An intelligent diagnosis method and system for the entire life cycle of a central air-conditioning system

Abstract

The embodiments of this specification provide an intelligent diagnosis method and system for the entire life cycle of a central air-conditioning system, which belongs to the technical field of central air-conditioning systems. The method includes evaluating the equipment selection and energy efficiency of the central air-conditioning system in the selection stage; verifying the accuracy of the sensor and evaluating whether the hydraulic distribution of the chilled water and the hydraulic distribution of the cooling water are balanced in the verification stage; evaluating the control strategy and system operating parameters of the central air-conditioning system in the debugging stage; and evaluating the energy efficiency of the central air-conditioning system, the chiller, the refrigeration pump, the cooling pump and the cooling tower in the operation stage. The method has the advantages of being able to perform intelligent diagnosis throughout the entire life cycle of the central air-conditioning system without relying on manual inspections and the experience judgment of inspectors, and having high diagnostic efficiency and relatively accurate.

Description

Technical Field

[0001] This specification relates to the field of central air-conditioning systems, and in particular to an intelligent diagnosis method and system for the entire life cycle of a central air-conditioning system. Background Art

[0002] Central air-conditioning system energy consumption accounts for a relatively large proportion in many types of buildings, especially in public buildings such as hospitals, transportation hubs, industrial and commercial office buildings. However, there is generally room for improvement in operational management levels, and the diagnosis of central air-conditioning systems is particularly important.

[0003] Traditional diagnosis of system energy efficiency and equipment failures relies on on-site manual inspections and the inspectors' experience. While this approach can identify some potential faults, it is time-consuming, labor-intensive, subjective, and prone to omissions. With the rapid development of the Internet of Things and the widespread adoption of building automation systems, many air conditioning units can now automatically collect data, reflect real-time operating status, and upload this data to a unified information-based central control system. This provides the foundation for intelligent diagnosis of central air conditioning systems throughout their entire lifecycle, from selection to calibration, commissioning, and ultimately operation. However, existing diagnostic technologies still have significant limitations. Specifically, they rely on empirical model setup and operational management, and insufficient experience can easily lead to abnormal operating conditions.

[0004] Therefore, it is necessary to provide an intelligent diagnosis method and system for the entire life cycle of a central air-conditioning system, which can be used to perform intelligent diagnosis of the entire life cycle of the central air-conditioning system without relying on manual inspections and the experience judgment of inspectors. Summary of the Invention

[0005] In order to solve the technical problems in the prior art, one of the embodiments of this specification provides an intelligent diagnosis method for the entire life cycle of a central air-conditioning system, including: in the selection stage, evaluating the equipment selection and energy efficiency of the central air-conditioning system; in the verification stage, verifying the accuracy of the sensor, and evaluating whether the hydraulic distribution of the chilled water and the hydraulic distribution of the cooling water are balanced; in the debugging stage, evaluating the control strategy and system operating parameters of the central air-conditioning system; in the operation stage, evaluating the energy efficiency of the central air-conditioning system, chiller, refrigeration pump, cooling pump and cooling tower.

[0006] In some embodiments, during the selection stage, the equipment selection and energy efficiency of the central air-conditioning system are evaluated, including: obtaining the load rate of the chiller at multiple operating time points and the actual temperature of the working area of ​​the central air-conditioning system; based on the load rate of the chiller at multiple operating time points, the actual temperature of the working area of ​​the central air-conditioning system and the set temperature of the working area of ​​the central air-conditioning system, evaluating the equipment selection of the central air-conditioning system.

[0007] In some embodiments, during the selection stage, the equipment selection and energy efficiency of the central air-conditioning system are evaluated, including: establishing a physical model of the chiller, determining the energy efficiency of the chiller under the nominal operating conditions on the nameplate of the chiller based on the physical model of the chiller, and evaluating the energy efficiency of the chiller based on the energy efficiency of the chiller under the nominal operating conditions on the nameplate of the chiller and the energy efficiency of the chiller under the latest nominal operating conditions; establishing a physical model of the refrigeration pump, determining the pump efficiency of the refrigeration pump under the nominal operating conditions on the nameplate of the refrigeration pump based on the physical model of the refrigeration pump, and evaluating the energy efficiency of the refrigeration pump based on the energy efficiency of the refrigeration pump under the nominal operating conditions on the nameplate of the refrigeration pump. and the latest nominal pump efficiency of the refrigeration pump, evaluate the energy efficiency of the refrigeration pump; establish a physical model of the cooling pump, determine the pump efficiency of the cooling pump under the nominal operating conditions on the nameplate of the cooling pump through the physical model of the cooling pump based on the nominal operating conditions on the nameplate of the cooling pump, and evaluate the energy efficiency of the cooling pump based on the energy efficiency of the cooling pump under the nominal operating conditions on the nameplate of the cooling pump and the latest nominal pump efficiency of the cooling pump; establish a physical model of the cooling tower, determine the power consumption ratio of the cooling tower under the nominal operating conditions on the nameplate of the cooling tower through the physical model of the cooling tower based on the nominal operating conditions on the nameplate of the cooling tower, and evaluate the energy efficiency of the cooling tower based on the energy efficiency of the cooling tower under the nominal operating conditions on the nameplate of the cooling tower and the latest nominal power consumption ratio of the cooling tower.

[0008] In some embodiments, the accuracy of the sensor is verified in the verification stage, including: performing a regression analysis on the cooling capacity or energy consumption of the refrigeration station in the historical operation data and the outdoor wet-bulb temperature to determine the upper confidence limit of the outdoor wet-bulb temperature and the lower confidence limit of the outdoor wet-bulb temperature, and judging whether the outdoor wet-bulb temperature sensor is abnormal based on the outdoor wet-bulb temperature collected at the operation point and the upper confidence limit of the outdoor wet-bulb temperature and the lower confidence limit of the outdoor wet-bulb temperature; performing a regression analysis on the chilled water supply and return water temperature in the historical operation data and the inlet and outlet temperatures of the evaporator to determine the upper confidence limit of the chilled water supply and return water temperature and the lower confidence limit of the chilled water supply and return water temperature, and judging whether the outdoor wet-bulb temperature sensor is abnormal based on the chilled water supply and return water temperature collected at the operation point and the upper confidence limit of the chilled water supply and return water temperature and the lower confidence limit of the chilled water supply and return water temperature. limit, judge whether the chilled water supply and return water temperature sensor is abnormal; perform regression analysis on the cooling water supply and return water temperature in the historical operation data and the inlet and outlet temperatures of the condenser, determine the upper confidence limit and the lower confidence limit of the cooling water supply and return water temperature, and judge whether the cooling water supply and return water temperature sensor is abnormal based on the cooling water supply and return water temperature collected at the operation point and the upper confidence limit and the lower confidence limit of the cooling water supply and return water temperature; perform regression analysis on the chilled water flow in the historical operation data and the energy consumption of the refrigeration station, determine the upper confidence limit and the lower confidence limit of the chilled water flow, and judge whether the chilled water flow sensor is abnormal based on the chilled water flow collected at the operation point and the upper confidence limit and the lower confidence limit of the chilled water flow.

[0009] In some embodiments, during the verification stage, the hydraulic distribution of the chilled water and the hydraulic distribution of the cooling water are evaluated, including: judging whether the hydraulic distribution of the chilled water is balanced based on the temperature of each return branch pipe of the chilled water, the temperature of the return main pipe, the actual temperature of the working area of ​​the central air-conditioning system and the set temperature of the working area of ​​the central air-conditioning system; judging whether the hydraulic distribution of the cooling water is balanced based on the return water temperature of the cooling tower and the return main pipe temperature.

[0010] In some embodiments, during the debugging phase, the control strategy and system operating parameters of the central air-conditioning system are evaluated, including: determining the situation of turning on or off the equipment when the central air-conditioning system executes the power on / off and load addition / subtraction strategies; performing regression analysis on the chilled water supply temperature in the historical operation data and the load or the outdoor wet-bulb temperature to determine the upper confidence limit and the lower confidence limit of the chilled water supply temperature, and judging whether the chilled water supply temperature of the operating point is abnormal based on the chilled water supply temperature collected at the operating point and the upper confidence limit and the lower confidence limit of the chilled water supply temperature; performing regression analysis on the cooling water return temperature in the historical operation data and the load or the outdoor wet-bulb temperature to determine the upper confidence limit and the lower confidence limit of the cooling water return temperature, and judging whether the cooling water supply temperature of the operating point is abnormal based on the cooling water return temperature collected at the operating point and the upper confidence limit and the lower confidence limit of the cooling water return temperature. The lower confidence limit of the cooling water return temperature is used to judge whether the cooling water return temperature of the operating point is abnormal; the cooling water supply and return temperature difference in the historical operation data is regressed with the load or the outdoor wet-bulb temperature to determine the upper confidence limit and the lower confidence limit of the cooling water supply and return temperature difference, and based on the cooling water supply and return temperature difference collected at the operating point and the upper confidence limit and the lower confidence limit of the cooling water supply and return temperature difference, judge whether the cooling water supply and return temperature difference of the operating point is abnormal; the cooling water supply and return temperature difference in the historical operation data is regressed with the load or the outdoor wet-bulb temperature to determine the upper confidence limit and the lower confidence limit of the cooling water supply and return temperature difference, and based on the cooling water supply and return temperature difference collected at the operating point and the upper confidence limit and the lower confidence limit of the cooling water supply and return temperature difference, judge whether the cooling water supply and return temperature difference of the operating point is abnormal.

[0011] In some embodiments, during the operation phase, the energy efficiency of the central air-conditioning system, chiller, refrigeration pump, cooling pump and cooling tower is evaluated, including: regressing the system energy consumption in the historical operation data with the cooling capacity / outdoor wet-bulb temperature to determine the upper confidence limit and the lower confidence limit of the system energy consumption, and judging whether the operation point is an abnormal point based on the system energy consumption at the operation point and the upper confidence limit and the lower confidence limit of the system energy consumption; regressing the energy efficiency of the chiller in the historical operation data with the evaporation temperature, condensing temperature and partial load rate to determine the upper confidence limit and the lower confidence limit of the energy efficiency of the chiller, and judging whether the operation point is an abnormal point based on the energy efficiency of the chiller at the operation point and the upper confidence limit and the lower confidence limit of the energy efficiency of the chiller; and regressing the energy efficiency of the refrigeration pump in the historical operation data with the cooling capacity, the head of the refrigeration pump, the flow rate of chilled water and the efficiency of the refrigeration pump. , determine the upper confidence limit of the energy efficiency of the refrigeration pump and the lower confidence limit of the energy efficiency of the refrigeration pump, and judge whether the operating point is an abnormal point based on the energy efficiency of the refrigeration pump at the operating point and the upper confidence limit of the energy efficiency of the refrigeration pump and the lower confidence limit of the energy efficiency of the refrigeration pump; perform regression analysis on the energy efficiency of the cooling pump in the historical operating data with the cooling capacity, the head of the cooling pump, the flow rate of cooling water and the efficiency of the cooling pump, determine the upper confidence limit of the energy efficiency of the cooling pump and the lower confidence limit of the energy efficiency of the cooling pump, and judge whether the operating point is an abnormal point based on the energy efficiency of the cooling pump at the operating point and the upper confidence limit of the energy efficiency of the cooling pump and the lower confidence limit of the energy efficiency of the cooling pump; perform regression analysis on the energy efficiency of the cooling pump in the historical operating data with the cooling capacity and the air volume of the cooling tower, determine the upper confidence limit of the energy efficiency of the cooling pump and the lower confidence limit of the energy efficiency of the cooling pump, and judge whether the operating point is an abnormal point based on the energy efficiency of the cooling pump at the operating point and the upper confidence limit of the energy efficiency of the cooling pump and the lower confidence limit of the energy efficiency of the cooling pump.

[0012] One of the embodiments of the present specification provides an intelligent diagnostic system for the entire life cycle of a central air-conditioning system, including: a monitoring module for monitoring the operating status and operating parameters of each device of the central air-conditioning system during the selection stage, verification stage, commissioning stage and operation stage; a data processing module for processing the historical operation data of the central air-conditioning system; a diagnostic module for evaluating the equipment selection and energy efficiency of the central air-conditioning system during the selection stage; also for verifying the accuracy of sensors and evaluating the hydraulic balance of chilled water and cooling water during the verification stage; also for evaluating the control strategy and system operating parameters of the system during the commissioning stage; and also for evaluating the energy efficiency of the system, chillers, refrigeration pumps and cooling pumps, and cooling towers during the operation stage.

[0013] In some embodiments, the monitoring module is also used to: obtain the energy consumption, cooling capacity, energy efficiency, evaporation temperature, condensation temperature, partial load rate, energy consumption of the chiller, head of the chiller, flow rate of chilled water, efficiency of the chiller, energy consumption of the cooling pump, head of the cooling pump, flow rate of cooling water, efficiency of the cooling pump, power consumption ratio of the cooling tower, energy consumption of the cooling tower, flow rate of cooling water, fan efficiency of the cooling tower, and air volume of the cooling tower during the selection stage; obtain the outdoor wet-bulb temperature, cooling capacity, energy consumption of the refrigeration station, supply and return temperature of chilled water, inlet and outlet temperature of the evaporator, supply and return temperature of cooling water, and the cooling water temperature during the verification stage. temperature, inlet and outlet temperatures of the condenser, chilled water flow rate, temperature on each chilled water return branch pipe, and return water temperature of each cooling tower; during the commissioning phase, obtain the chilled water supply temperature, cooling water return temperature, chilled water supply and return temperature difference, cooling water supply and return temperature difference, load, and outdoor wet-bulb temperature; during the operation phase, obtain the energy efficiency, evaporating temperature, condensing temperature, and part load rate of the chiller; the energy efficiency, cooling capacity, head of the chiller pump, chilled water flow rate, efficiency of the chiller pump, energy efficiency of the cooling pump, heat dissipation, head of the cooling pump, cooling water flow rate, efficiency of the cooling pump, energy efficiency of the cooling tower, heat dissipation, and air volume of the cooling tower.

[0014] In some embodiments, the data processing module is further used to filter the historical operating data of the central air-conditioning system based on operating time, parameter limits and PauTa criteria. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0016] Figure 1 This is a schematic diagram of an application scenario of an intelligent diagnostic system for the entire life cycle of a central air-conditioning system according to some embodiments of this specification;

[0017] Figure 2 This is a module diagram of an intelligent diagnostic system for the entire life cycle of a central air-conditioning system according to some embodiments of this specification;

[0018] Figure 3 is an exemplary flow chart of an intelligent diagnosis method for the entire life cycle of a central air-conditioning system according to some embodiments of this specification;

[0019] Figure 4a is a schematic diagram of a regression curve of system energy consumption and outdoor wet-bulb temperature according to some embodiments of this specification;

[0020] Figure 4bis a schematic diagram of a regression curve of energy efficiency and part load rate of a chiller according to some embodiments of this specification;

[0021] Figure 4c is a schematic diagram of a regression curve of energy efficiency and chilled water flow of a chilled water pump according to some embodiments of this specification;

[0022] Figure 4d is a schematic diagram of a regression curve of the energy efficiency of a cooling pump and the cooling water flow rate according to some embodiments of this specification;

[0023] Figure 4e Schematic diagram of a regression curve of the energy efficiency and cooling water flow rate of a cooling tower according to some embodiments of this specification.

[0024] In the figure, 110 is a processing device; 120 is a network; 130 is a user terminal; 140 is a storage device; 150 is a central air-conditioning unit; and 160 is a monitoring component. DETAILED DESCRIPTION

[0025] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0026] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0027] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0028] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0029] Figure 1 This is a schematic diagram of an application scenario of an intelligent diagnosis system for the entire life cycle of a central air-conditioning system according to some embodiments of this specification.

[0030] like Figure 1 As shown, the application scenario may include a processing device 110 , a network 120 , a user terminal 130 , a storage device 140 , a central air conditioning unit 150 , and a monitoring component 160 .

[0031] In some embodiments, the processing device 110 can be used to process information and / or data related to intelligent diagnosis of the entire life cycle of the central air-conditioning system. For example, the processing device 110 can be used to evaluate the equipment selection and energy efficiency of the central air-conditioning system in the selection stage; in the verification stage, verify the accuracy of the sensor, and evaluate whether the hydraulic distribution of the chilled water and the hydraulic distribution of the cooling water are balanced; in the debugging stage, evaluate the control strategy and system operating parameters of the central air-conditioning system; in the operation stage, evaluate the energy efficiency of the central air-conditioning system, chiller, refrigeration pump, cooling pump and cooling tower. For more descriptions of the processing device 110, please refer to the descriptions in other parts of this application. For example, Figure 3 and its description.

[0032] In some embodiments, processing device 110 may be regional or remote. For example, processing device 110 may access information and / or data stored in user terminal 130 and storage device 140 via network 120. In some embodiments, processing device 110 may directly connect to user terminal 130 and storage device 140 to access the information and / or data stored therein. In some embodiments, processing device 110 may be executed on a cloud platform. For example, the cloud platform may include one or any combination of a private cloud, a public cloud, a hybrid cloud, a community cloud, a decentralized cloud, an internal cloud, and the like.

[0033] In some embodiments, the processing device 110 may include a processor 210, which may include one or more sub-processors (e.g., a single-core processing device or a multi-core multi-core processing device). By way of example only, the processor may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, or any combination thereof.

[0034] The network 120 can facilitate the exchange of data and / or information in the application scenario. In some embodiments, one or more components in the application scenario (e.g., the processing device 110, the user terminal 130, and the storage device 140) can send data and / or information to other components in the application scenario via the network 120. For example, the processing device 110 can obtain historical operating data from the storage device 140 via the network 120. In some embodiments, the network 120 can be any type of wired or wireless network. For example, the network 120 can include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, an internet network, a local area network (LAN), a Bluetooth network, a ZigBee network, a near field communication (NFC) network, or any combination thereof.

[0035] In some embodiments, the user terminal 130 can obtain information or data in the application scenario. For example, the user terminal 130 can obtain the evaluation results from the processing device 110 via the network 120. In some embodiments, the user terminal 130 can include a mobile device (e.g., a smartphone, a smartwatch, etc.), a tablet computer, a laptop computer, etc., or any combination thereof.

[0036] In some embodiments, storage device 140 can be connected to network 120 to enable communication with one or more components of the application scenario (e.g., processing device 110, user terminal 130, etc.). One or more components of the application scenario can access data or instructions stored in storage device 140 via network 120. In some embodiments, storage device 140 can be directly connected to or communicate with one or more components of the application scenario (e.g., processing device 110, user terminal 130). In some embodiments, storage device 140 can be part of processing device 110.

[0037] The central air conditioning unit 150 may include at least one cooling tower, a freezing pump, a cooling pump and a chiller. The central air conditioning unit 150 may also include other equipment, such as a water valve arranged on the freezing side of the chiller, a water valve arranged on the cooling side of the chiller, etc.

[0038] Monitoring component 160 is used to monitor the operating status and operating parameters of each device in central air conditioning system 150 during the selection, verification, commissioning, and operation stages. During the selection stage, the monitoring component 160 obtains information on the chiller's energy consumption, cooling capacity, chiller efficiency, evaporation temperature, condensing temperature, partial load rate, chiller pump energy consumption, chiller pump head, chilled water flow rate, chiller pump efficiency, cooling pump energy consumption, cooling pump head, cooling water flow rate, cooling pump efficiency, cooling tower power consumption ratio, cooling tower energy consumption, cooling water flow rate, cooling tower fan efficiency, and cooling tower air volume. During the verification phase, obtain the outdoor wet-bulb temperature, cooling capacity, energy consumption of the refrigeration station, chilled water supply and return temperature, evaporator inlet and outlet temperatures, cooling water supply and return temperatures, condenser inlet and outlet temperatures, chilled water flow rate, temperature on each chilled water return branch, return temperature of each cooling tower, etc.; during the commissioning phase, obtain the chilled water supply temperature, cooling water return temperature, chilled water supply and return temperature difference, cooling water supply and return temperature difference, load, outdoor wet-bulb temperature, etc.; during the operation phase, obtain the energy efficiency, evaporation temperature, condensing temperature, partial load rate of the chiller; energy efficiency, cooling capacity, head of the chilled pump, flow rate of chilled water, efficiency of the chilled pump, energy efficiency of the cooling pump, heat dissipation, head of the cooling pump, flow rate of cooling water, efficiency of the cooling pump, energy efficiency of the cooling tower, heat dissipation, air volume of the cooling tower, etc.

[0039] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of the present application. For those skilled in the art, various changes and modifications can be made under the guidance of the contents of this application. The features, structures, methods and other features of the exemplary embodiments described in this application can be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the storage device 140 can be a data storage device including a cloud computing platform, such as a public cloud, a private cloud, a community and a hybrid cloud. However, these changes and modifications will not deviate from the scope of the present application.

[0040] Figure 2 This is a module diagram of an intelligent diagnostic system for the entire life cycle of a central air-conditioning system according to some embodiments of this specification.

[0041] like Figure 2 As shown, an intelligent diagnosis system for the entire life cycle of a central air-conditioning system may include a monitoring module, a data processing module and a diagnosis module.

[0042] The monitoring module can be used to monitor the operating status and operating parameters of each device of the central air-conditioning system during the selection stage, the verification stage, the debugging stage and the operation stage. In some embodiments, the monitoring module can obtain the energy consumption, cooling capacity, energy efficiency, evaporation temperature, condensation temperature, partial load rate, energy consumption of the chiller, head of the chiller, flow rate of chilled water, efficiency of the chiller, energy consumption of the cooling pump, head of the cooling pump, flow rate of cooling water, efficiency of the cooling pump, power consumption ratio of the cooling tower, energy consumption of the cooling tower, flow rate of cooling water, fan efficiency of the cooling tower, air volume of the cooling tower, etc. during the selection stage; and obtain the outdoor wet-bulb temperature, cooling capacity, energy consumption of the refrigeration station, supply and return temperature of chilled water, inlet and outlet temperature of the evaporator, supply and return temperature of cooling water, cooling tower fan efficiency, air volume of cooling tower, etc. during the verification stage. The inlet and outlet temperatures of the condenser, the chilled water flow rate, the temperature on each return branch of the chilled water, the return water temperature of each cooling tower, etc.; during the commissioning phase, obtain the chilled water supply temperature, cooling water return temperature, chilled water supply and return temperature difference, cooling water supply and return temperature difference, load, and outdoor wet-bulb temperature; during the operation phase, obtain the energy efficiency, evaporation temperature, condensing temperature, part load rate, etc. of the chiller; the energy efficiency, cooling capacity, head of the chilled water pump, chilled water flow rate, efficiency of the chilled water pump, energy efficiency of the cooling pump, heat dissipation, head of the cooling water pump, cooling water flow rate, efficiency of the cooling pump, energy efficiency of the cooling tower, heat dissipation, air volume of the cooling tower, etc.

[0043] The data processing module can be used to process the historical operation data of the central air-conditioning system. In some embodiments, the data processing module can also be used to filter the historical operation data of the central air-conditioning system based on the operating time, parameter limits and PauTa criteria.

[0044] The diagnostic module can be used to evaluate the equipment selection and energy efficiency of central air conditioning systems during the selection phase. It can also be used during the calibration phase to verify sensor accuracy and assess the hydraulic balance of chilled and cooling water. It can also be used during the commissioning phase to evaluate the system's control strategy and operating parameters. During the operation phase, the diagnostic module can also be used to evaluate the energy efficiency of the system, chillers, chiller and cooling pumps, and cooling towers.

[0045] For more information about the monitoring module, data processing module, and diagnostic module, see Figure 3 The related descriptions will not be repeated here.

[0046] It should be noted that the above description of the intelligent diagnostic system and its modules for the entire life cycle of the central air conditioning system is for convenience only and does not limit this specification to the scope of the embodiments. It is understandable that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the modules or form subsystems connected to other modules without deviating from the principles. In some embodiments, Figure 2 The monitoring module, data processing module, and diagnostic module disclosed herein may be separate modules within a system, or a single module may implement the functions of two or more of the aforementioned modules. For example, the modules may share a storage module, or each module may have its own storage module. Such variations are within the scope of this specification.

[0047] Figure 3 This is an exemplary flow chart of an intelligent diagnostic method for the entire life cycle of a central air-conditioning system according to some embodiments of this specification. Figure 3 As shown, a central air conditioning system full life cycle intelligent diagnosis method includes the following steps. In some embodiments, a central air conditioning system full life cycle intelligent diagnosis method can be performed by a central air conditioning system full life cycle intelligent diagnosis system or processing device 110.

[0048] Step 310 , in the selection phase, evaluate the equipment selection and energy efficiency of the central air conditioning system.

[0049] In some embodiments, the diagnostic module can obtain the load rate of the chiller at multiple operating time points and the actual temperature of the working area of ​​the central air-conditioning system through the monitoring module, and evaluate the equipment selection of the central air-conditioning system based on the load rate of the chiller at multiple operating time points, the actual temperature of the working area of ​​the central air-conditioning system and the set temperature of the working area of ​​the central air-conditioning system.

[0050] As you can understand, a central air conditioning system consists of a chiller, a refrigeration pump, a cooling pump, and a cooling tower. The selection of a central air conditioning system depends on the selection of the chiller. Based on the historical operating data of the central air conditioning system, the cumulative load distribution of the chiller from low to high load factors is compiled. If the cumulative distribution of the chiller at 70% load factor is greater than 70%, the central air conditioning system is oversized. If the cumulative distribution of the chiller at 90% load factor is less than 30% and the actual temperature in the air-conditioned area is greater than the set temperature, the central air conditioning system is undersized.

[0051] In some embodiments, the diagnostic module may establish a physical model of the chiller, determine the energy efficiency of the chiller under the nameplate nominal operating conditions based on the nameplate nominal operating conditions of the chiller using the physical model of the chiller, and evaluate the energy efficiency of the chiller based on the energy efficiency of the chiller under the nameplate nominal operating conditions and the energy efficiency of the chiller under the most recent nominal operating conditions. The physical model of the chiller may characterize the correspondence between the energy efficiency of the chiller and the operating conditions.

[0052] Energy consumption of chiller P CHL Depends on the cooling capacity Q CHThe COP of the chiller depends on the evaporation temperature T. EVA , condensation temperature T CON and the part load rate PLR, i.e.

[0053]

[0054] Where, T EVA is the evaporation temperature, T CON The physical model described above can be trained based on the operating data of the central air conditioning system. Substituting the nominal operating conditions on the chiller's nameplate into the physical model, the COP of the chiller in the computer room under nominal operating conditions can be obtained. This can then be compared with the latest nominal energy efficiency COP of chillers. If the difference exceeds 20%, the chiller's energy efficiency is low.

[0055] In some embodiments, the diagnostic module may establish a physical model of the refrigeration pump, determine the pump efficiency of the refrigeration pump under the nominal operating conditions on the nameplate based on the physical model of the refrigeration pump and the nominal operating conditions on the nameplate of the refrigeration pump, and evaluate the energy efficiency of the refrigeration pump based on the energy efficiency of the refrigeration pump under the nominal operating conditions on the nameplate and the latest nominal pump efficiency of the refrigeration pump. The physical model of the refrigeration pump may characterize the correspondence between the energy efficiency of the refrigeration pump and the operating conditions.

[0056] Energy consumption of refrigeration pump Q CHWP Depends on the head H of the refrigeration pump CHWP , Chilled water flow m chw and the efficiency of the refrigeration pump η CHWP ,Right now

[0057]

[0058]

[0059] Where η CHWP,rated is the chilled water pump rated efficiency, PLR CHWP is the chilled water pump load rate, and a0-a3 are related parameters. Based on the operating data of the central air-conditioning system, the above physical model can be trained. Substituting the nominal operating conditions on the nameplate of the chilled water pump into the physical model, the pump efficiency η of the chilled water pump in the machine room under the nominal operating conditions can be obtained. CHWP If the difference is more than 20%, it indicates that the energy efficiency of the chiller pump is too low.

[0060] In some embodiments, the diagnostic module may establish a physical model of the cooling pump, determine the cooling pump's pump efficiency under the nominal operating conditions on the cooling pump's nameplate based on the physical model of the cooling pump, and evaluate the cooling pump's energy efficiency based on the cooling pump's energy efficiency under the nominal operating conditions on the cooling pump's nameplate and the cooling pump's latest nominal pump efficiency. The physical model of the cooling pump may characterize the relationship between the cooling pump's energy efficiency and operating conditions.

[0061] Energy consumption of cooling pump Q CWP Depends on the head H of the cooling pump CWP , cooling water flow m cw and the cooling pump efficiency η CWP ,Right now

[0062]

[0063]

[0064] Where η CWP,rated is the cooling water pump rated efficiency, PLR CWP is the cooling water pump load rate, and b0-b3 are related parameters. Based on the operating data of the central air conditioning system, the physical model of the cooling pump can be trained. Substituting the nominal operating conditions on the cooling pump nameplate into the physical model, the pump efficiency η of the cooling pump in the machine room under the nominal operating conditions can be obtained. CWP And compare it with the nominal efficiency of the latest cooling pump. If the difference is more than 20%, it means that the cooling pump is less energy efficient;

[0065] In some embodiments, the diagnostic module may establish a physical model of the cooling tower, determine the power consumption ratio of the cooling tower under the nominal operating conditions on the nameplate based on the nominal operating conditions on the cooling tower's nameplate using the physical model of the cooling tower, and evaluate the energy efficiency of the cooling tower based on the energy efficiency of the cooling tower under the nominal operating conditions on the nameplate and the cooling tower's latest nominal power consumption ratio. The physical model of the cooling tower may characterize the correspondence between the power consumption ratio of the cooling tower and the operating conditions.

[0066] Cooling tower power consumption ratio (ie energy efficiency) PM COT Depends on the energy consumption of the cooling tower and the flow rate of cooling water m cw , namely PM COT =Q COT / m cw The energy consumption of cooling tower is Q COT Depends on the cooling tower fan efficiency η COT 、Air volume of cooling tower m AIR ,Right now

[0067]

[0068] Where Q COT, Q COT,rated is the cooling tower fan energy consumption and the cooling tower fan rated energy consumption, γ air is the load factor, γ air =m AIR / m AIR,rated , c0-c3 are training parameters. A physical model of the cooling tower can be trained based on the operating data of the central air conditioning system. By substituting the nominal operating conditions on the cooling tower's nameplate into the physical model, the power consumption ratio of the cooling tower in the computer room under nominal operating conditions can be obtained. This can then be compared with the latest power consumption ratio of the cooling tower. If the difference exceeds 20%, it indicates that the cooling tower's energy efficiency is low.

[0069] In some embodiments, before the diagnosis module uses the historical operation data, the data processing module may first filter the historical operation data of the central air-conditioning system based on the operation time, parameter limit and PauTa criterion.

[0070] The historical operation data of the central air-conditioning system is filtered based on the operating time. Specifically, the operation of the central air-conditioning system can be divided into the startup phase, the operation phase, and the shutdown phase. Due to the unstable operation of the startup phase and the shutdown phase, the data within 30 minutes after the startup strategy is executed and the data after the shutdown strategy is executed are filtered out.

[0071] The historical operation data of the central air-conditioning system is filtered based on the parameter limits, including: due to the upper and lower boundary constraints of the system operation parameters, the data of the chiller, refrigeration pump, cooling pump, and cooling tower operating outside the range of 30-50Hz are filtered out; the data of the chilled water supply temperature T CHWS , Cooling water return temperature T CWR , Chilled water supply and return temperature difference ΔT CHW , Cooling water supply and return water temperature difference ΔT CW Data outside the upper and lower limits of the set values;

[0072] The historical operating data of the central air-conditioning system are screened based on the PauTa criterion. Specifically, due to the large discreteness of the monitoring parameters and the existence of sensor measurement errors, the parameters are discretized and normally distributed, and the data outside the three standard deviations are filtered out.

[0073] Step 320 , during the verification phase, verifies the accuracy of the sensors and evaluates whether the hydraulic distribution of the chilled water and the cooling water are balanced.

[0074] Since the operating parameters in the central air conditioning system are strongly correlated with each other, the accuracy of the sensor is mainly verified by using the strong correlation between these parameters. aum , RH%) and cooling capacity Q CH , Energy consumption of refrigeration station Q CSThere is a strong correlation; the chilled water supply and return water temperature T CHWS 、T CHWR The inlet and outlet temperatures T of the evaporator on the chiller communication card EVAS 、T EVAR Approximate; cooling water supply temperature T CWS , Cooling water return temperature T CWR The inlet and outlet temperature T of the condenser on the chiller communication card CONS 、T CONR Approximate; Chilled water flow m chw and cooling capacity Q CH , Energy consumption of refrigeration station Q CS There is a strong correlation between energy consumption.

[0075] In some embodiments, the diagnostic module may perform a regression analysis on the cooling capacity or energy consumption of the refrigeration station in the historical operating data and the outdoor wet-bulb temperature to determine the upper confidence limit and the lower confidence limit of the outdoor wet-bulb temperature, and determine whether the outdoor wet-bulb temperature sensor is abnormal based on the outdoor wet-bulb temperature collected at the operating point and the upper confidence limit and the lower confidence limit of the outdoor wet-bulb temperature.

[0076] For example, the diagnostic module can convert the cooling capacity Q in the historical operation data into CH , Energy consumption of refrigeration station Q CS and outdoor wet bulb temperature (T aum , RH%) are respectively subjected to regression analysis. If the operating point is outside the 95% confidence upper limit and the 95% confidence lower limit, it means that the outdoor wet-bulb temperature sensor is abnormal.

[0077] In some embodiments, the diagnostic module can perform regression analysis on the chilled water supply and return water temperatures in the historical operating data and the inlet and outlet temperatures of the evaporator to determine the upper confidence limit and lower confidence limit of the chilled water supply and return water temperatures, and judge whether the chilled water supply and return water temperature sensors are abnormal based on the chilled water supply and return water temperatures collected at the operating point and the upper confidence limit and lower confidence limit of the chilled water supply and return water temperatures.

[0078] For example, the diagnosis module can convert the chilled water supply and return water temperature T in the historical operation data into C Q CSHWS 、T CHWR and the inlet and outlet temperature of the evaporator T EVAS 、T EVAR Perform regression analysis. If the operating point is outside the 95% confidence upper limit and the 95% confidence lower limit, the chilled water supply and return water temperature sensors are abnormal.

[0079] In some embodiments, the diagnostic module can perform regression analysis on the cooling water supply and return water temperatures in the historical operating data and the inlet and outlet temperatures of the condenser to determine the upper confidence limit and lower confidence limit of the cooling water supply and return water temperatures, and based on the cooling water supply and return water temperatures collected at the operating point and the upper confidence limit and lower confidence limit of the cooling water supply and return water temperatures, determine whether the cooling water supply and return water temperature sensors are abnormal.

[0080] For example, the diagnosis module can convert the cooling water supply and return water temperature T in the historical operation data into CWS 、T CWR The inlet and outlet temperatures of the condenser are T CONS 、T CONR Perform regression analysis. If the operating point is outside the 95% confidence upper limit and the 95% confidence lower limit, it means that the cooling water supply and return water temperature sensors are abnormal.

[0081] In some embodiments, the diagnostic module can perform regression analysis on the chilled water flow in the historical operating data and the energy consumption of the refrigeration station to determine the upper confidence limit and the lower confidence limit of the chilled water flow, and judge whether the chilled water flow sensor is abnormal based on the chilled water flow collected at the operating point and the upper confidence limit and the lower confidence limit of the chilled water flow.

[0082] For example, the diagnostic module can convert the cooling capacity Q in the historical operation data into CH , Energy consumption of refrigeration station Q CS Respectively with the chilled water flow m chw Perform regression analysis. If the operating point is outside the 95% confidence upper limit and the 95% confidence lower limit, the chilled water flow sensor is abnormal.

[0083] In some embodiments, the diagnostic module can determine whether the hydraulic distribution of the chilled water is balanced based on the temperature of each return branch pipe of the chilled water, the temperature of the return main pipe, the actual temperature of the working area of ​​the central air-conditioning system, and the set temperature of the working area of ​​the central air-conditioning system.

[0084] The hydraulic balance of chilled water is mainly reflected in the temperature T of each return branch of chilled water. CHWR,branch Check whether the temperature of each cooling area is within 2°C of the temperature of the return water main and the temperature of each cooling area is within 2°C of the temperature setting value. If it is outside the temperature setting value, it indicates that the hydraulic distribution of the chilled water is unbalanced and needs to be debugged.

[0085] In some embodiments, the diagnostic module may determine whether the hydraulic distribution of the cooling water is balanced based on the return water temperature of the cooling tower and the return water main temperature.

[0086] The hydraulic balance of cooling water is mainly reflected in the return water temperature T of each cooling tower. COTR,branchIs it within 2°C of the return main pipe temperature? If it is outside the return main pipe temperature, it indicates that the hydraulic distribution of the cooling water is unbalanced and needs to be debugged.

[0087] Step 330 , during the debugging phase, evaluate the control strategy and system operating parameters of the central air conditioning system.

[0088] The energy consumption of a central air-conditioning system is determined by its operating power and operating time. To control the energy consumption of the system, it is very important to evaluate whether the central air-conditioning system is operating within the non-on / off time range.

[0089] Whether the control strategy fails or is fully executed within the power-on / off timeframe will also affect actual energy consumption. In the power-on / off strategy and load-on / load-off strategy, evaluate whether there are multiple devices on or difficult to shut down. This means that when executing the power-on / off strategy, there are failures to start or shut down devices.

[0090] In some embodiments, the diagnostic module may perform a regression analysis on the chilled water supply temperature in the historical operating data and the load or the outdoor wet-bulb temperature to determine the upper confidence limit and the lower confidence limit of the chilled water supply temperature, and based on the chilled water supply temperature collected at the operating point and the upper confidence limit and the lower confidence limit of the chilled water supply temperature, determine whether the chilled water supply temperature at the operating point is abnormal.

[0091] In terms of system operating parameters, the main parameters include the chilled water supply temperature T CHWS (also known as chiller control parameters), cooling water return temperature T CWR (also known as cooling tower control parameters), chilled water supply and return water temperature difference ΔT CHW (also known as the chilled pump control parameter), cooling water supply and return water temperature difference ΔT CW (Also called cooling pump control parameter). CH , outdoor wet bulb temperature T wet The above four parameters are used as output quantities and are regressed based on historical operating data.

[0092] In some embodiments, the diagnostic module may perform a regression analysis on the chilled water supply temperature in the historical operating data and the load or the outdoor wet-bulb temperature to determine the upper confidence limit and the lower confidence limit of the chilled water supply temperature, and based on the chilled water supply temperature collected at the operating point and the upper confidence limit and the lower confidence limit of the chilled water supply temperature, determine whether the chilled water supply temperature at the operating point is abnormal.

[0093] For example, the chilled water supply temperature T in the historical operation data CHWS With load Q CH Or outdoor wet bulb temperature T wetRegression analysis is performed. If the operating point is outside the 95% confidence upper limit and the 95% confidence lower limit, the chilled water temperature of the operating point is diagnosed as abnormal.

[0094] In some embodiments, the diagnostic module can perform regression analysis on the cooling water return temperature in the historical operating data and the load or outdoor wet-bulb temperature to determine the upper confidence limit and the lower confidence limit of the cooling water return temperature, and based on the cooling water return temperature collected at the operating point and the upper confidence limit and the lower confidence limit of the cooling water return temperature, determine whether the cooling water return temperature at the operating point is abnormal.

[0095] For example, the cooling water return temperature T in the historical operation data CWR With load Q CH Or outdoor wet bulb temperature T wet Regression analysis is performed. If the operating point is outside the 95% confidence upper limit and the 95% confidence lower limit, the cooling water return temperature of the operating point is diagnosed as abnormal.

[0096] In some embodiments, the diagnostic module may perform a regression analysis on the chilled water supply and return water temperature difference in the historical operating data and the load or outdoor wet-bulb temperature to determine the upper confidence limit and the lower confidence limit of the chilled water supply and return water temperature difference, and based on the chilled water supply and return water temperature difference collected at the operating point and the upper confidence limit and the lower confidence limit of the chilled water supply and return water temperature difference, determine whether the chilled water supply and return water temperature difference at the operating point is abnormal.

[0097] For example, the chilled water supply and return temperature difference ΔT in the historical operation data CHW With load Q CH Or outdoor wet bulb temperature T wet Regression analysis is performed. If the operating point is outside the 95% confidence upper limit and the 95% confidence lower limit, the chilled water supply and return water temperature difference at the operating point is diagnosed as abnormal.

[0098] In some embodiments, the diagnostic module may perform regression analysis on the cooling water supply and return water temperature difference in historical operating data and the load or outdoor wet-bulb temperature to determine the upper confidence limit and lower confidence limit of the cooling water supply and return water temperature difference, and based on the cooling water supply and return water temperature difference collected at the operating point and the upper confidence limit and lower confidence limit of the cooling water supply and return water temperature difference, determine whether the cooling water supply and return water temperature difference at the operating point is abnormal.

[0099] For example, the cooling water supply and return water temperature difference ΔT in the historical operation data CW With load Q CH Or outdoor wet bulb temperature T wet Regression analysis is performed. If the operating point is outside the 95% confidence upper limit and the 95% confidence lower limit, the cooling water supply and return water temperature difference at the operating point is diagnosed as abnormal.

[0100] Step 340 , during the operation phase, evaluate the energy efficiency of the central air conditioning system, chillers, refrigeration pumps, cooling pumps, and cooling towers.

[0101] The main factors affecting the energy consumption of central air conditioning systems are load or cooling capacity and the energy efficiency of each device. When evaluating the energy efficiency of the system, the correlation between the system energy consumption and load is mainly evaluated, that is, the correlation between the system energy consumption and cooling capacity and outdoor wet-bulb temperature is evaluated. The diagnostic module can perform regression analysis on the system energy consumption in the historical operation data and the cooling capacity / outdoor wet-bulb temperature. Figure 4a is a schematic diagram of the regression curve of system energy consumption and outdoor wet-bulb temperature according to some embodiments of this specification, such as Figure 4a As shown in FIG, if the system energy consumption of an actual operating point is outside the 95% confidence upper limit and the 95% confidence lower limit, the operating point is diagnosed as an abnormal point.

[0102] Perform regression analysis on the system energy consumption in the historical operation data and the cooling capacity / outdoor wet-bulb temperature respectively to determine the upper and lower confidence limits of the system energy consumption. Based on the system energy consumption of the operating point and the upper and lower confidence limits of the system energy consumption, determine whether the operating point is an abnormal point.

[0103] In some embodiments, the diagnostic module may perform regression analysis on the chiller energy efficiency in the historical operating data with the evaporation temperature, condensation temperature and partial load rate respectively to determine the upper confidence limit and lower confidence limit of the chiller energy efficiency, and judge whether the operating point is an abnormal point based on the chiller energy efficiency at the operating point and the upper confidence limit and lower confidence limit of the chiller energy efficiency.

[0104] The COP of the chiller depends on the evaporation temperature T EVA , condensation temperature T CON and the part load rate PLR, i.e.

[0105]

[0106] Where, T EVA is the evaporation temperature, T CON is the condensing temperature, PLR (part load ratio) is the load rate, and C0-C3 are training parameters. The COP of the chiller in the historical operation data is compared with the evaporation temperature T EVA / Condensation temperature T CON / Partial load rate PLR ​​and chiller COP were regressed and analyzed respectively. Figure 4b is a schematic diagram of a regression curve of energy efficiency and partial load rate of a chiller according to some embodiments of this specification, such as Figure 4bAs shown in the figure, if the actual operating point is outside the 95% confidence upper limit and the 95% confidence lower limit, that is, the COP of the chiller at this operating point is too low (or too high) due to the evaporation temperature T EVA / Condensation temperature T CON / part load rate PLR ​​results.

[0107] In some embodiments, the diagnostic module can perform regression analysis on the energy efficiency of the refrigeration pump in the historical operating data with the cooling capacity, the head of the refrigeration pump, the flow rate of chilled water and the efficiency of the refrigeration pump, determine the upper confidence limit and the lower confidence limit of the energy efficiency of the refrigeration pump, and judge whether the operating point is an abnormal point based on the energy efficiency of the refrigeration pump and the upper confidence limit and the lower confidence limit of the energy efficiency of the refrigeration pump at the operating point.

[0108] Energy efficiency of the refrigeration pump (also known as the refrigeration pump transfer factor) WTF CHWP Depends on the cooling capacity Q CH , refrigeration pump head H CHWP , Chilled water flow m chw (or frequency Hz), efficiency of the refrigeration pump η CHWP ,Right now

[0109]

[0110]

[0111] Where η CHWP,rated is the chilled water pump rated efficiency, PLR CHWP is the chilled water pump load rate, a0-a3 are related parameters. CHWP Respectively with the cooling capacity Q CH / Head of refrigeration pump H CHWP / Chilled water flow m chw (or frequency Hz) / efficiency η of the refrigeration pump CHWP Perform regression analysis, Figure 4c is a schematic diagram of a regression curve of the energy efficiency of a refrigeration pump and the chilled water flow rate according to some embodiments of this specification, such as Figure 4c As shown in the figure, if the actual operating point is outside the 95% confidence upper limit and the 95% confidence lower limit, the energy efficiency WTF of the refrigeration pump at the operating point is CHWP Too low (or too high) is caused by the cooling capacity Q CH / Head of refrigeration pump H CHWP / Chilled water flow m chw (or frequency Hz) / efficiency η of the refrigeration pump CHWP caused by.

[0112] In some embodiments, the diagnostic module can perform regression analysis on the energy efficiency of the cooling pump in the historical operating data and the cooling capacity, the head of the cooling pump, the flow rate of cooling water and the efficiency of the cooling pump to determine the upper confidence limit of the energy efficiency of the cooling pump and the lower confidence limit of the energy efficiency of the cooling pump, and judge whether the operating point is an abnormal point based on the energy efficiency of the cooling pump at the operating point and the upper confidence limit and lower confidence limit of the energy efficiency of the cooling pump.

[0113] Cooling pump energy efficiency (also known as cooling pump delivery factor) WTF CWP Depends on the heat dissipation Q CW , Cooling pump head H CWP , cooling water flow m cw (or frequency Hz), cooling pump efficiency η CWP ,Right now

[0114]

[0115]

[0116] Where η CWP,rated is the cooling water pump rated efficiency, PLR CWP is the cooling water pump load rate, b0-b3 are related parameters. Heat dissipation Q CW Depends on the cooling capacity Q CH The energy efficiency WTF of the cooling pump based on historical operating data is compared with the COP of the chiller. CHWP Respectively with the cooling capacity Q CH / Cooling pump head H CWP / Cooling water flow m cw (or frequency Hz) / efficiency η of cooling pump CWP Perform regression analysis, Figure 4d is a schematic diagram of a regression curve of the energy efficiency of the cooling pump and the cooling water flow rate according to some embodiments of this specification, such as Figure 4d As shown in the figure, if the actual operating point is outside the 95% confidence upper limit and the 95% confidence lower limit, the energy efficiency WTF of the cooling pump at the operating point is CWP Too low (or too high) is caused by the cooling capacity Q CH / Cooling pump head H CWP / Cooling water flow m cw (or frequency Hz) / efficiency η of cooling pump CWP caused by.

[0117] In some embodiments, the diagnostic module may perform regression analysis on the energy efficiency of the cooling pump in the historical operating data and the cooling capacity and the air volume of the cooling tower respectively to determine the upper confidence limit and the lower confidence limit of the energy efficiency of the cooling pump, and judge whether the operating point is an abnormal point based on the energy efficiency of the cooling pump at the operating point and the upper confidence limit and the lower confidence limit of the energy efficiency of the cooling pump.

[0118] Cooling tower energy efficiency (also known as cooling tower transfer factor) WTF COT Depends on the heat dissipation Q CW 、Air volume of cooling tower m AIR (or frequency Hz), that is

[0119]

[0120] Where Q COT,rated is the rated energy consumption of the cooling tower fan, γ air is the load factor, γ air =m AIR / m AIR,rated ,c0-c3 are training parameters. The energy efficiency WTF of the cooling pump in the historical operation data is COT Respectively with the cooling capacity Q CH / Cooling tower air volume m AIR (or frequency Hz) for regression analysis, Figure 4e is a schematic diagram of a regression curve of the energy efficiency and cooling water flow of a cooling tower according to some embodiments of this specification, such as Figure 4e As shown in the figure, if the actual operating point is outside the 95% confidence upper limit and the 95% confidence lower limit, the energy efficiency WTF of the cooling tower at the operating point is COT Too low (or too high) is caused by the cooling capacity Q CH / Air volume of cooling tower m AIR (or frequency Hz).

[0121] In some embodiments, the intelligent diagnosis method and system for the entire life cycle of the central air-conditioning system, in the selection stage, evaluates the equipment selection and energy efficiency of the central air-conditioning system; in the verification stage, verifies the accuracy of the sensor, and evaluates the hydraulic balance of the chilled water and cooling water; in the debugging stage, evaluates the control strategy of the system and the system operating parameters; in the operation stage, evaluates the energy efficiency of the system, chillers, freezing pumps and cooling pumps, and cooling towers. Compared with traditional diagnostic technologies that mainly rely on experience-based model setting and operation management, insufficient experience can easily lead to abnormal operating conditions; and usually due to insufficient data models, only simple statistical analysis can be completed, and the cause of the fault cannot be traced, and self-repair and iterative updates cannot be completed. The present invention can quickly discover faults and trace their sources by evaluating and diagnosing each stage of the central air-conditioning system's entire life cycle, and completes self-iteration and continuous updating of the diagnostic knowledge base through the diagnostic results of each stage, thereby achieving sustainable energy efficiency improvement and stable operation.

[0122] It should be noted that the above description of a central air conditioning system's full lifecycle intelligent diagnostic method is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art will be able to make various modifications and alterations to this central air conditioning system's full lifecycle intelligent diagnostic method under the guidance of this specification. However, such modifications and alterations remain within the scope of this specification.

[0123] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0124] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0125] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in this specification are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0126] Similarly, it should be noted that, in order to simplify the presentation of this specification and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this specification sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this specification requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0127] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. An intelligent diagnostic method for the entire life cycle of a central air-conditioning system, characterized in that: include: During the selection phase, evaluate the equipment selection and energy efficiency of the central air conditioning system; During the calibration phase, the accuracy of the sensors is verified to assess whether the hydraulic distribution of the chilled water and the cooling water is balanced; During the commissioning phase, the control strategy and system operating parameters of the central air-conditioning system are evaluated; During the operation phase, evaluate the energy efficiency of the central air-conditioning system, chillers, refrigeration pumps, cooling pumps and cooling towers; During the selection phase, the equipment selection and energy efficiency of the central air conditioning system are evaluated, including: Obtaining the load rate of the chiller at multiple operating time points and the actual temperature of the working area of ​​the central air-conditioning system; Evaluate the equipment selection of the central air-conditioning system based on the load rate of the chiller at multiple operating time points, the actual temperature of the working area of ​​the central air-conditioning system, and the set temperature of the working area of ​​the central air-conditioning system; During the verification phase, the accuracy of the sensor is verified, including: performing a regression analysis on the cooling capacity or energy consumption of the refrigeration station in the historical operation data and the outdoor wet-bulb temperature to determine an upper confidence limit and a lower confidence limit of the outdoor wet-bulb temperature; and determining whether the outdoor wet-bulb temperature sensor is abnormal based on the outdoor wet-bulb temperature collected at the operation point and the upper confidence limit and the lower confidence limit of the outdoor wet-bulb temperature; Performing regression analysis on the chilled water supply and return water temperatures in historical operating data and the inlet and outlet temperatures of the evaporator to determine the upper confidence limit and lower confidence limit of the chilled water supply and return water temperatures; and judging whether the chilled water supply and return water temperature sensors are abnormal based on the chilled water supply and return water temperatures collected at the operating point and the upper confidence limit and lower confidence limit of the chilled water supply and return water temperatures; Performing regression analysis on the cooling water supply and return water temperatures in historical operating data and the inlet and outlet temperatures of the condenser to determine the upper confidence limit and the lower confidence limit of the cooling water supply and return water temperatures; and judging whether the cooling water supply and return water temperature sensors are abnormal based on the cooling water supply and return water temperatures collected at the operating point and the upper confidence limit and the lower confidence limit of the cooling water supply and return water temperatures; Performing a regression analysis on the chilled water flow rate in the historical operating data and the energy consumption of the refrigeration station to determine an upper confidence limit and a lower confidence limit of the chilled water flow rate, and judging whether a chilled water flow sensor is abnormal based on the chilled water flow rate collected at the operating point and the upper confidence limit and the lower confidence limit of the chilled water flow rate; During the commissioning phase, the control strategy and system operating parameters of the central air conditioning system are evaluated, including: Determine whether the central air conditioning system turns on or off the equipment when executing the power on / off and load increase / decrease strategy; Performing a regression analysis on the chilled water supply temperature in historical operating data and the load or outdoor wet-bulb temperature to determine an upper confidence limit and a lower confidence limit of the chilled water supply temperature; and judging whether the chilled water supply temperature at the operating point is abnormal based on the chilled water supply temperature collected at the operating point and the upper confidence limit and the lower confidence limit of the chilled water supply temperature; Performing a regression analysis on the cooling water return temperature in historical operating data and the load or outdoor wet-bulb temperature to determine an upper confidence limit and a lower confidence limit for the cooling water return temperature; and judging whether the cooling water return temperature at the operating point is abnormal based on the cooling water return temperature collected at the operating point and the upper confidence limit and the lower confidence limit for the cooling water return temperature; Performing a regression analysis on the chilled water supply and return water temperature difference in historical operation data and the load or outdoor wet-bulb temperature to determine an upper confidence limit and a lower confidence limit of the chilled water supply and return water temperature difference; and judging whether the chilled water supply and return water temperature difference at the operating point is abnormal based on the chilled water supply and return water temperature difference collected at the operating point and the upper confidence limit and the lower confidence limit of the chilled water supply and return water temperature difference; Performing a regression analysis on the cooling water supply and return water temperature difference in historical operation data and the load or outdoor wet-bulb temperature to determine an upper confidence limit and a lower confidence limit of the cooling water supply and return water temperature difference; and judging whether the cooling water supply and return water temperature difference at the operating point is abnormal based on the cooling water supply and return water temperature difference collected at the operating point and the upper confidence limit and the lower confidence limit of the cooling water supply and return water temperature difference; During the operation phase, the energy efficiency of the central air conditioning system, chillers, refrigeration pumps, cooling pumps and cooling towers will be evaluated, including: Performing regression analysis on the system energy consumption in the historical operation data and the cooling capacity / outdoor wet-bulb temperature, respectively, to determine the upper confidence limit and the lower confidence limit of the system energy consumption; and judging whether the operating point is an abnormal point based on the system energy consumption at the operating point and the upper confidence limit and the lower confidence limit of the system energy consumption; Performing regression analysis on the chiller energy efficiency in historical operating data with the evaporation temperature, condensing temperature, and part load rate, respectively, to determine an upper confidence limit and a lower confidence limit of the chiller energy efficiency; and determining whether the operating point is an abnormal point based on the chiller energy efficiency at the operating point and the upper confidence limit and the lower confidence limit of the chiller energy efficiency; Performing regression analysis on the energy efficiency of the refrigeration pump in the historical operation data and the cooling capacity, the head of the refrigeration pump, the flow rate of chilled water, and the efficiency of the refrigeration pump, respectively, to determine the upper confidence limit and the lower confidence limit of the energy efficiency of the refrigeration pump; and judging whether the operating point is an abnormal point based on the energy efficiency of the refrigeration pump at the operating point and the upper confidence limit and the lower confidence limit of the energy efficiency of the refrigeration pump; Performing a regression analysis on the energy efficiency of the cooling pump in the historical operation data and the cooling capacity, the head of the cooling pump, the flow rate of cooling water, and the efficiency of the cooling pump to determine the upper confidence limit and the lower confidence limit of the energy efficiency of the cooling pump; and judging whether the operating point is an abnormal point based on the energy efficiency of the cooling pump at the operating point and the upper confidence limit and the lower confidence limit of the energy efficiency of the cooling pump; The energy efficiency of the cooling pump in the historical operating data is regressed against the cooling capacity and the air volume of the cooling tower to determine the upper confidence limit and lower confidence limit of the energy efficiency of the cooling pump. Based on the energy efficiency of the cooling pump at the operating point and the upper confidence limit and lower confidence limit of the energy efficiency of the cooling pump, it is determined whether the operating point is an abnormal point.

2. The intelligent diagnosis method for the entire life cycle of a central air conditioning system according to claim 1, characterized in that: During the selection phase, the equipment selection and energy efficiency of the central air conditioning system are evaluated, including: Establishing a physical model of the chiller, determining the energy efficiency of the chiller under the nameplate nominal operating conditions based on the nameplate nominal operating conditions of the chiller using the physical model of the chiller, and evaluating the energy efficiency of the chiller based on the energy efficiency of the chiller under the nameplate nominal operating conditions and the energy efficiency of the chiller under the latest nominal operating conditions; Establishing a physical model of the refrigeration pump, determining the pump efficiency of the refrigeration pump under the nominal operating conditions on the nameplate of the refrigeration pump using the physical model of the refrigeration pump based on the nominal operating conditions on the nameplate of the refrigeration pump, and evaluating the energy efficiency of the refrigeration pump based on the energy efficiency of the refrigeration pump under the nominal operating conditions on the nameplate of the refrigeration pump and the latest nominal pump efficiency of the refrigeration pump; Establishing a physical model of the cooling pump, determining the pump efficiency of the cooling pump under the nominal operating conditions on the nameplate of the cooling pump using the physical model of the cooling pump based on the nominal operating conditions on the nameplate of the cooling pump, and evaluating the energy efficiency of the cooling pump based on the energy efficiency of the cooling pump under the nominal operating conditions on the nameplate of the cooling pump and the latest nominal pump efficiency of the cooling pump; A physical model of the cooling tower is established, and the power consumption ratio of the cooling tower under the nominal operating conditions on the nameplate of the cooling tower is determined through the physical model of the cooling tower based on the nominal operating conditions on the nameplate of the cooling tower. Based on the energy efficiency of the cooling tower under the nominal operating conditions on the nameplate of the cooling tower and the latest nominal power consumption ratio of the cooling tower, the energy efficiency of the cooling tower is evaluated.

3. The intelligent diagnosis method for the entire life cycle of a central air conditioning system according to claim 1, characterized in that: During the verification phase, the hydraulic distribution of the chilled water and the cooling water is evaluated to determine whether the hydraulic distribution is balanced, including: determining whether the hydraulic distribution of the chilled water is balanced based on the temperatures of the return branch pipes of the chilled water, the temperature of the return main pipe, the actual temperature of the working area of ​​the central air-conditioning system, and the set temperature of the working area of ​​the central air-conditioning system; Based on the return water temperature of the cooling tower and the return water main temperature, it is determined whether the hydraulic distribution of the cooling water is balanced.

4. An intelligent diagnostic system for the entire life cycle of a central air conditioning system, characterized in that: A method for intelligent diagnosis of the entire life cycle of a central air-conditioning system according to any one of claims 1 to 3, comprising: The monitoring module is used to monitor the operating status and operating parameters of each device in the central air-conditioning system during the selection, verification, commissioning and operation stages; A data processing module, used for processing historical operation data of the central air-conditioning system; The diagnostic module is used to evaluate the equipment selection and energy efficiency of the central air-conditioning system during the selection phase; it is also used to verify the accuracy of sensors and evaluate the hydraulic balance of chilled water and cooling water during the verification phase; it is also used to evaluate the system control strategy and system operating parameters during the commissioning phase; and it is also used to evaluate the energy efficiency of the system, chillers, refrigeration pumps and cooling pumps, and cooling towers during the operation phase.

5. The intelligent diagnostic system for the entire life cycle of a central air-conditioning system according to claim 4, characterized in that: The monitoring module is also used for: During the selection phase, obtain the energy consumption, cooling capacity, energy efficiency, evaporation temperature, condensation temperature, part load rate, energy consumption of the chiller pump, chiller pump head, chilled water flow rate, chiller pump efficiency, energy consumption of the cooling pump, cooling pump head, cooling water flow rate, cooling pump efficiency, power consumption ratio of the cooling tower, energy consumption of the cooling tower, cooling water flow rate, fan efficiency of the cooling tower, and air volume of the cooling tower; During the calibration phase, obtain the outdoor wet-bulb temperature, cooling capacity, energy consumption of the refrigeration station, chilled water supply and return temperatures, evaporator inlet and outlet temperatures, cooling water supply and return temperatures, condenser inlet and outlet temperatures, chilled water flow, temperature on each chilled water return branch pipe, and return water temperature of each cooling tower; During the commissioning phase, obtain the chilled water supply temperature, cooling water return temperature, chilled water supply and return temperature difference, cooling water supply and return temperature difference, load, and outdoor wet-bulb temperature; During the operation phase, the energy efficiency, evaporation temperature, condensing temperature, part load rate, energy efficiency of the chiller pump, cooling capacity, head of the chiller pump, flow rate of chilled water, efficiency of the chiller pump, energy efficiency of the cooling pump, heat dissipation, head of the cooling pump, flow rate of cooling water, efficiency of the cooling pump, energy efficiency of the cooling tower, heat dissipation, and air volume of the cooling tower are obtained.

6. An intelligent diagnostic system for the entire life cycle of a central air conditioning system as claimed in claim 4 or 5, characterized in that: The data processing module is further used to filter the historical operating data of the central air-conditioning system based on operating time, parameter limits and PauTa criteria.

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