

[0002] The operating efficiency of traditional cooling towers usually relies on devices such as temperature sensors and flow meters for local monitoring. However, these methods often cannot comprehensively consider the complexity of the cooling tower system, especially the interaction between control parameters and external environmental factors. The existing technologies mainly rely on static parameters and single control methods to adjust the operation of the cooling tower, lacking dynamic optimization and real-time adjustment of control parameters at all levels. In addition, the existing evaluation of cooling efficiency usually relies on simple static measurements, failing to effectively achieve real-time monitoring and automatic adjustment of the performance of the cooling tower. Summary of the Invention

[0003] This application provides a method and system for monitoring the operating state of a cooling tower based on big data analysis, which is used to solve the technical problem that the monitoring of the operating state of the cooling tower in the existing technology relies on simple static measurements, lacks dynamic optimization and real-time adjustment of control parameters at all levels, and affects the operating efficiency of the cooling tower.

[0004] In the first aspect of this application, a method for monitoring the operating state of a cooling tower based on big data analysis is provided. The method includes: obtaining the temperature of the heat source water at the inlet of the cooling tower and the temperature of the cooling water at the outlet of the cooling tower, and calculating the cooling temperature difference by using the temperature of the heat source water and the temperature of the cooling water; establishing a time-series temperature relationship of the heat source cooling process based on the temperature of the heat source water, the temperature of the cooling water, and the cooling temperature difference, and performing a cooling efficiency evaluation to obtain the time-series cooling efficiency; analyzing the control relationship between the control parameters at all levels in the cooling tower through historical operation data and on-site big data samples, and establishing a hierarchical control analysis path; taking the operating cooling target of the heat source device as the target value, parsing the control parameters at all levels through the hierarchical control analysis path based on the time-series cooling efficiency, and identifying the operating state of the cooling tower; and sending a status feedback message according to the operating state of the cooling tower.

[0005] In a second aspect of the present application, a monitoring system for the operating state of a cooling tower based on big data analysis is provided. The system includes: a cooling temperature difference calculation module, which is used to obtain the temperature of the heat source water at the inlet of the cooling tower and the temperature of the cooling water at the outlet of the cooling tower, and calculate the cooling temperature difference by using the temperature of the heat source water and the temperature of the cooling water; a cooling efficiency evaluation module, which is used to establish a time-series temperature relationship of the heat source cooling process based on the temperature of the heat source water, the temperature of the cooling water, and the cooling temperature difference, and perform a cooling efficiency evaluation to obtain the time-series cooling efficiency; a hierarchical control parsing path establishment module, which is used to analyze the control relationship between the hierarchical control parameters in the cooling tower through historical operation data and on-site big data samples, and establish a hierarchical control parsing path; a cooling tower operating state recognition module, which is used to take the operating cooling target of the heat source device as the target value, and parse the hierarchical control parameters through the hierarchical control parsing path based on the time-series cooling efficiency to identify the operating state of the cooling tower; a state feedback module, which is used to send state feedback information according to the operating state of the cooling tower.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages: The monitoring method and system for the operating state of a cooling tower based on big data analysis provided by the present application relate to the technical field of data processing. By obtaining the water temperature data at the inlet and outlet of the cooling tower, calculating the cooling temperature difference and evaluating the cooling efficiency, analyzing the control relationship between the hierarchical control parameters by combining historical data and on-site samples, establishing a hierarchical control parsing path, based on the cooling target of the heat source device, parsing the time-series cooling efficiency, adjusting the control parameters, identifying the operating state of the cooling tower, and sending state feedback information, it solves the technical problem that the monitoring of the operating state of the cooling tower in the prior art relies on simple static measurement, lacks dynamic optimization and real-time adjustment of the hierarchical control parameters, and affects the operating efficiency of the cooling tower, and realizes the technical effect of real-time dynamically optimizing the control parameters through big data analysis and multi-level control parsing, and improving the operating efficiency of the cooling tower. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0008] Figure 1 It is a schematic flow chart of the monitoring method for the operating state of a cooling tower based on big data analysis provided by the embodiments of the present application; Figure 2Schematic diagram of the cooling tower operation status monitoring system based on big data analysis provided by the embodiments of the present application.

[0009] Explanation of reference numerals: Cooling temperature difference calculation module 11, cooling efficiency evaluation module 12, hierarchical control analysis path establishment module 13, cooling tower operation status recognition module 14, status feedback module 15. Detailed implementation manners

[0010] The present application provides a method and system for monitoring the operation status of a cooling tower based on big data analysis, which is used to solve the technical problem that the monitoring of the operation status of the existing cooling tower depends on simple static measurement, lacks dynamic optimization and real-time adjustment of control parameters at each level, and affects the operation efficiency of the cooling tower.

[0011] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0012] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned accompanying drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such used data may be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices.

[0013] Embodiment 1, as Figure 1 shown, the present application provides a method for monitoring the operation status of a cooling tower based on big data analysis, and the method includes: P10: Obtain the heat source water temperature at the inlet of the cooling tower and the cooling water temperature at the outlet of the cooling tower, and calculate the cooling temperature difference by using the heat source water temperature and the cooling water temperature.

[0014] Specifically, the temperature sensors installed at the inlet and outlet of the cooling tower are used to obtain the temperature of the heat source water and the cooling water in real time. The temperature of the heat source water refers to the water temperature entering the cooling tower, usually from the cooling circulation system of industrial equipment or power plants, while the cooling water temperature refers to the water temperature discharged from the tower after the water is cooled by the cooling tower. Among them, thermocouples or RTD sensors can be used as the temperature sensors, which can measure the water temperature with high precision. And the layout of the sensors needs to ensure accurate collection of the temperature data of the heat source water and the cooling water, and the inlet and outlet positions will be reasonably arranged according to the design of the cooling tower and the fluid flow characteristics.

[0015] By monitoring these temperature data in real time, the cooling temperature difference can be calculated. The calculation formula can be: Cooling temperature difference = Heat source water temperature - Cooling water temperature. This calculation result reflects the cooling effect of the cooling tower. If the cooling temperature difference is large, it means that the cooling tower has a good cooling effect during this period, and the heat is effectively transferred and released. On the contrary, if the cooling temperature difference is small, it may indicate that the performance of the cooling tower is low and the cooling effect is poor, and maintenance or adjustment may be required.

[0016] The calculation of the cooling temperature difference not only provides data support for evaluating the working efficiency of the cooling tower, but also provides a basis for subsequent performance optimization and fault diagnosis. For example, if the cooling temperature difference suddenly decreases, it may mean that the cooling capacity of the cooling tower is affected by certain factors, such as fan failure, insufficient cooling water flow or equipment aging. At this time, system automation control or manual detection is required to determine the cause and take measures.

[0017] To ensure the accuracy of this process, the temperature sensors need to have good stability and high precision, and be able to resist interference and work stably for a long time during operation. Therefore, it is crucial to select the appropriate sensor type and installation location. Usually, the sensors are connected to the data acquisition system, and the data is transmitted to the control center in real time, so as to monitor the working status of the cooling tower at any time and make dynamic adjustments according to the real-time data.

[0018] In summary, this step realizes the real-time monitoring of the performance of the cooling tower by collecting the temperatures at the inlet and outlet of the cooling tower and calculating the cooling temperature difference. The accurate execution of this process not only helps to judge the operating status of the cooling tower, but also provides key data for subsequent optimization and maintenance.

[0019] P20: Based on the heat source water temperature, the cooling water temperature, and the cooling temperature difference, establish the chronological temperature relationship of the heat source cooling process, evaluate the cooling efficiency, and obtain the chronological cooling efficiency.

[0020] Furthermore, step P20 of the embodiment of the present application further includes: P21: Arrange the heat source water temperature, cooling water temperature, and cooling temperature difference in time series according to the acquisition timestamp; P22: Calculate the cooling efficiency based on the cooling temperature difference and the heat source water temperature; P23: Align the cooling efficiency in time according to the time series arrangement order to obtain the time series cooling efficiency.

[0021] It should be understood that by analyzing the time series relationship of the heat source water temperature, cooling water temperature, and cooling temperature difference, a dynamic model of the heat source cooling process is established, and the cooling efficiency is evaluated accordingly, and finally the time series cooling efficiency is obtained. This process can not only reflect the cooling performance of the cooling tower at different time points, but also provide important quantitative indicators for subsequent operating state identification.

[0022] Specifically, first, arrange the heat source water temperature, cooling water temperature, and cooling temperature difference in time series according to the acquisition timestamp. The acquisition timestamp refers to the specific time when each temperature data point is recorded, providing a time reference for the time series analysis of the data. By arranging the data in time series, the changing trends of these temperature parameters over time can be clearly observed. This process requires the data acquisition system to have a high-precision time synchronization function to ensure that the data collected by different sensors can be accurately corresponding to the same time point, avoiding analysis errors caused by time deviation. For example, if the sensor acquisition frequency is once per minute, the timestamp of each data point should be accurate to the second to ensure the accuracy and reliability of the data.

[0023] After completing the time series arrangement, calculate the cooling efficiency based on the cooling temperature difference and the heat source water temperature. The cooling efficiency is an important indicator to measure the performance of the cooling tower, reflecting the ability of the cooling tower to cool the heat source water to the target temperature within a unit time. It can be calculated through the proportional relationship between the cooling effect of the cooling tower (i.e., the cooling temperature difference) and the temperature of the heat source water. The specific calculation formula can be: Cooling efficiency = Cooling temperature difference / Heat source water temperature. Among them, the cooling temperature difference represents the cooling effect of the cooling tower, and the heat source water temperature represents the initial temperature of the water input to the cooling tower. This formula quantifies the cooling ability of the cooling tower by comparing the actual cooling temperature difference of the cooling tower with the initial temperature of the heat source water. The higher the cooling efficiency, the better the cooling effect of the cooling tower; on the contrary, a lower cooling efficiency may imply problems in the operation of the cooling tower, such as insufficient cooling water flow, aging of cooling tower components, etc. In actual operation, the calculation of the cooling efficiency should be carried out for the data of each time point to ensure the dynamics and accuracy of the results.

[0024] Finally, arrange the calculated cooling efficiency in chronological order to obtain the chronological cooling efficiency. The chronological cooling efficiency is a dynamic indicator that represents the change in the cooling efficiency of the cooling tower at different time points. By arranging the cooling efficiency in chronological order, a chronological cooling efficiency curve can be obtained, enabling real-time monitoring and dynamic analysis of the operating status of the cooling tower. For example, if there is an obvious downward trend in the chronological cooling efficiency curve during a certain period, it may indicate that the cooling capacity of the cooling tower is decreasing and timely inspection and maintenance are required.

[0025] In this way, not only can the operating efficiency of the cooling tower be monitored in real time, but also based on the change trend of the chronological cooling efficiency, potential performance fluctuations of the cooling tower can be detected in a timely manner, providing key data support for subsequent fault diagnosis, performance optimization, and system regulation.

[0026] P30: Analyze the control relationships between the control parameters at each level in the cooling tower through historical operation data and on-site big data samples, and establish a hierarchical control analysis path.

[0027] Furthermore, step P30 of the embodiment of the present application further includes: P31: Collect the cooling tower control parameters and establish a cooling control hierarchy according to the distribution relationship of the control parameters; P32: Construct a sample matrix with the historical operation data in accordance with the paired relationship between environmental data, control parameters, and cooling efficiency; P33: Match and align the on-site big data samples with the environmental data and cooling efficiency of the historical operation data, and add them to the sample matrix to form an extended sample matrix; P34: Based on the extended sample matrix, fit the relationship between the control parameters at each level and the cooling efficiency of the cooling control hierarchy to obtain the control relationships at each level; P35: Establish the hierarchical control analysis path according to the mapping relationship between the environmental data, the cooling control hierarchy, and the control relationships at each level.

[0028] Optionally, by analyzing historical operation data and on-site big data samples, reveal the control relationships between the control parameters at each level in the cooling tower and establish a hierarchical control analysis path. This process can not only help understand the operating mechanism of the cooling tower, but also provide a theoretical basis for subsequent operating status identification and optimal control.

[0029] Specifically, first, collect the control parameters of the cooling tower and establish a cooling control hierarchy according to the distribution relationship of these parameters. The control parameters usually include the fan speed of the cooling tower, the cooling water flow rate, the working state of the spray system, etc. These parameters play a key role in the operation of the cooling tower. By analyzing the mutual relationship and hierarchical structure among these parameters, the control system of the cooling tower can be divided into multiple levels. For example, the first level may include the key parameters directly affecting the cooling efficiency, such as the fan speed and the cooling water flow rate; the second level may include auxiliary parameters, such as the water pressure of the spray system. This hierarchical division helps the subsequent analysis of the relationship between the control parameters and the cooling efficiency.

[0030] Next, construct a sample matrix based on the paired relationship of environmental data, control parameters, and cooling efficiency from the historical operation data. The historical operation data contains detailed records of the cooling tower under different operating conditions, and these data are an important basis for analyzing the relationship between the control parameters and the cooling efficiency. The construction of the sample matrix requires integrating the environmental data (such as environmental temperature, humidity, etc.), control parameters (such as fan speed, cooling water flow rate, etc.), and the corresponding cooling efficiency in each group of data to form a structured data set. For example, each row in the sample matrix can represent the operation data at a specific time point, including the environmental temperature, fan speed, cooling water flow rate, and the corresponding cooling efficiency value at that time.

[0031] Then, match and align the environmental data and cooling efficiency of the on-site big data samples with the historical operation data, and add these data to the sample matrix to form an extended sample matrix. The on-site big data samples refer to a large amount of real-time data collected during the actual operation, and these data can reflect the operation status of the cooling tower under different working conditions. Matching and aligning the on-site big data samples with the historical operation data can increase the diversity and representativeness of the sample matrix, thereby improving the accuracy of subsequent analysis. For example, if the on-site big data samples contain data on operation in a high-temperature environment, while such data is lacking in the historical operation data, then by matching, aligning, and adding it to the sample matrix, the content of the sample matrix can be enriched to more comprehensively reflect the operation characteristics of the cooling tower.

[0032] Based on the expanded sample matrix, the relationship between the control parameters of each level in the cooling control hierarchy and the cooling efficiency is fitted to obtain the control relationship of each level. For example, a mathematical model between the control parameters and the cooling efficiency can be established with the help of statistical analysis or machine learning algorithms, such as linear regression, decision trees or neural networks. Then, based on the mathematical model, the data in the expanded sample matrix is analyzed to reveal how the control parameters of each level affect the cooling efficiency. For example, through fitting, it can be found that in the first level, an increase in the fan speed may significantly improve the cooling efficiency, while in the second level, the water pressure of the sprinkler system has a relatively small effect on the cooling efficiency, but there is still a certain correlation. This relationship fitting can not only help understand the operating mechanism of the cooling tower, but also provide a basis for optimizing the control strategy.

[0033] Finally, based on the mapping relationship between environmental data, cooling control levels, and the control relationships at each level, a hierarchical control parsing pathway is established. The hierarchical control parsing pathway is a comprehensive model that integrates environmental data, cooling control levels, and the relationships between control parameters at each level and cooling efficiency to form a complete parsing framework. Through this framework, the operating status of the cooling tower can be deeply analyzed, and the cooling efficiency that can be achieved by adjusting the control parameters under different environmental conditions can be predicted. For example, when the ambient temperature rises, the hierarchical control parsing pathway can predict how to adjust the fan speed and cooling water flow to keep the cooling efficiency within a reasonable range. Through the hierarchical control parsing pathway, the cooling tower can achieve intelligent parameter adjustment and optimization to adapt to the cooling needs under different environmental conditions.

[0034] This process not only helps us understand the cooling tower's operating mechanism but also provides a theoretical basis for subsequent operational status identification and optimized control. Through the hierarchical control analysis pathway, the cooling tower's operating status can be accurately monitored and optimized, thereby improving its operating efficiency and reliability.

[0035] Furthermore, step P35 of the embodiment of the present application further includes: P35-1: Sort the cooling control levels according to the structural process relationship of the cooling tower, and establish a corresponding analysis channel for each control level; P35-2: Fit the environmental data corresponding to the cooling control level and the control relationship of each level to the corresponding analysis channel, cascade connect each analysis channel in the sorting order, and obtain the hierarchical control analysis path.

[0036] In a possible embodiment of the present application, the establishment of the cooling tower control hierarchical parsing path can be further expanded to ensure that the various control levels of the cooling tower can be effectively sorted and cascaded, thereby realizing the establishment of an efficient hierarchical control parsing path.

[0037] First, according to the structural process relationship of the cooling tower, sort the cooling control levels. During the operation of the cooling tower, different control levels are interdependent, with different functions and influence scopes. Therefore, it is necessary to reasonably sort these control levels. The basis for sorting can be the importance, priority of the control levels in the operation of the cooling tower and their logical relationships. For example, the fan control may be the basic level, directly affecting the air flow of the cooling tower, while the water pump control level may be affected by the fan control level. By sorting according to the structural process relationship of the cooling tower, it can be ensured that each control level is adjusted step by step in the order of its influence on the cooling effect.

[0038] Next, fit the environmental data corresponding to the cooling control levels and the control relationships of each level into their respective parsing channels, and cascade-connect the parsing channels in the sorted order. The environmental data refers to the external conditions affecting the operation of the cooling tower, such as environmental temperature, humidity, etc. The control relationships of each level can be the mathematical models between the control parameters obtained by fitting in the previous steps and the cooling efficiency. Fit these data and models into the corresponding parsing channels to establish a complete input-output relationship in each channel. For example, in the parsing channel of the first level, the input may be the inlet water flow and the environmental temperature, and the output is the cooling efficiency of this level; in the parsing channel of the second level, the input may be the pressure of the spraying system and the output of the first level, and the output is the cooling efficiency of the second level, and so on. By cascade-connecting these parsing channels in the sorted order, a complete hierarchical control parsing path can be formed. Cascade-connection means that the output of each level will be used as the input of the next level, thereby realizing the dynamic parsing of the entire operation process of the cooling tower. After the control parameters of each level are processed and adjusted in the parsing channel, they are cascaded and transmitted to the next level to achieve multi-level automatic control.

[0039] Through the cascade-connected hierarchical control parsing path, the operation of the cooling tower can be optimized and adjusted according to the actual environmental data and control parameters. Each parsing channel affects the cooling efficiency according to its control relationship, ensuring that the cooling tower can continuously perform adaptive adjustment according to real-time data during operation, optimize the cooling efficiency, and maintain efficient and stable operation under different environmental conditions.

[0040] Furthermore, step P35-2 of the embodiment of the present application further includes: P35-21: Analyze the control dependence relationship between each cooling control level based on the historical operation data, determine the influence relationship between levels, and obtain the level correlation index; P35-22: According to the level correlation index, configure a level cooling influence coefficient between adjacent control levels, fit it into the hierarchical control parsing path, and use it as the linkage weight parameter between the hierarchical parsing channels.

[0041] Specifically, the establishment process of the cooling control hierarchy analysis path can be further refined, and the analysis of the dependency relationships between control hierarchies and the configuration of linkage weight parameters can be increased. Ensure that the relationships between each level are clearly analyzed and appropriate linkage coefficients are configured for them, thereby improving the coordination and accuracy of the entire control system.

[0042] First, based on historical operation data, analyze the control dependency relationships between each cooling control hierarchy, determine the influence relationships between hierarchies, and then obtain a hierarchy correlation index. In this step, by analyzing historical operation data, especially the correlations between environmental factors, control parameters, and cooling efficiency, identify the dependency relationships between each cooling control hierarchy. For example, the fan control may directly affect the operating state of the water pump, and the operating state of the water pump in turn affects the magnitude of the cooling water flow. Through in-depth analysis of these relationships, the degree of influence of each control hierarchy on other hierarchies can be determined, thereby obtaining a hierarchy correlation index. This correlation index provides a theoretical basis for subsequent configuration of linkage coefficients, can quantify the influence of each control hierarchy on other hierarchies, and provides guidance for the dynamic adjustment of the system.

[0043] By using historical data to analyze the interaction of control parameters and their impact on cooling efficiency, it can be clarified which control hierarchies have a strong impact on other hierarchies and which hierarchies have relatively independent relationships. The extraction of the hierarchy correlation index provides data support for further optimizing the linkage between control hierarchies, ensuring that each level in the control system can be adaptively adjusted according to environmental conditions and reasonably influence other levels.

[0044] Next, according to the hierarchy correlation index, configure a hierarchy cooling influence coefficient between adjacent control hierarchies and fit it into the hierarchy control analysis path as the linkage weight parameter between hierarchy analysis channels. The hierarchy cooling influence coefficient is a quantitative parameter that describes the interaction intensity between adjacent control hierarchies. For a hierarchy with a larger influence coefficient, its adjustment will have a more significant impact on adjacent hierarchies, while for a hierarchy with a smaller influence coefficient, the control adjustment has a weaker effect on adjacent hierarchies. For example, if the adjustment of the fan speed has a greater impact on the demand for cooling water flow, then the hierarchy cooling influence coefficient between the fan speed control hierarchy and the cooling water flow control hierarchy will be larger. By fitting these coefficients into the hierarchy control analysis path, the interaction between adjacent control hierarchies can be introduced into the analysis path, thereby achieving a more accurate dynamic analysis of the entire cooling tower operation process. By configuring these linkage weight parameters, precise adjustment between control hierarchies can be achieved, thereby enhancing the stability and adaptability of the system.

[0045] P40: Using the operating cooling target of the heat source device as the target value, based on the timing cooling efficiency, perform hierarchical control parameter analysis through the hierarchical control analysis path to identify the operating state of the cooling tower.

[0046] Furthermore, step P40 of the embodiment of the present application further includes: P41: Analyze the load timing change of the heat source device and the device operating temperature constraint conditions, and establish a cooling target curve; P42: Align the cooling target curve with the timing cooling efficiency in time series to obtain a cooling deviation amount; P43: According to the cooling deviation amount, perform hierarchical control parameter analysis through the hierarchical control analysis path to obtain a deviation compensation result; P44: According to the deviation compensation result and the cooling deviation amount, perform compensation state identification to determine the operating state of the cooling tower.

[0047] Optionally, by analyzing the load change of the heat source device and the device operating temperature constraint conditions, and combining with the timing cooling efficiency data of the cooling tower, perform hierarchical control parameter analysis to identify the operating state of the cooling tower.

[0048] First, analyze the load timing change of the heat source device and the device operating temperature constraint conditions, and establish a cooling target curve. During the operation of the heat source device, heat is generated, and its load size and operating temperature change over time. For example, in a certain factory, a large injection molding machine generates a large amount of heat during each molding process. The heat must be taken away by the cooling tower in time, otherwise the product will deform and the device may issue a high-temperature alarm. Therefore, it is necessary to establish a cooling target curve based on the load timing change and operating temperature constraint conditions of the heat source device. This curve reflects the cooling effect required by the heat source device at different time points. The establishment of the cooling target curve needs to combine the process parameters and operating characteristics of the heat source device and the design parameters of the cooling tower, and is achieved through data analysis and modeling.

[0049] Next, align the cooling target curve with the timing cooling efficiency in time series to obtain a cooling deviation amount. The timing cooling efficiency is calculated through the previous steps, and it reflects the actual cooling effect of the cooling tower at different time points. Aligning the cooling target curve with the timing cooling efficiency in time series can visually compare the differences between the two, thereby obtaining the cooling deviation amount, that is, at a certain time point, the difference between the actual cooling effect of the cooling tower and the required cooling effect of the heat source device. By calculating the cooling deviation amount, it can be judged whether the operation of the cooling tower meets the cooling requirements of the heat source device. For example, if the cooling deviation amount is positive, it means that the actual cooling effect of the cooling tower is lower than the target value, and there may be a problem of insufficient cooling; on the contrary, if the cooling deviation amount is negative, it means that the actual cooling effect of the cooling tower is higher than the target value, and there may be a situation of excessive cooling.

[0050] Then, based on the cooling deviation, the hierarchical control analysis pathway is used to analyze the control parameters of each level to obtain the deviation compensation result. In this step, the cooling deviation is used as the input signal, and the control parameters of each level are adjusted through the previously established hierarchical control analysis pathway. These control parameters include fan speed, water pump flow, cooling water flow, etc. During this process, the hierarchical control analysis pathway will optimize and adjust each control level based on the cooling deviation to compensate for the deviation and ensure that the cooling tower can achieve the predetermined cooling effect. The deviation compensation result shows which control parameters need to be adjusted and how to adjust them so that the actual operating state of the cooling tower is close to the target cooling effect.

[0051] Finally, the compensation state is identified based on the deviation compensation results and the cooling deviation amount to determine the operating status of the cooling tower. In this step, by further analyzing the compensation results, it is determined whether the cooling deviation amount has been effectively compensated, thereby confirming whether the cooling tower has achieved the predetermined cooling target. For example, if the deviation compensation result can effectively reduce the cooling deviation amount, it means that the operating state of the cooling tower is controllable and the cooling needs of the heat source equipment can be met by adjusting the control parameters; conversely, if the deviation compensation result cannot effectively reduce the cooling deviation amount, or the cooling deviation amount continues to increase, it indicates that the cooling tower may have a fault or abnormal operation, and timely inspection and maintenance are required. Through compensation state identification, dynamic monitoring and evaluation of the cooling tower's operating status can be achieved, providing a scientific basis for the optimized operation and fault warning of the cooling tower.

[0052] By accurately calculating the cooling deviation and compensating for it, the operating status of the cooling tower can be intelligently identified and optimized, ensuring that the cooling tower can operate efficiently and stably under various load changes and environmental conditions, meeting the cooling needs of the equipment and avoiding the impact of excessive temperature on production quality.

[0053] Furthermore, step P43 of the embodiment of the present application further includes: P43-1: Based on the sequential cooling efficiency and the operating cooling target, the compensation target efficiency is determined; P43-2: The compensation target efficiency and the current environmental monitoring data are used as multi-condition constraints and input into the hierarchical control analysis path, and the control parameters are searched and analyzed layer by layer within the control parameter range of each level according to the sorting order of the cooling control level; P43-3: During the search process, the control parameters of each level are adjusted in linkage and the cooling efficiency is corrected in linkage according to the linkage weight parameters between the hierarchical analysis channels, so as to search for the combination of control parameters of each level to maximize the final operating cooling target and obtain the control parameters of each level.

[0054] It should be understood that the hierarchical control analysis path can be used to dynamically adjust the control parameters of each level in combination with the timing cooling efficiency and the operation cooling target to optimize the operating status of the cooling tower and maximize the cooling target.

[0055] First, the compensation target efficiency is determined based on the sequential cooling efficiency and the operational cooling target. This target efficiency is a target value adjusted based on the cooling tower's current efficiency to ensure that the cooling effect of the cooling tower meets the cooling target required by the heat source equipment. This target value is determined by comprehensively considering the load changes of the heat source equipment, environmental conditions, and the actual operating status of the cooling tower. For example, if the load of the heat source equipment increases, resulting in an increase in cooling demand, the compensation target efficiency will be increased accordingly to ensure that the cooling tower can meet the increased cooling requirements.

[0056] Next, the compensation target efficiency and current environmental monitoring data (such as ambient temperature and humidity) are input into the hierarchical control analysis path as multi-conditional constraints. Based on the sorting order of the cooling control hierarchy, a search and analysis is performed layer by layer within the control parameter range of each level. This process is similar to the constraint processing in multi-objective optimization problems. Through layer-by-layer search, a control parameter combination that satisfies the compensation target efficiency and environmental constraints is found. For example, if the current ambient temperature is high, it may be necessary to increase the cooling water flow or increase the fan speed to compensate for the reduced cooling efficiency caused by the high temperature environment.

[0057] In the process of searching for control parameters layer by layer, the control parameters of each level are adjusted in a linkage manner based on the linkage weight parameters between the hierarchical parsing channels. The linkage weight parameters reflect the degree of mutual influence between different control levels. By dynamically adjusting the control parameters, the linkage correction of the cooling efficiency is achieved. For example, if the adjustment of the fan speed has a greater impact on the cooling water flow, then when adjusting the fan speed, the cooling water flow also needs to be adjusted accordingly to ensure the optimization of the overall cooling efficiency. This process is similar to the parameter optimization in dynamic power consumption adjustment. By adjusting the control parameters in real time, it is ensured that the operating state of the cooling tower can dynamically adapt to the load changes of the heat source equipment, thereby maximizing the operating cooling target. The adjustment of the control parameters is dynamic, not fixed. It will be updated in real time according to the changes in the load of the heat source equipment.

[0058] For example, if a sudden increase in the heat source equipment load during cooling tower operation leads to an increase in cooling demand, control parameters such as fan speed and cooling water flow are dynamically adjusted through a hierarchical control analysis pathway based on the compensation target efficiency and current environmental conditions. Simultaneously, linkage weight parameters are used to coordinate the relationships between control parameters at each level, ensuring that the adjusted parameter combination maximizes the cooling target.

[0059] This dynamic adjustment control mechanism based on load output enables each control level of the cooling tower to respond to real-time data and achieve collaborative optimization between levels, ultimately ensuring that the cooling tower can maximize cooling effects and meet production needs based on the needs of heat source equipment and environmental changes.

[0060] Furthermore, step P44 of the embodiment of the present application further includes: P44-1: When the deviation compensation result meets the cooling deviation amount, the operating state of the cooling tower is a matching state; P44-2: When the deviation compensation result does not meet the cooling deviation amount, the operating state of the cooling tower is an abnormal state.

[0061] Specifically, the relationship between the deviation compensation result and the cooling deviation amount can be analyzed to further determine whether the operating state of the cooling tower is normal.

[0062] After the dynamic adjustment of the control parameters is completed, the system will perform a comparative analysis based on the deviation compensation result and the cooling deviation amount. Specifically, when the deviation compensation result can effectively meet the cooling deviation amount, that is, after adjusting the control parameters, the difference between the actual cooling efficiency and the target cooling efficiency of the cooling tower is effectively eliminated or controlled within the allowable range. At this time, the operating state of the cooling tower is determined to be a matching state. The matching state indicates that the current operating condition of the cooling tower is good and can meet the cooling requirements of the heat source equipment, so no further intervention is required.

[0063] On the contrary, when the deviation compensation result cannot meet the cooling deviation amount, that is, after adjusting the control parameters, the difference between the actual cooling efficiency and the target cooling efficiency of the cooling tower is still large and the expected cooling effect cannot be achieved. At this time, the operating state of the cooling tower is determined to be an abnormal state. At this time, there may be problems such as faults, equipment failures, or improper system configurations in the operation of the cooling tower, and further inspections and treatments are required to restore the normal operation of the system.

[0064] Through this comparative analysis, dynamic monitoring and real-time evaluation of the operating state of the cooling tower can be achieved. In practical applications, the system can automatically trigger corresponding warning or adjustment mechanisms according to the determination result of the operating state. For example, when the cooling tower is in an abnormal state, the system can automatically issue an alarm to remind the operator to perform inspections and maintenance; at the same time, the control parameters can also be further adjusted according to the preset rules to try to restore the normal operating state of the cooling tower.

[0065] This method for identifying the operating state based on the deviation compensation result and the cooling deviation amount can not only timely detect the operating problems of the cooling tower, but also provide a scientific basis for the optimized operation and fault warning of the cooling tower, thereby ensuring the efficient and stable operation of the cooling tower.

[0066] Furthermore, after determining the operating state of the cooling tower, step P40 of the embodiment of the present application further includes: P45a: When the operating state of the cooling tower is in an abnormal state, use the historical operating data of the cooling tower and environmental condition samples for similarity comparison to locate the control parameters that deviate; P46a: Conduct a correlation analysis on the fluctuation trend of the control parameters of each layer of the cooling tower and the cooling deviation response; P47a: Perform an abnormal deviation evaluation based on the correlation analysis results and the deviation control parameters, predict the abnormal control parameters, and locate the abnormal nodes.

[0067] In a possible embodiment of the present application, after determining that the operating state of the cooling tower is in an abnormal state, a series of analysis measures can be further taken to diagnose the abnormal cause and locate the problem node, so as to provide a clear basis for subsequent optimization and adjustment.

[0068] First, when the operating state of the cooling tower is abnormal, use the historical operating data of the cooling tower and environmental condition samples for similarity comparison to locate the control parameters that deviate. The operating performance of the cooling tower depends not only on its own operating parameters, but also on external environments (such as wet-bulb temperature, wind speed) and heat source loads. When a deviation occurs, by analyzing the operating state of the cooling tower under similar environmental conditions in historical data, the control parameter changes that may cause the abnormality can be quickly located. For example, if the current environmental conditions are similar to a certain moment in the historical data, but the operating state of the cooling tower is abnormal, then through comparison, it can be found which control parameters deviate from the previous "successful operation" state, thus providing "problematic candidate parameters" for subsequent diagnosis. For example, if the rotation speed of a certain fan has always been maintained within a certain range under similar environments, but the current rotation speed has obvious fluctuations, this may indicate that there is an abnormality in the fan control.

[0069] Next, conduct a correlation analysis on the fluctuation trend of the control parameters of each layer of the cooling tower and the cooling deviation response. The cooling tower is a dynamic system, and small changes in control parameters (such as fan rotation speed, pump frequency, etc.) may have a significant impact on the cooling effect. Through time series analysis, studying the causal relationship between parameter fluctuations and the decline in cooling efficiency can achieve parameter impact ranking, that is, identifying which parameter change is most likely to cause the current cooling deviation. For example, if the fan frequency drops from 50% to 20%, and the cooling temperature difference drops from 9°C to 5°C, through correlation analysis, the impact degree of the change in fan frequency on the decline in cooling efficiency can be confirmed, thus supporting automatic adjustment decisions, such as preferentially adjusting the fan parameters.

[0070] Finally, based on the results of the association analysis and the deviation control parameters, an abnormal deviation evaluation is carried out to predict the abnormal control parameters and locate the abnormal nodes. By combining the results of the association analysis and the deviation control parameters found in the comparison with historical data, the specific reasons for the current abnormality can be further evaluated, and the control parameters that may cause problems can be predicted. For example, if the association analysis shows that the fluctuation of the fan frequency has the greatest impact on the cooling efficiency, and the comparison with historical data finds that the current fan frequency is significantly lower than the value in the historical successful operation state, it can be predicted that the fan frequency is the main factor causing the current abnormality, and the fan system can be located as the abnormal node.

[0071] Through the above steps, not only can the control parameters causing the abnormal operation of the cooling tower be quickly located, but also a clear direction can be provided for subsequent optimization and adjustment. This method based on the comparison of historical data and association analysis can effectively improve the diagnostic efficiency of the operating state of the cooling tower, reduce the troubleshooting time, and ensure the efficient and stable operation of the cooling tower.

[0072] P50: Send status feedback information according to the operating state of the cooling tower.

[0073] Specifically, according to the judgment result of the operating state of the cooling tower, send status feedback information to the control system or the operator. Through this feedback process, ensure that the operating state of the cooling tower is always monitored and evaluated, so as to make timely adjustments or take necessary maintenance measures to ensure that the equipment is always in the optimal working state.

[0074] Specifically, when the cooling tower is identified as the matching state, the system should generate feedback information to confirm that the cooling tower has reached the predetermined cooling target and the cooling efficiency meets the requirements. This feedback can help the operator understand that the system is in a normal state and no additional intervention is required. The feedback information will include data such as the current operating state, temperature difference, and cooling efficiency of the cooling tower, and the operator can confirm the normal operation of the system based on this.

[0075] If the cooling tower is identified as an abnormal state, the feedback information will be more detailed, providing support for the operator to locate and repair the problem. For example, the feedback information will include a specific description of the abnormal state, such as the cooling temperature difference not reaching the predetermined target, the cooling efficiency being lower than the standard, and the abnormal fluctuation of the control parameters. In addition, the system can also analyze the possible reasons based on the current abnormal state and suggest subsequent operation measures. For example, if the system detects that the fan speed is too low resulting in poor cooling effect, the feedback information may suggest increasing the fan speed or prompting to check the operating state of the fan equipment. In this way, the feedback information is not only a report of the current state, but also a guide for problem solving, helping maintenance personnel to respond to abnormal situations in a timely manner.

[0076] In addition to specific status descriptions and recommended measures, the feedback information also provides relevant data monitoring content in real time, including current temperature data, cooling efficiency, control parameters, etc. This information helps the operator evaluate the health of the current system status and the changing trend of the cooling effect, so as to determine whether adjustment or maintenance is required.

[0077] All feedback information will be transmitted to the relevant operators or maintenance personnel through preset communication channels (such as monitoring platforms, SMS notifications, emails, etc.) to ensure that they can quickly receive the operation status information of the system. This process ensures that the operator can quickly respond and take effective measures when problems occur in the cooling tower, avoiding the cooling tower being in an abnormal state for a long time and affecting production and equipment safety.

[0078] Through this feedback mechanism, the operation status of the cooling tower can be continuously monitored and evaluated, providing timely data support for subsequent adjustment and optimization, and ensuring that the cooling tower always operates efficiently and stably during actual operation.

[0079] In summary, the embodiments of the present application at least have the following technical effects: In the present application, the heat source water temperature and cooling water temperature at the inlet and outlet of the cooling tower are obtained, the cooling temperature difference is calculated, and the cooling efficiency is evaluated based on the time-series temperature relationship. Combining historical operation data and on-site big data analysis, the control relationship of the control parameters at each level of the cooling tower is established to form a hierarchical control analysis path. Based on the cooling target of the heat source equipment, the control parameters at each level are analyzed through time-series cooling efficiency to identify the operation status of the cooling tower, and real-time adjustment is performed according to the status feedback information to optimize the cooling effect and improve the system efficiency.

[0080] It achieves the technical effect of real-time dynamically optimizing control parameters through big data analysis and multi-level control analysis, and improving the operation efficiency of the cooling tower.

[0081] Embodiment 2, based on the same inventive concept as the method for monitoring the operation status of a cooling tower based on big data analysis in the foregoing embodiment, as Figure 2 shown, the present application provides a system for monitoring the operation status of a cooling tower based on big data analysis. The system in the embodiments of the present application and the method embodiments are based on the same inventive concept. Among them, the system includes: A cooling temperature difference calculation module 11, which is used to obtain the heat source water temperature at the inlet of the cooling tower and the cooling water temperature at the outlet of the cooling tower, and calculate the cooling temperature difference by using the heat source water temperature and the cooling water temperature.

[0082] The cooling efficiency evaluation module 12 is used to establish a time-series temperature relationship of the heat source cooling process based on the heat source water temperature, cooling water temperature, and cooling temperature difference, perform cooling efficiency evaluation, and obtain time-series cooling efficiency.

[0083] The hierarchical control analysis path establishment module 13 is used to analyze the control relationship between the control parameters of each level in the cooling tower through historical operation data and field big data samples, and establish a hierarchical control analysis path.

[0084] The cooling tower operation status identification module 14 is used to identify the operation status of the cooling tower by analyzing the control parameters of each level through the hierarchical control analysis path based on the time sequence cooling efficiency, taking the operation cooling target of the heat source equipment as the target value.

[0085] The state feedback module 15 is used to send state feedback information according to the operating state of the cooling tower.

[0086] Furthermore, the cooling efficiency evaluation module 12 is further configured to perform the following steps: The heat source water temperature, cooling water temperature, and cooling temperature difference are arranged in time series according to the acquisition timestamp; the cooling efficiency is calculated based on the cooling temperature difference and the heat source water temperature; and the cooling efficiency is time-aligned in time series according to the time series arrangement order to obtain the time series cooling efficiency.

[0087] Furthermore, the hierarchical control parsing path establishing module 13 is further configured to perform the following steps: The control parameters of the cooling tower are collected, and a cooling control hierarchy is established according to the distribution relationship of the control parameters; the historical operating data are paired according to the relationship between environmental data, control parameters and cooling efficiency to construct a sample matrix; the field big data samples are matched and aligned with the environmental data and cooling efficiency of the historical operating data, and added to the sample matrix to form an extended sample matrix; based on the extended sample matrix, the relationship between the control parameters and cooling efficiency of each level of the cooling control hierarchy is fitted to obtain the control relationship of each level; according to the mapping relationship between the environmental data, the cooling control hierarchy and the control relationship of each level, the hierarchical control analysis path is established.

[0088] Furthermore, the hierarchical control parsing path establishing module 13 is further configured to perform the following steps: According to the structural process relationship of the cooling tower, the cooling control levels are sorted and a corresponding parsing channel is established for each control level. The environmental data corresponding to the cooling control levels and the control relationships of each level are fitted into the parsing channels to which they belong. The parsing channels are cascaded in the sorting order to obtain the hierarchical control parsing path.

[0089] Further, the hierarchical control parsing path establishment module 13 is further configured to perform the following steps: Analyze the control dependence relationship between each cooling control level based on the historical operation data, determine the influence relationship between levels, and obtain a level correlation index; according to the level correlation index, configure a level cooling influence coefficient between adjacent control levels, fit it into the hierarchical control parsing path, and use it as the linkage weight parameter between hierarchical parsing channels.

[0090] Further, the cooling tower operation state recognition module 14 is further configured to perform the following steps: Analyze the load time-series change of the heat source device and the equipment operation temperature constraint conditions, and establish a cooling target curve; align the cooling target curve with the time-series cooling efficiency in time series to obtain a cooling deviation amount; according to the cooling deviation amount, perform parsing of each level control parameter through the hierarchical control parsing path to obtain a deviation compensation result; perform compensation state recognition based on the deviation compensation result and the cooling deviation amount to determine the operation state of the cooling tower.

[0091] Further, the cooling tower operation state recognition module 14 is further configured to perform the following steps: When the deviation compensation result satisfies the cooling deviation amount, the operation state of the cooling tower is a matching state; when the deviation compensation result does not satisfy the cooling deviation amount, the operation state of the cooling tower is an abnormal state.

[0092] Further, the cooling tower operation state recognition module 14 is further configured to perform the following steps: When the operation state of the cooling tower is an abnormal state, perform similarity comparison using the historical operation data of the cooling tower and environmental condition samples to locate the deviated control parameters; perform correlation analysis on the fluctuation trend of each layer control parameter of the cooling tower and the cooling deviation response; perform abnormal deviation evaluation based on the correlation analysis result and the deviated control parameters, predict the abnormal control parameters, and locate the abnormal nodes.

[0093] Further, the cooling tower operation state recognition module 14 is further configured to perform the following steps: Based on the time-series cooling efficiency and the operation cooling target, determine the compensation target efficiency; use the compensation target efficiency and the current environmental monitoring data as multi-condition constraints, input them into the hierarchical control parsing path, and perform layer-by-layer control parameter search and analysis within the range of each layer control parameter according to the sorting order of the cooling control levels; during the search process, according to the linkage weight parameter between hierarchical parsing channels, perform linkage adjustment and cooling efficiency linkage correction on each layer control parameter to search for the combination of each layer control parameter to maximize the final operation cooling target, and obtain each layer control parameter.

[0094] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. In addition, the above specific embodiments of this specification have been described. Further, the processes depicted in the accompanying drawings do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0096] This specification and the drawings are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A method for monitoring the operating status of a cooling tower based on big data analysis, characterized in that Including: Obtain the temperature of the heat source water at the inlet of the cooling tower and the temperature of the cooling water at the outlet of the cooling tower, and calculate the cooling temperature difference by using the temperature of the heat source water and the temperature of the cooling water; Based on the temperature of the heat source water, the temperature of the cooling water, and the cooling temperature difference, establish the time-sequence temperature relationship of the heat source cooling process, conduct a cooling efficiency evaluation, and obtain the time-sequence cooling efficiency; Analyze the control relationship between the control parameters at each level in the cooling tower through historical operation data and on-site big data samples, and establish a hierarchical control analysis path; Taking the operating cooling target of the heat source equipment as the target value, based on the time-sequence cooling efficiency, conduct an analysis of the control parameters at each level through the hierarchical control analysis path to identify the operating state of the cooling tower; Send status feedback information according to the operating state of the cooling tower.

2. The method for monitoring the operating state of a cooling tower based on big data analysis according to claim 1, wherein Taking the operating cooling target of the heat source equipment as the target value, based on the time-sequence cooling efficiency, conduct an analysis of the control parameters at each level through the hierarchical control analysis path to identify the operating state of the cooling tower, including: Analyze the load time-sequence change of the heat source equipment and the equipment operation temperature constraint conditions, and establish a cooling target curve; Align the time sequence of the cooling target curve and the time-sequence cooling efficiency to obtain a cooling deviation amount; According to the cooling deviation amount, conduct an analysis of the control parameters at each level through the hierarchical control analysis path to obtain a deviation compensation result; Based on the deviation compensation result and the cooling deviation amount, conduct a compensation state identification to determine the operating state of the cooling tower.

3. The method for monitoring the operating state of a cooling tower based on big data analysis according to claim 2, wherein Determine the operating state of the cooling tower, including: When the deviation compensation result meets the cooling deviation amount, the operating state of the cooling tower is a matching state; When the deviation compensation result does not meet the cooling deviation amount, the operating state of the cooling tower is an abnormal state.

4. The method for monitoring the operating status of a cooling tower based on big data analysis according to claim 3, characterized in that, After determining the operating state of the cooling tower, it further includes: When the operating state of the cooling tower is an abnormal state, use the historical operation data of the cooling tower and the environmental condition samples for similarity comparison to locate the deviated control parameters; Conduct a correlation analysis on the fluctuation trend of the control parameters at each level of the cooling tower and the cooling deviation response; According to the correlation analysis result and the deviated control parameters, conduct an abnormal deviation evaluation, predict the abnormal control parameters, and locate the abnormal nodes.

5. The method for monitoring the operating state of a cooling tower based on big data analysis according to claim 1, wherein, Based on the temperature of the heat source water, the temperature of the cooling water, and the cooling temperature difference, establish the time-sequence temperature relationship of the heat source cooling process, conduct a cooling efficiency evaluation, and obtain the time-sequence cooling efficiency, including: Arrange the temperature of the heat source water, the temperature of the cooling water, and the cooling temperature difference in time sequence according to the acquisition timestamp; Calculate the cooling efficiency according to the cooling temperature difference and the temperature of the heat source water; Arrange the cooling efficiency in time alignment according to the time-sequence arrangement order to obtain the time-sequence cooling efficiency.

6. The method for monitoring the operating state of a cooling tower based on big data analysis according to claim 1, wherein Analyze the control relationship between the control parameters at each level in the cooling tower through historical operation data and on-site big data samples, and establish a hierarchical control analysis path, including: Collect the control parameters of the cooling tower, and establish a cooling control hierarchy according to the distribution relationship of the control parameters; Construct a sample matrix with the historical operation data in pairs of environmental data, control parameters, and cooling efficiency; Match and align the on-site big data samples with the environmental data and cooling efficiency of the historical operation data, and add them to the sample matrix to form an extended sample matrix; Based on the extended sample matrix, fit the relationship between the control parameters and the cooling efficiency of each level of the cooling control hierarchy to obtain the control relationship of each level; According to the mapping relationship between the environmental data, the cooling control hierarchy and the control relationships of each level, establish the hierarchical control analysis path.

7. The method for monitoring the operating status of a cooling tower based on big data analysis according to claim 6, characterized in that, Establishing the hierarchical control analysis path includes: Sort the cooling control hierarchy according to the structural flow relationship of the cooling tower, and establish a corresponding analysis channel for each control level; Fit the environmental data corresponding to the cooling control hierarchy and the control relationships of each level into the corresponding analysis channels, and cascade-connect the analysis channels in the sorted order to obtain the hierarchical control analysis path.

8. The method for monitoring the operating status of a cooling tower based on big data analysis according to claim 7, wherein, Obtaining the hierarchical control analysis path further includes: Analyze the control dependence relationship between each cooling control level based on the historical operation data, determine the influence relationship between levels, and obtain the hierarchical correlation index; According to the hierarchical correlation index, configure the hierarchical cooling influence coefficient between adjacent control levels, and fit it into the hierarchical control analysis path as the linkage weight parameter between the hierarchical analysis channels.

9. The method for monitoring the operating state of a cooling tower based on big data analysis according to claim 8, wherein Taking the operating cooling target of the heat source device as the target value, perform the analysis of the control parameters of each level through the hierarchical control analysis path based on the time-sequential cooling efficiency, including: Determine the compensation target efficiency based on the time-sequential cooling efficiency and the operating cooling target; Use the compensation target efficiency and the current environmental monitoring data as multi-condition constraints, input them into the hierarchical control analysis path, and perform a layer-by-layer search and analysis of the control parameters within the control parameter range of each cooling control level according to the sorted order of the cooling control levels; During the search process, according to the linkage weight parameter between the hierarchical analysis channels, perform linkage adjustment of the control parameters of each level and linkage correction of the cooling efficiency, so as to search for the combination of control parameters of each level to maximize the final operating cooling target, and obtain the control parameters of each level.

10. A cooling tower operation status monitoring system based on big data analysis, characterized in that, The system includes: A cooling temperature difference calculation module, which is used to obtain the temperature of the heat source water at the inlet of the cooling tower and the temperature of the cooling water at the outlet of the cooling tower, and calculate the cooling temperature difference by using the temperature of the heat source water and the temperature of the cooling water; A cooling efficiency evaluation module, which is used to establish the time-sequential temperature relationship of the heat source cooling process based on the temperature of the heat source water, the temperature of the cooling water, and the cooling temperature difference, perform cooling efficiency evaluation, and obtain the time-sequential cooling efficiency; A hierarchical control analysis path establishment module, which is used to analyze the control relationship between the control parameters of each level in the cooling tower through historical operation data and on-site big data samples, and establish a hierarchical control analysis path; A cooling tower operation state identification module, which is used to take the operating cooling target of the heat source device as the target value, perform the analysis of the control parameters of each level through the hierarchical control analysis path based on the time-sequential cooling efficiency, and identify the operation state of the cooling tower; A state feedback module, which is used to send state feedback information according to the operation state of the cooling tower.

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