Energy efficiency optimization and fault diagnosis methods, devices, terminals, media and products based on the combined health of cooling towers in refrigeration rooms

Abstract

The present application relates to the technical field of energy efficiency optimization of high-efficiency refrigeration rooms, and in particular to an energy efficiency optimization and fault diagnosis method, device, terminal, medium and product based on the health of a cooling tower combination in a refrigeration room, the method comprising: obtaining the current multi-index cooling tower data of a target cooling tower and normalizing it; wherein the multi-index cooling tower data comprises: service life, failure rate, historical average power, energy efficiency ratio and number of maintenance; constructing a cooling tower health function and determining the weight parameters of each function; based on the normalized multi-index cooling tower data, according to the cooling tower health function, calculating the current health of the target cooling tower, so as to optimize the energy efficiency and diagnose the cooling tower combination in the refrigeration room. The present application comprehensively considers multiple indicators and conducts a comprehensive quantitative assessment of the health status of the cooling tower.

Description

Technical Field

[0001] The present application relates to the technical field of energy efficiency optimization of high-efficiency refrigeration rooms, and in particular to an energy efficiency optimization and fault diagnosis method, device, terminal, medium and product based on the combined health of cooling towers in refrigeration rooms. Background Art

[0002] In the refrigeration system, the cooling tower, as one of the important components, plays an irreplaceable role in the cooling process of the system. Although it does not play the role of core equipment like the chiller, the stable operation status of the cooling tower also directly affects the efficiency and performance of the entire refrigeration system. The health assessment of the cooling tower is still of great significance, which can help operation and maintenance personnel better maintain and manage equipment and improve the stability and reliability of the system. However, the current existing technologies often only focus on the monitoring and analysis of a single indicator, lack of a comprehensive method to evaluate the health of the cooling tower, making it difficult to comprehensively and accurately evaluate the overall operating status of the cooling tower. Water flow monitoring: Evaluate the operating status of the cooling tower by monitoring the water flow of the cooling tower. Water flow is an important parameter for the normal operation of the cooling tower, which can reflect whether there is blockage or abnormality in the system. However, it is difficult to fully evaluate the health of the cooling tower by relying solely on water flow monitoring.

[0003] Temperature difference analysis: The heat dissipation efficiency of a cooling tower is evaluated by monitoring the temperature difference between the inlet and outlet of the cooling tower. The temperature difference can reflect the cooling capacity of the cooling tower, but it lacks comprehensive consideration of other key indicators.

[0004] Regular maintenance inspection: The cooling tower is cleaned by regular inspection to ensure its normal operation. However, this method relies on experience and manual judgment, lacks quantitative analysis methods, and cannot fully reflect the health status of the cooling tower.

[0005] The existing technology has the following problems and deficiencies in cooling tower health assessment:

[0006] Single parameter dependence: Existing methods usually only focus on a single parameter, such as cooling efficiency or failure rate, and cannot comprehensively and comprehensively evaluate the health of the cooling tower. Single parameter evaluation methods tend to ignore other important operating indicators, resulting in one-sided and inaccurate evaluation results.

[0007] Lack of real-time performance: Although regular maintenance inspections can reveal some problems, they are lagging behind and cannot reflect the health status of the equipment in real time. If maintenance is performed after a failure occurs, it often causes certain losses, making it difficult to provide early warning and prevent potential failures.

[0008] Strong dependence on experience: Traditional evaluation methods rely on human experience and judgment, lacking scientific and objective quantitative analysis methods. Different operators may have different judgment criteria, and the evaluation results are highly subjective and uncertain.

[0009] Lack of comprehensive evaluation model: The existing technology lacks a comprehensive evaluation model that can combine multiple operating parameters to fully reflect the health of the cooling tower. Lack of comprehensive consideration of equipment operating life, failure rate, cooling efficiency and other aspects makes it difficult to provide a comprehensive and accurate health assessment. Summary of the invention

[0010] In view of the shortcomings of the prior art mentioned above, the purpose of the present application is to provide an energy efficiency optimization and fault diagnosis method, device, terminal, medium and product based on the combined health of the cooling tower in the refrigeration room, so as to solve the problem of the lack of a method for comprehensively evaluating the health status of the cooling tower with multiple indicators.

[0011] To achieve the above-mentioned purpose and other related purposes, the first aspect of the present application provides an energy efficiency optimization and fault diagnosis method based on the combined health of cooling towers in a refrigeration room, comprising: obtaining the current multi-index cooling tower data of the target cooling tower and normalizing it; wherein the multi-index cooling tower data includes: service life, failure rate, historical average power, energy efficiency ratio and maintenance times; constructing a cooling tower health function and determining the weight parameters of each function; wherein the constructed cooling tower health function is: ; k1, k2, k3, k4, k5 are weight parameters of cooling tower health function, is the health of the cooling tower, L is the normalized service life, F is the normalized failure rate, P is the normalized historical average power, E is the normalized energy efficiency ratio, and M is the normalized maintenance times; based on the normalized multi-index cooling tower data and according to the cooling tower health function, the current health of the target cooling tower is calculated to provide energy efficiency optimization and fault diagnosis for the cooling tower combination in the refrigeration room.

[0012] In some embodiments of the first aspect of the present application, the method further includes: a calculation formula for the normalized service life is: ; Where L is the normalized service life, is the current service life, is the normal service life; the normalized failure rate is calculated as follows: ; Where F is the normalized failure rate, is the current failure rate, This is the normal failure rate.

[0013] In some embodiments of the first aspect of the present application, the method further includes: a calculation formula for the normalized historical average power is: ; Where P is the normalized historical average power, is the rated power, is the historical average power; the calculation formula of the normalized energy efficiency ratio is: ; Where E is the normalized energy efficiency ratio, is the actual energy efficiency ratio, It is the standard energy efficiency ratio.

[0014] In some embodiments of the first aspect of the present application, the method further includes: a calculation formula for the normalized maintenance times is: ; Where M is the normalized maintenance times, is the actual number of maintenance times, This is the standard maintenance frequency.

[0015] In some embodiments of the first aspect of the present application, the method further includes: setting a health threshold; when the current health of each cooling tower is lower than the set health threshold, adjusting the operating parameters of the cooling tower or performing maintenance on the cooling tower.

[0016] To achieve the above-mentioned purpose and other related purposes, the second aspect of the present application provides an energy efficiency optimization and fault diagnosis device based on the combined health of cooling towers in a refrigeration room, comprising: a processing module, used to obtain the current multi-index cooling tower data of the target cooling tower and normalize it; wherein the multi-index cooling tower data includes: service life, failure rate, historical average power, energy efficiency ratio and maintenance times; a function construction module, connected to the processing module, used to construct a cooling tower health function and determine the weight parameters of each function; wherein the constructed cooling tower health function is: ; k1, k2, k3, k4, k5 are weight parameters of cooling tower health function, is the health of the cooling tower, L is the normalized service life, F is the normalized failure rate, P is the normalized historical average power, E is the normalized energy efficiency ratio, and M is the normalized maintenance times; a health calculation module is connected to the function construction module, and is used to calculate the current health of the target cooling tower based on the normalized multi-indicator cooling tower data and the cooling tower health function.

[0017] To achieve the above-mentioned purpose and other related purposes, the third aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the energy efficiency optimization and fault diagnosis method based on the health of the cooling tower combination of the refrigeration room.

[0018] To achieve the above-mentioned purpose and other related purposes, the fourth aspect of the present application provides a computer program product, which includes a computer program code. When the computer program code runs on a computer, the computer implements the energy efficiency optimization and fault diagnosis method based on the health of the cooling tower combination of the refrigeration room.

[0019] To achieve the above-mentioned purpose and other related purposes, the fifth aspect of the present application provides an electronic terminal, including a memory, a processor and a computer program stored in the memory; the processor executes the computer program to implement the energy efficiency optimization and fault diagnosis method based on the health of the cooling tower combination of the refrigeration room.

[0020] As described above, the energy efficiency optimization and fault diagnosis method, device, terminal, medium and product based on the combined health of cooling towers in refrigeration rooms of the present application have the following beneficial effects:

[0021] This application takes into account multiple cooling tower indicators and conducts a comprehensive quantitative assessment of the health status of the cooling tower. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Shown is a flow chart of an energy efficiency optimization and fault diagnosis method based on the combined health of cooling towers in a refrigeration room in one embodiment of the present application.

[0023] Figure 2 Shown is a schematic block diagram of an energy efficiency optimization and fault diagnosis device based on the combined health of cooling towers in a refrigeration room in one embodiment of the present application.

[0024] Figure 3 Shown is a schematic diagram of the structure of an electronic terminal in one embodiment of the present application. DETAILED DESCRIPTION

[0025] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0026] In the embodiments of the present application, words such as "first" and "second" are used to distinguish the same or similar items with substantially the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference.

[0027] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" represent examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0028] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc or abc, where a, b, c can be single or multiple.

[0029] To facilitate understanding of the embodiments of the present application, first Figure 1 Detailed description. Figure 1 The flowchart of a method for energy efficiency optimization and fault diagnosis based on the combined health of cooling towers in a refrigeration room in an embodiment of the present invention is shown. The method for energy efficiency optimization and fault diagnosis based on the combined health of cooling towers in a refrigeration room in this embodiment mainly includes the following steps:

[0030] Step S11: obtaining the current multi-index cooling tower data of the target cooling tower and normalizing it.

[0031] In one embodiment, the multi-index cooling tower data includes: service life, failure rate, historical average power, energy efficiency ratio, and maintenance times.

[0032] In one embodiment, a plurality of sensors installed inside the cooling tower are used to collect cooling tower index data.

[0033] In one embodiment, the normalized service life is calculated using the following formula 1:

[0034] ;(Formula 1)

[0035] Where L is the normalized service life, is the current service life, For normal service life.

[0036] It should be noted that the current service life refers to the time the cooling tower has been in operation, and the normal service life refers to the expected operating life of the cooling tower under normal operating conditions.

[0037] It should also be noted that the specific value of the normalized service life ranges from 0 to 1. The smaller the specific value of the normalized service life is, the longer the remaining life of the equipment is.

[0038] In a specific embodiment, the normal service life is usually provided by the manufacturer of the cooling tower. If the manufacturer cannot provide it, it can be estimated based on industry standards and historical data. The specific process includes:

[0039] Industry standard reference: Use the average service life data in industry standards and research reports as a reference. For example, the average service life of refrigeration equipment under normal operating conditions is usually between 10 and 15 years. This data can be obtained by consulting industry association reports, academic research papers or standard documents.

[0040] Historical data analysis: Collect historical operating data of cooling towers of the same type and calculate their average service life through statistical analysis. For example, use cooling tower operation records or public equipment failure and life databases to calculate the average value.

[0041] In one embodiment, the normalized failure rate is calculated using the following formula 2:

[0042] ;(Formula 2)

[0043] Among them, F is the normalized failure rate, is the current failure rate, This is the normal failure rate.

[0044] It should be noted that the normal failure rate refers to the failure rate of the cooling tower under normal operating conditions, which is usually determined by historical data or industry standards, and the current failure rate refers to the actual failure rate of the cooling tower at all times in history.

[0045] It should also be noted that the specific value of the normalized failure rate ranges from 0 to 1. The smaller the specific value of the normalized failure rate is, the lower the failure rate of the device is.

[0046] In one embodiment, the normalized historical average power is calculated using the following formula 3:

[0047] ;(Formula 3)

[0048] Where P is the normalized historical average power, is the rated power, is the historical average power.

[0049] It should be noted that the historical average power refers to the average power consumption of the cooling tower at all times in history.

[0050] It should also be noted that the specific value of the normalized historical average power ranges from 0 to 1. The larger the specific value of the normalized historical average power is, the closer the power consumption of the cooling tower equipment is to the rated value.

[0051] In one embodiment, the normalized energy efficiency ratio is calculated using the following formula 4:

[0052] ;(Formula 4)

[0053] Among them, E is the normalized energy efficiency ratio, is the actual energy efficiency ratio, It is the standard energy efficiency ratio.

[0054] It should be noted that the energy efficiency ratio is a quantitative value of the cooling efficiency of the cooling tower. Cooling efficiency = temperature difference between inlet and outlet of the tower / cooling tower inlet water temperature - ambient wet bulb temperature.

[0055] It should also be noted that the specific value of the normalized energy efficiency ratio is between 0 and 1. The larger the specific value of the normalized energy efficiency ratio is, the higher the energy efficiency of the equipment is.

[0056] In one embodiment, the normalized maintenance times are calculated using the following formula 5:

[0057] ;(Formula 5)

[0058] Among them, M is the normalized maintenance number, is the actual number of maintenance times, This is the standard maintenance frequency.

[0059] It should be noted that the maintenance times refer to the maintenance times of the cooling tower within a specific time period. Those skilled in the art can set the specific value of the specific time period as required, and the present invention does not limit this.

[0060] It should also be noted that the specific value of the normalized maintenance times ranges from 0 to 1, and a larger value of the normalized maintenance times indicates a greater number of equipment maintenance times.

[0061] Step S12: construct cooling tower health function and determine weight parameters of each function.

[0062] In one embodiment, the cooling tower health function is constructed as shown in the following formula 6:

[0063] ;(Formula 6)

[0064] Among them, k1, k2, k3, k4, k5 are the weight coefficients of the cooling tower health function. is the health of the cooling tower, L is the normalized service life, F is the normalized failure rate, P is the normalized historical average power, E is the normalized energy efficiency ratio, and M is the normalized maintenance times.

[0065] In a specific embodiment, the specific values ​​of the weight coefficients k1, k2, k3, k4, and k5 may be determined by experts in the field based on actual operating experience of the refrigeration pump.

[0066] In a specific embodiment, the weight coefficients k1, k2, k3, k4, and k5 can be determined by the analytic hierarchy process (AHP), and the specific process includes: decomposing the various indicators of the multi-indicator refrigeration pump data into several levels through the constructed indicator hierarchy model to form a hierarchical structure; using the paired comparison method to construct a judgment matrix and calculate the weight coefficient of each indicator; and ensuring the rationality of the judgment matrix through consistency testing.

[0067] In a specific embodiment, the historical operating data of the cooling tower is used to analyze the impact of service life, failure rate, historical average power, energy efficiency ratio and maintenance times on the health of the cooling tower based on statistical analysis and machine learning algorithms to adjust the specific values ​​of k1, k2, k3, k4 and k5.

[0068] Step S13: Based on the normalized multi-index cooling tower data and according to the cooling tower health function, the current health of the target cooling tower is calculated to provide energy efficiency optimization and fault diagnosis for the cooling tower combination in the refrigeration room.

[0069] In one embodiment, the health of the cooling tower may be predicted using a trained LSTM-based cooling tower health prediction model.

[0070] In one embodiment, the method for training a cooling tower health prediction model based on LSTM includes: performing outlier processing and normalization on the collected historical cooling tower index data in turn to obtain a cooling tower index data set; inputting the cooling tower index data set into the LSTM model for training to obtain a trained LSTM-based cooling tower health prediction model.

[0071] In one embodiment, the cooling tower index data includes: service life, failure rate, historical average power, energy efficiency ratio and maintenance times.

[0072] It should be noted that the normalization method of the historical cooling tower index data is the same as the normalization method of the current cooling tower index data described above, and no repeated explanation is given here. The outlier processing and normalization processing ensure the accuracy and consistency of the data.

[0073] It should also be noted that the long short-term memory network (LSTM) is a recurrent neural network model commonly used to process and predict time series data. Compared with traditional recurrent neural networks, LSTM can handle long-term dependency problems more effectively and can remember long-term sequence information. The core structure of LSTM includes three gates: forget gate, input gate and output gate, as well as a cell state, which work together to control the flow of information. This structure enables LSTM to selectively remember or forget previous information, thereby better predicting future trends or state changes.

[0074] In one embodiment, the normalized cooling tower index data of each cooling tower is input into a trained LSTM-based cooling tower health model to obtain the current health of each cooling tower and the changing trend of the health.

[0075] It should be noted that LSTM is able to learn complex time series patterns from historical data, such as equipment usage trends, failure modes, and maintenance cycles.

[0076] In one embodiment, the method further includes: setting a health threshold; when the current health of each cooling tower is lower than the set health threshold, triggering an alarm to adjust the operating parameters of the cooling tower or perform maintenance on the cooling tower, thereby avoiding increased energy efficiency or increased risk of failure.

[0077] In one embodiment, adjusting the operating parameters of the cooling tower includes adjusting water treatment parameters, equipment operation mode, and maintenance plan.

[0078] In one embodiment, the method further includes displaying the current health status and change trend of each cooling tower through a visual interface to intuitively display the status of the cooling tower.

[0079] In one embodiment, a clustering algorithm is used in combination with health to perform fault analysis on a cooling tower. The specific process includes:

[0080] The operation data of the cooling tower is collected; wherein the operation data of the cooling tower includes but is not limited to the inlet and outlet temperatures of cooling water, the speed of the fan, the amount of spray water, the motor current, the health index of the cooling tower, etc.; the health of the cooling tower is calculated by the energy efficiency optimization and fault diagnosis method based on the combined health of the cooling tower in the refrigeration room in the embodiment of the present invention; the collected data is cleaned and normalized to obtain a data set to ensure data quality and consistency; a clustering algorithm (K-means, DBSCAN) is used to analyze the data set with health characteristics, and the data points are grouped into different clusters. Normally operating data points are clustered together, and data points containing fault modes form independent or marginalized clusters, thereby obtaining clustering results. Based on the clustering results, abnormal data points that are far away from normal clusters are identified, which indicate that the health of the cooling tower has decreased or there are specific fault modes. Combined with the changing trend of health, the severity of the fault and the possible development direction can be further analyzed; by monitoring the dynamic changes of health characteristics and clustering analysis results, the faults that may be encountered in the cooling tower in the future can be predicted, and preventive maintenance measures can be taken to avoid potential operation interruptions.

[0081] In a specific embodiment, the clustering algorithm is used to identify abnormal data points from the operating data of a running cooling tower, which show that the health of the cooling tower is gradually declining, accompanied by an abnormal increase in the cooling outlet temperature. Further diagnosis confirms that the cooling tower's spray system is clogged, and timely cleaning and maintenance avoid further deterioration of cooling efficiency and extend the service life of the cooling tower.

[0082] Figure 2 is a schematic block diagram of an energy efficiency optimization and fault diagnosis device 2 based on the combined health of cooling towers in a refrigeration room provided in an embodiment of the present application. Figure 2 As shown, the cooling tower health calculation device 2 includes:

[0083] The processing module 21 is used to obtain the current multi-index cooling tower data of the target cooling tower and normalize it; wherein the multi-index cooling tower data includes: service life, failure rate, historical average power, energy efficiency ratio and maintenance times;

[0084] The function construction module 22 is connected to the processing module 21 and is used to construct a cooling tower health function and determine the weight parameters of each function; wherein the constructed cooling tower health function is: ; k1, k2, k3, k4, k5 are weight parameters of cooling tower health function, is the health of the cooling tower, L is the normalized service life, F is the normalized failure rate, P is the normalized historical average power, E is the normalized energy efficiency ratio, and M is the normalized maintenance times;

[0085] The health calculation module 23 is connected to the function construction module 22 and is used to calculate the current health of the target cooling tower based on the normalized multi-index cooling tower data and the cooling tower health function, so as to optimize the energy efficiency and diagnose the faults of the cooling tower combination in the refrigeration room.

[0086] It should be understood that the specific process of each module executing the above corresponding steps has been described in detail in the above method embodiment, and for the sake of brevity, it will not be repeated here.

[0087] It should also be understood that the division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0088] In one embodiment, the method further includes: the calculation formula of the normalized service life is: ; Where L is the normalized service life, is the current service life, is the normal service life; the normalized failure rate is calculated as follows: ; Where F is the normalized failure rate, is the current failure rate, This is the normal failure rate.

[0089] In one embodiment, the method further includes: the calculation formula of the normalized historical average power is: ; Where P is the normalized historical average power, is the rated power, is the historical average power; the calculation formula of the normalized energy efficiency ratio is: ; Where E is the normalized energy efficiency ratio, is the actual energy efficiency ratio, It is the standard energy efficiency ratio.

[0090] In one embodiment, the method further includes: the calculation formula of the normalized maintenance times is: ; Where M is the normalized maintenance times, is the actual number of maintenance times, This is the standard maintenance frequency.

[0091] In one embodiment, the method further includes: setting a health threshold; when the current health of each cooling tower is lower than the set health threshold, adjusting the operating parameters of the cooling tower or performing maintenance on the cooling tower.

[0092] Figure 3 is a schematic block diagram of an electronic terminal provided in an embodiment of the present application. Figure 3 As shown, the electronic terminal includes: at least one processor 301, a memory 302, at least one network interface 303 and a user interface 305. The various components in the device are coupled together through a bus system 304. It can be understood that the bus system 304 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 304 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 3 In the specification, various buses are labeled as bus systems.

[0093] The user interface 305 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0094] It is understood that the memory 302 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable categories of memory.

[0095] The memory 302 in the embodiment of the present invention is used to store various categories of data to support the operation of the electronic terminal 300. Examples of these data include: any executable program for operating on the electronic terminal 300, such as an operating system 3021 and an application 3022; the operating system 3021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 3022 may include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The energy efficiency optimization and fault diagnosis method based on the health of the cooling tower combination in the refrigeration room provided in the embodiment of the present invention may be included in the application 3022.

[0096] The method disclosed in the above embodiment of the present invention can be applied to the processor 301, or implemented by the processor 301. The processor 301 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 301 or the instruction in the form of software. The above processor 301 may be a general processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 301 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The general processor 301 can be a microprocessor or any conventional processor, etc. In combination with the steps of the accessory optimization method provided in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0097] In an exemplary embodiment, the electronic terminal 300 may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD) to execute the aforementioned method.

[0098] According to the method provided in the embodiment of the present application, the present application also provides a computer program product, which includes: a computer program code, when the computer program code is run on a computer, the computer executes Figure 1 The energy efficiency optimization and fault diagnosis method based on the combined health of cooling towers in a refrigeration room in the illustrated embodiment.

[0099] According to the method provided in the embodiment of the present application, the present application also provides a computer-readable storage medium, which stores a program code, and when the program code is run on a computer, the computer executes Figure 1 The energy efficiency optimization and fault diagnosis method based on the combined health of cooling towers in a refrigeration room in the illustrated embodiment.

[0100] The terms "component", "module", "system", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process, a processor, an object, an executable file, an execution thread, a program and / or a computer running on a processor. By way of illustration, both applications and computing devices running on a computing device can be components. One or more components may reside in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In addition, these components may be executed from various computer-readable media having various data structures stored thereon. Components may, for example, communicate through local and / or remote processes according to signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system and / or a network, such as the Internet interacting with other systems through signals).

[0101] Those of ordinary skill in the art will appreciate that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0103] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0106] In the above embodiments, the functions of each functional unit can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. Available media may be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., high-density digital video discs (DVDs), or semiconductor media (e.g., solid state disks (SSDs)).

[0107] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0108] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0109] In summary, the present application provides a method, device, terminal, medium and product for energy efficiency optimization and fault diagnosis based on the health of the cooling tower combination in the refrigeration room. The method includes: obtaining the current multi-index cooling tower data of the target cooling tower and normalizing it; wherein the multi-index cooling tower data includes: service life, failure rate, historical average power, energy efficiency ratio and maintenance times; constructing a cooling tower health function and determining the weight parameters of each function; based on the normalized multi-index cooling tower data, according to the cooling tower health function, calculating the current health of the target cooling tower for energy efficiency optimization and fault diagnosis of the cooling tower combination in the refrigeration room. The present application comprehensively considers multiple indicators and conducts a comprehensive quantitative assessment of the health status of the cooling tower. Therefore, the present application effectively overcomes the various shortcomings in the prior art and has a high industrial utilization value.

[0110] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. An energy efficiency optimization and fault diagnosis method based on the combined health of cooling towers in a refrigeration room, characterized in that: include: Obtaining current multi-index cooling tower data of the target cooling tower and normalizing it; wherein the multi-index cooling tower data includes: service life, failure rate, historical average power, energy efficiency ratio, and maintenance times; Construct cooling tower health functions and determine the weight parameters of each function; Among them, the constructed cooling tower health function is: k1, k2, k3, k4, k5 are weight parameters of cooling tower health function, CH 冷却塔 is the health of the cooling tower, L is the normalized service life, F is the normalized failure rate, P is the normalized historical average power, E is the normalized energy efficiency ratio, and M is the normalized maintenance times; The normalized service life is calculated as: L=L cur / L n ; Among them, L is the normalized service life, L cur is the current service life, L n Normal service life; The normalized failure rate is calculated as: F=F cur / F n ; Among them, F is the normalized failure rate, F cur is the current failure rate, F n is the normal failure rate; The calculation formula of the normalized historical average power is: P=P r / P a ; Among them, P is the normalized historical average power, P r is the rated power, P a is the historical average power; The calculation formula of normalized energy efficiency ratio is: E=E c / AND s ; Among them, E is the normalized energy efficiency ratio, E c is the actual energy efficiency ratio, E s is the standard energy efficiency ratio; The calculation formula for the normalized maintenance times is: M=M r / M s ; Among them, M is the normalized maintenance number, M r is the actual maintenance times, M s is the standard maintenance frequency; Based on the normalized multi-index cooling tower data, according to the cooling tower health function, the current health of the target cooling tower is calculated for energy efficiency optimization and fault diagnosis of the cooling tower combination in the refrigeration room.

2. The energy efficiency optimization and fault diagnosis method based on the combined health of cooling towers in a refrigeration room according to claim 1 is characterized in that: The method further includes: setting a health threshold; when the current health of each cooling tower is lower than the set health threshold, adjusting the operating parameters of the cooling tower or maintaining the cooling tower.

3. An energy efficiency optimization and fault diagnosis device based on the combined health of cooling towers in a refrigeration room, characterized in that: include: A processing module, used for obtaining and normalizing the current multi-index cooling tower data of the target cooling tower; wherein the multi-index cooling tower data includes: service life, failure rate, historical average power, energy efficiency ratio and maintenance times; A function construction module, connected to the processing module, for constructing cooling tower health functions and determining weight parameters of each function; Among them, the constructed cooling tower health function is: k1, k2, k3, k4, k5 are weight parameters of cooling tower health function, CH 冷却塔 is the health of the cooling tower, L is the normalized service life, F is the normalized failure rate, P is the normalized historical average power, E is the normalized energy efficiency ratio, and M is the normalized maintenance times; The normalized service life is calculated as: L=L cur / L n ; Among them, L is the normalized service life, L cur is the current service life, L n Normal service life; The normalized failure rate is calculated as: F=F cur / F n ; Among them, F is the normalized failure rate, F cur is the current failure rate, F n is the normal failure rate; The calculation formula of the normalized historical average power is: P=P r / P a ; Among them, P is the normalized historical average power, P r is the rated power, P a is the historical average power; The calculation formula of normalized energy efficiency ratio is: E=E c / AND s ; Among them, E is the normalized energy efficiency ratio, E c is the actual energy efficiency ratio, E s is the standard energy efficiency ratio; The calculation formula for the normalized maintenance times is: M=M r / M s ; Among them, M is the normalized maintenance number, M r is the actual maintenance times, M s is the standard maintenance frequency; The health calculation module is connected to the function construction module and is used to calculate the current health of the target cooling tower based on the normalized multi-index cooling tower data and the cooling tower health function.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

5. A computer program product, characterized in that The computer program product includes a computer program code, and when the computer program code is executed on a computer, the computer is enabled to implement the method according to any one of claims 1 to 2.

6. An electronic terminal comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the method according to any one of claims 1 to 2.

Images