Green low-carbon energy consumption multi-source data standardization diagnosis method based on cloud platform

Abstract

This invention relates to the field of energy management technology, specifically to a standardized diagnostic method for multi-source green and low-carbon energy consumption based on a cloud platform. By acquiring the outdoor wet-bulb temperature, cooling water temperature, load rate, and operating energy efficiency ratio of each unit under various historical operating conditions, an energy efficiency benchmark response surface is constructed for each unit, using load rate and cooling water return temperature as inputs. The difference between the actual energy efficiency under current operating conditions and the response surface is used to determine the overall unit efficiency deviation, and the cooling-side transmission deviation is determined by combining the influence of cooling water temperature differences, thus calculating the cooling-side contribution ratio. Simultaneously, the cooling tower contribution ratio is determined based on the difference between the cooling water outlet temperature and the outdoor wet-bulb temperature, and the cooling water pump contribution ratio is determined based on the difference between the cooling water return temperature and the outlet temperature. Finally, a progressive fault diagnosis is performed based on these three ratio indicators, achieving equipment-level source localization of energy efficiency problems in the refrigeration system.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, specifically to a standardized diagnostic method for multi-source green and low-carbon energy consumption based on a cloud platform. Background Technology

[0002] Hospital refrigeration systems typically consist of multiple devices working together, including chillers, cooling towers, and cooling water pumps. The coupling relationships between these devices are dynamically changing, and the mixing of old and new equipment is common, meaning the system is constantly in flux. Furthermore, because hospital refrigeration systems are energy-intensive and operate continuously, they account for a significant portion of hospital energy consumption, and their operational efficiency directly determines the overall carbon emission level of the hospital.

[0003] In the current energy consumption diagnosis of hospital refrigeration systems, existing technologies typically rely on cloud platforms to centrally store multi-source data. However, they mainly depend on static benchmarks such as equipment nameplate parameters and fixed attenuation models to assess energy efficiency. In actual applications, due to the dynamic changes in the coupling relationship between refrigeration system equipment and the common practice of mixing new and old equipment, existing technologies cannot accurately identify the causes of energy efficiency reduction in refrigeration systems under different operating conditions. This leads to a serious disconnect between diagnostic conclusions and actual operating conditions, affecting the accuracy of diagnostic conclusions. Summary of the Invention

[0004] To address the technical problem that existing technologies rely on static benchmarks to assess energy efficiency, which cannot accurately identify the causes of energy efficiency reduction in refrigeration systems under different operating conditions, thus affecting the accuracy of diagnostic conclusions, the present invention aims to provide a standardized diagnostic method for green and low-carbon energy consumption based on multi-source data from a cloud platform. The specific technical solution adopted is as follows: This invention proposes a standardized diagnostic method for multi-source green and low-carbon energy consumption based on a cloud platform, the method comprising: Obtain the outdoor wet-bulb temperature, cooling water return temperature, and cooling water outlet temperature of the refrigeration system under each historical operating condition, as well as the load rate and operating energy efficiency ratio of each device in the refrigeration system; determine the energy efficiency benchmark response surface of each device based on the data fitting between the load rate, cooling water return temperature and operating energy efficiency ratio of each device under each historical operating condition. Based on the discrepancy between the operating energy efficiency ratio of each device under the current operating conditions and the discreteness of the energy efficiency reference response surface, the overall unit efficiency deviation of each device is determined; based on the energy efficiency reference response surface, and considering the difference in the influence of different cooling water return temperatures under the current and historical operating conditions, the cooling-side transmission deviation of each device is determined; based on the relative relationship between the cooling-side transmission deviation and the overall unit efficiency deviation, the cooling-side contribution ratio of each device is determined. The contribution percentage of the cooling tower is determined based on the deviation between the cooling water outlet temperature and the outdoor wet-bulb temperature under the current operating conditions; the contribution percentage of the cooling water pump is determined based on the deviation between the cooling water return temperature and the cooling water outlet temperature under the current operating conditions. Based on the contribution ratios of the cooling side, cooling tower, and cooling water pump, fault diagnosis of the refrigeration system is performed.

[0005] Furthermore, the method for obtaining the energy efficiency reference response surface includes: Based on the load rate, cooling water return temperature and operating energy efficiency ratio of each device under various historical operating conditions, surface fitting is performed in three-dimensional space to determine the energy efficiency benchmark response surface of each device.

[0006] Furthermore, the method for obtaining the overall unit efficiency deviation includes: Substitute the load rate and cooling water return temperature of each device under the current operating conditions into the energy efficiency benchmark response surface to determine the corresponding theoretical operating energy efficiency ratio; Based on the difference between the operating energy efficiency ratio of each device under current operating conditions and the theoretical operating energy efficiency ratio, the corresponding total efficiency deviation of the unit is determined.

[0007] Furthermore, the method for obtaining the transmission deviation on the cooling side includes: For each device, based on the distribution of the deviation between the load rate under each historical operating condition and the load rate under the current operating condition, characteristic operating conditions are determined from the historical operating conditions; the characteristic operating condition corresponding to the maximum operating energy efficiency ratio among the characteristic operating conditions is taken as the optimal characteristic operating condition. Substitute the load rate and cooling water return temperature under the optimal characteristic operating conditions into the energy efficiency benchmark response surface to determine the corresponding optimal operating energy efficiency ratio. Based on the difference between the theoretical operating energy efficiency ratio and the optimal operating energy efficiency ratio, the cooling-side transmission deviation of each device is determined.

[0008] Furthermore, the method for obtaining the contribution ratio of the cooling tower includes: Based on the deviation of the cooling water return temperature of the refrigeration system under the current operating conditions from the cooling water return temperature under historical operating conditions, determine the total performance deviation of the cooling side under the current operating conditions. Based on the difference between the cooling water outlet temperature and the outdoor wet-bulb temperature under the current operating conditions, the approximation degree under the current operating conditions is determined; based on the difference between the approximation degree and the preset ideal approximation degree, the corresponding cooling tower contribution is determined. The contribution ratio of the cooling tower under the current operating condition is determined based on the total performance deviation of the cooling side and the contribution of the cooling tower. The contribution ratio of the cooling tower is positively correlated with the contribution of the cooling tower and negatively correlated with the total performance deviation of the cooling side.

[0009] Furthermore, the method for obtaining the total performance deviation on the cooling side includes: Under each historical operating condition, the system load rate of the refrigeration system is determined based on the weighted average of the load rates of all devices; the system operating energy efficiency ratio of the refrigeration system is determined based on the weighted average of the operating energy efficiency ratios of all devices. Based on the deviation distribution of system load rate between each historical operating condition and the current operating condition, similar operating conditions are determined from the historical operating conditions; the similar operating condition corresponding to the maximum system operating energy efficiency ratio among the similar operating conditions is taken as the optimal similar operating condition. The total performance deviation on the cooling side is determined based on the difference between the cooling water return temperature under the current operating conditions and the cooling water return temperature under the optimal similar operating conditions.

[0010] Furthermore, the process for obtaining the contribution ratio of the cooling water pump includes: The temperature rise under the current operating conditions is determined based on the difference between the cooling water return temperature and the cooling water outlet temperature; the corresponding cooling water pump contribution is determined based on the difference between the temperature rise and the preset ideal temperature rise. The contribution ratio of the cooling water pump under the current operating condition is determined based on the total performance deviation of the cooling side and the contribution of the cooling water pump. The contribution ratio of the cooling water pump is positively correlated with the contribution of the cooling water pump and negatively correlated with the total performance deviation of the cooling side.

[0011] Furthermore, the process of fault diagnosis of the refrigeration system based on the contribution ratio of the cooling side, the contribution ratio of the cooling tower, and the contribution ratio of the cooling water pump includes: For each device, if the contribution ratio of the cooling side is less than the preset cooling side diagnostic threshold, it is determined to be an internal device fault. If the contribution ratio of the cooling side is greater than or equal to the preset cooling side diagnostic threshold, and the contribution ratio of the cooling tower is greater than the preset cooling tower diagnostic threshold, then the cooling tower is determined to be faulty. If the contribution ratio of the cooling side is greater than or equal to the preset cooling side diagnostic threshold, and the contribution ratio of the cooling water pump is greater than the preset cooling water pump diagnostic threshold, then the cooling water pump is determined to be faulty. If the contribution ratio of the cooling side is greater than or equal to the preset cooling side diagnostic threshold, and the contribution ratio of the cooling tower is less than or equal to the preset cooling tower diagnostic threshold, and the contribution ratio of the cooling water pump is less than or equal to the preset cooling water pump diagnostic threshold, then it is determined to be a cooling side coupling mismatch.

[0012] Furthermore, a local weighted regression algorithm is used for surface fitting.

[0013] This invention also proposes a cloud-based green and low-carbon energy consumption multi-source data standardization diagnostic system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a cloud-based green and low-carbon energy consumption multi-source data standardization diagnostic method.

[0014] The present invention has the following beneficial effects: This invention determines the energy efficiency benchmark response surface for each device by fitting data on load rate, cooling water return temperature, and operating energy efficiency ratio under various historical operating conditions. This solves the problem in existing technologies where static benchmarks cannot reflect the true aging degree and coupling characteristics of the equipment, enabling dynamic and accurate modeling of each device under different load rates and water temperatures. By determining the overall unit efficiency deviation of each device, real-time quantitative evaluation of the comprehensive energy efficiency performance of each device is achieved. By determining the cooling-side transmission deviation of each device, the impact of water temperature on unit energy efficiency is analyzed, thereby achieving independent quantification of the impact of cooling-side faults. By determining the contribution ratio of the cooling tower and the cooling water pump, cooling-side faults are separated into two levels: cooling tower and cooling water pump, enabling independent diagnosis of both. By performing fault diagnosis of the refrigeration system based on the aforementioned contribution ratios of the cooling-side, cooling tower, and cooling water pump, the causes of energy efficiency reduction in the refrigeration system under different operating conditions are accurately identified, improving the accuracy of diagnostic conclusions. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 The flowchart illustrates a cloud-based, multi-source data standardization diagnostic method for green and low-carbon energy consumption, as provided in one embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a cloud-based green and low-carbon energy consumption multi-source data standardization diagnostic method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the green and low-carbon energy consumption multi-source data standardization diagnostic method provided by the present invention.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a cloud-based, multi-source data standardization diagnostic method for green and low-carbon energy consumption, provided by an embodiment of the present invention. The method includes: S101: Obtain the outdoor wet-bulb temperature, cooling water return temperature, and cooling water outlet temperature of the refrigeration system under each historical operating condition, as well as the load rate and operating energy efficiency ratio of each device in the refrigeration system; determine the energy efficiency reference response surface of each device based on the data fitting of the load rate, cooling water return temperature and operating energy efficiency ratio of each device under each historical operating condition.

[0021] Hospital refrigeration systems typically consist of multiple devices working in coordination, including chillers, cooling towers, and cooling water pumps. The cooling towers and cooling water pumps constitute the cooling side of the system. In actual operation, the energy efficiency of each device is not constant but is influenced by both external weather conditions and internal load demands. Specifically, the outdoor wet-bulb temperature determines the cooling tower's heat dissipation capabilities, directly affecting the theoretical minimum temperature the cooling water can reach. The cooling water return temperature reflects the actual heat dissipation provided to the chiller on the cooling side. The cooling water outlet temperature characterizes the heat dissipation of the chiller and the inlet water status of the cooling tower. Meanwhile, the load rate of each device reflects the proportion of cooling load it currently undertakes, while the operating energy efficiency ratio (EER) is a direct indicator of its energy conversion efficiency.

[0022] If only single-dimensional data is collected, such as focusing solely on the relationship between load rate and energy efficiency ratio, the impact of changes in external weather conditions on system performance will be ignored. This leads to significant fluctuations in the energy efficiency ratio corresponding to the same load rate under different seasons or weather conditions, making it impossible to establish a stable evaluation benchmark. Therefore, this invention obtains the outdoor wet-bulb temperature, cooling water return temperature, and cooling water outlet temperature of the refrigeration system under each historical operating condition, as well as the load rate and operating energy efficiency ratio of each device in the refrigeration system. This provides a complete data foundation for subsequent energy efficiency diagnosis, enabling energy efficiency evaluation to eliminate external interference factors and focus on the system's own problems.

[0023] In one specific implementation of this invention, to avoid high-frequency data redundancy, a condition-triggered slicing mechanism is introduced. Historical operating status data of each device is obtained through the hospital's refrigeration system cloud platform. When a chiller unit is detected to start from a standstill, the frequency of the chilled water pump changes by more than 5%, or the cooling tower fan starts or stops, continuous data within a 15-minute time window before and after the event occurs is automatically extracted to form a condition-triggered event dataset. This dataset includes the outdoor wet-bulb temperature, cooling water return temperature, and cooling water outlet temperature at each sampling interval (set to 1 minute in this embodiment), as well as the load rate and energy efficiency ratio of each device in the refrigeration system. The load rate and operating energy efficiency ratio can be obtained from the data analysis module built into the cloud platform. Specifically, the real-time load rate and real-time operating energy efficiency ratio of the device are obtained from the data analysis module built into the cloud platform. The real-time load rate and real-time operating energy efficiency ratio are mapped to the range of [0,1] using the minimum-maximum normalization algorithm. The reference data for the real-time load rate and real-time operating energy efficiency ratio when performing the minimum-maximum normalization algorithm are the real-time load rate and real-time operating energy efficiency ratio of the device under all historical operating conditions.

[0024] It should be noted that, in order to filter out noise, reduce data redundancy, and eliminate the influence of outliers, for each load case dataset, the third quantile of each parameter in the dataset is used as the representative value of each parameter for that historical load case. For example, the third quantile of all load rates in each load case dataset is used as the representative value of the load rate for each historical load case. The data used in subsequent analyses are all representative values ​​of each parameter.

[0025] In hospital refrigeration systems, different chiller units often vary in model, capacity, factory energy efficiency rating, and time of commissioning, leading to significant differences in aging and maintenance levels. Existing technologies typically use fixed attenuation models, which fail to reflect the individual differences of each unit. For example, a ten-year-old chiller unit and a newly commissioned variable frequency chiller unit may have vastly different actual operating energy efficiencies, even under the same load rate and cooling water inlet temperature. If a uniform energy efficiency evaluation standard is applied to all equipment, older units are easily misjudged as having abnormal energy efficiency, when in reality their energy efficiency performance may already be within the normal range for their current aging state. Therefore, this invention determines the energy efficiency benchmark response surface for each unit based on the data fitting of load rate, cooling water return temperature, and operating energy efficiency ratio under various historical operating conditions. This surface represents the statistically optimal energy efficiency level actually achieved by the unit under different load rates and cooling water temperatures over a period of time. When equipment performance declines due to aging, its historical data points shift downwards, and the fitted surface adjusts accordingly, ensuring that the benchmark remains synchronized with the actual state of the equipment. This mechanism ensures that the benchmark for energy efficiency diagnosis is not a static value that remains unchanged, but a reference system that can dynamically evolve with the aging process of equipment, thereby fundamentally solving the problem of mismatch between static benchmark and actual operating conditions.

[0026] S102: Based on the discrepancy between the operating energy efficiency ratio of each device under the current operating conditions and the discrete difference of the energy efficiency reference response surface, determine the overall unit efficiency deviation of each device; based on the energy efficiency reference response surface, determine the cooling-side transmission deviation of each device according to the influence of different cooling water return temperatures under the current and historical operating conditions; determine the cooling-side contribution ratio of each device according to the relative relationship between the cooling-side transmission deviation and the overall unit efficiency deviation.

[0027] The energy efficiency reference response surface obtained by S101 reflects the theoretical operating energy efficiency ratio that each device can achieve under arbitrary load rate and arbitrary cooling water return temperature. However, in actual operation, due to equipment aging and external temperature fluctuations, the actual operating energy efficiency ratio often differs from the theoretical operating energy efficiency ratio given by the energy efficiency reference response surface. To comprehensively quantify this difference, this invention determines the overall unit efficiency deviation of each device based on the discrete difference between the operating energy efficiency ratio of each device under the current operating conditions and the energy efficiency reference response surface. This deviation reflects the gap between the current comprehensive energy efficiency performance of the device and its historical best level.

[0028] It should be noted that the operating data under the current operating conditions includes outdoor wet-bulb temperature, cooling water return temperature, cooling water outlet temperature, as well as the load rate and operating energy efficiency ratio of each device in the refrigeration system. The method of obtaining these data is the same as that of the historical operating data in S101, and the specific acquisition process will not be described in detail.

[0029] Considering that the overall unit efficiency deviation is a comprehensive indicator, its causes may stem from two aspects: firstly, the return water temperature of the cooling water supplied by the cooling side deviates from the ideal level; secondly, the internal state of the equipment itself has changed. If these two causes cannot be distinguished, the reasons for the reduced energy efficiency of the refrigeration system under current operating conditions cannot be identified. To address this issue, this invention starts with the difference in benchmark energy efficiency between the current and historical operating conditions at different cooling water return water temperatures. Based on the energy efficiency benchmark response surface, and according to the difference in the impact of different cooling water return water temperatures between the current and historical operating conditions, the cooling-side transmission deviation of each piece of equipment is determined. It should be noted that the cooling-side transmission deviation is purely caused by the water temperature deviating from the ideal level and is unrelated to the equipment's own state, thus separating the external factors in the overall unit efficiency deviation and quantifying the degree of impact of cooling-side problems on equipment energy efficiency. To further determine the importance of cooling-side problems in the overall energy efficiency problem, this invention determines the cooling-side contribution ratio of each piece of equipment based on the relative relationship between the cooling-side transmission deviation and the overall unit efficiency deviation. This percentage directly reflects the weight of the external factor of high water temperature in the total energy efficiency loss: when the percentage is large, it indicates that the main cause of the energy efficiency loss problem is on the cooling side, and the cooling tower and cooling water pump should be checked first; when the percentage is low, it indicates that the problem mainly originates from the equipment itself, and the focus of diagnosis should be shifted to the internal condition of the unit, thereby providing a basis for decision-making in subsequent fault diagnosis.

[0030] S103: Determine the corresponding contribution ratio of the cooling tower based on the deviation between the cooling water outlet temperature and the outdoor wet-bulb temperature under the current operating conditions; determine the corresponding contribution ratio of the cooling water pump based on the deviation between the cooling water return temperature and the cooling water outlet temperature under the current operating conditions.

[0031] After analyzing the contribution ratio of the cooling side through S102, if it is determined that the main cause of the energy efficiency problem lies in the cooling side, it is necessary to further locate the specific weak links within the cooling side. The cooling side consists of a cooling tower and a cooling water pump connected in series. The two have different mechanisms of influence on the cooling water temperature: the cooling tower is responsible for dissipating the heat in the cooling water to the atmospheric environment, and its heat dissipation capacity directly determines the theoretical minimum temperature to which the cooling water can be cooled; the cooling water pump is responsible for transporting the cooling water from the cooling tower to the chiller unit, and the degree of flow matching affects the temperature rise of the cooling water during the transportation process.

[0032] The physical significance of the difference between the cooling water outlet temperature and the outdoor wet-bulb temperature lies in the following: Under ideal conditions, the cooling tower can reduce the cooling water temperature to a level close to the outdoor wet-bulb temperature through evaporative cooling. The smaller the difference, the better the cooling tower's heat dissipation effect; conversely, the larger the difference, the less effective the cooling tower's heat dissipation. Therefore, this invention determines the corresponding contribution ratio of the cooling tower based on the deviation between the cooling water outlet temperature and the outdoor wet-bulb temperature under current operating conditions, thereby quantifying the weight of cooling tower failure in the overall cooling-side problem and providing a direct basis for quantifying the probability of cooling tower failure.

[0033] The difference between the cooling water return temperature and the cooling water outlet temperature reflects the temperature rise of the cooling water as it flows through the cooling water pump and pipelines in refrigeration engineering. It is a comprehensive indicator for measuring the flow matching status of the cooling water pump. Its physical significance lies in the following: Under ideal operating conditions, when the pump operating frequency is at the theoretical frequency, the temperature rise of the cooling water during circulation should remain within the design range or the historical optimal level. When the pump operating frequency deviates from the theoretical frequency, insufficient flow will lead to inadequate heat dissipation of the cooling water in the cooling tower, while excessive flow will increase the pump's workload and may cause frictional temperature rise in the pipelines. Both will ultimately manifest as an abnormal increase in the cooling water return temperature relative to the outlet temperature. Therefore, this invention determines the corresponding contribution ratio of the cooling water pump based on the deviation of the cooling water return temperature from the cooling water outlet temperature under current operating conditions, thereby quantifying the weight of cooling water pump failure in the overall cooling-side problem and providing a direct basis for quantifying the probability of cooling water pump failure.

[0034] S104: Based on the contribution ratio of the cooling side, the contribution ratio of the cooling tower, and the contribution ratio of the cooling water pump, perform fault diagnosis of the refrigeration system.

[0035] The contribution percentages of the cooling side, cooling tower, and cooling water pump are analyzed to address different levels of problems in energy efficiency diagnosis: the cooling side contribution percentage analyzes whether energy efficiency loss originates from the cooling side; the cooling tower contribution percentage analyzes whether, if energy efficiency loss is attributed to the cooling side, it originates from the cooling tower; and the cooling water pump contribution percentage analyzes whether, if energy efficiency loss is attributed to the cooling side, it originates from the cooling water pump. These three indicators progressively form a complete diagnostic chain from the overall system to the specific components, and from the system to the equipment. Furthermore, considering that all three percentage indicators are dimensionless, they intuitively reflect the weight of each link's problem in the overall energy efficiency problem. Therefore, this invention uses the aforementioned contribution percentages of the cooling side, cooling tower, and cooling water pump for fault diagnosis of refrigeration systems.

[0036] In summary, this invention, by fitting data on the load rate, cooling water return temperature, and operating energy efficiency ratio of each device under various historical operating conditions, determines the energy efficiency benchmark response surface for each device. This solves the problem in existing technologies where static benchmarks cannot reflect the true aging degree and coupling characteristics of the equipment, enabling dynamic and accurate modeling of each device under different load rates and water temperatures. By determining the overall unit efficiency deviation of each device, real-time quantitative evaluation of the comprehensive energy efficiency performance of each device is achieved. By determining the cooling-side transmission deviation of each device, the impact of water temperature on unit energy efficiency is analyzed, thereby achieving independent quantification of the impact of cooling-side faults. By determining the contribution ratio of the cooling tower and the cooling water pump, cooling-side faults are separated into two levels: cooling tower and cooling water pump, enabling independent diagnosis of both. By performing fault diagnosis of the refrigeration system based on the aforementioned contribution ratios of the cooling-side, cooling tower, and cooling water pump, the causes of energy efficiency reduction in the refrigeration system under different operating conditions are accurately identified, improving the accuracy of diagnostic conclusions.

[0037] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the energy efficiency reference response surface includes: Because the energy efficiency of a refrigeration system is the result of multivariate coupling, for the same equipment under the same load rate, when the cooling water return temperature increases, its condensing pressure rises, compressor energy consumption increases, and the corresponding operating energy efficiency ratio decreases; when the cooling water return temperature decreases, the corresponding operating energy efficiency ratio increases accordingly. Conversely, at the same cooling water return temperature, changes in load rate will also cause fluctuations in energy efficiency. If only a two-dimensional relationship between load rate and operating energy efficiency ratio is established, it is impossible to distinguish whether the energy efficiency change is caused by changes in load rate or changes in cooling water temperature, which will lead to ambiguity in the benchmark itself. Therefore, this invention constructs a three-dimensional energy efficiency benchmark response surface with load rate and cooling water return temperature as dual inputs and operating energy efficiency ratio as output. By fitting the surface in three-dimensional space according to the load rate, cooling water return temperature, and operating energy efficiency ratio of each equipment under various historical operating conditions, the energy efficiency benchmark response surface of each equipment is determined.

[0038] It should be noted that the energy efficiency benchmark response surface of each device is a globally fixed static mathematical model. By inputting the load rate and cooling water return temperature of each device, the theoretical operating energy efficiency ratio under the current operating conditions can be obtained.

[0039] In one specific implementation of this invention, three-dimensional data points are constructed based on the load rate, cooling water return temperature, and operating energy efficiency ratio of each device in the refrigeration system under each historical operating condition. Specifically, each device in the [missing information]... The form of the three-dimensional data points for each historical working condition is as follows: , Indicates the first Load rate under historical operating conditions Indicates the first Energy efficiency ratio under historical operating conditions Indicates the first Cooling water return temperature under historical operating conditions. A local weighted regression algorithm is used, with the load rate of each 3D data point as the x-axis and the cooling water return temperature as the y-axis, determining the 3D data points within a 3×3 neighborhood of each point to be fitted. The load rate and cooling water return temperature of the 3D data points within the neighborhood of the point to be fitted are used as input variables, and the corresponding operating energy efficiency ratio is used as the output variable. A surface fitting is performed on all historical data points in 3D space to generate a linear surface, which serves as the energy efficiency benchmark response surface for the equipment. The local weighted regression algorithm is a well-known technique in the art, and its implementation process will not be elaborated here.

[0040] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the overall unit efficiency deviation includes: Since the energy efficiency reference response surface of each device is only a static mathematical model, it needs to be correlated with the current actual operating conditions to play a diagnostic role. Under the current operating conditions, each device has a specific load rate and a specific cooling water return temperature, and these two parameters together determine the operating conditions of the device at the current moment. Therefore, this embodiment of the invention substitutes the load rate and cooling water return temperature of each device under the current operating conditions into the energy efficiency reference response surface to determine the corresponding theoretical operating energy efficiency ratio, which is used to characterize the normal level of the operating energy efficiency ratio that the device should exhibit under the current aging and maintenance level and under the current operating conditions.

[0041] The operating energy efficiency ratio under current operating conditions reflects the actual operating energy efficiency of the equipment at the current moment, while the theoretical operating energy efficiency ratio reflects the ideal operating energy efficiency level that the equipment should achieve under current conditions. Therefore, this invention determines the corresponding total efficiency deviation of the unit based on the difference between the operating energy efficiency ratio of each piece of equipment under current operating conditions and the theoretical operating energy efficiency ratio. This comprehensively reflects the impact of equipment aging and external temperature fluctuations on equipment energy efficiency. The larger the value, the more serious the energy efficiency loss problem of the equipment.

[0042] In one specific implementation of this invention, the absolute value of the difference between the operating energy efficiency ratio of each device under the current operating conditions and its corresponding theoretical operating energy efficiency ratio is taken as the total efficiency deviation of each device under the current operating conditions.

[0043] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the cooling-side transmission deviation includes: When assessing the impact of cooling water temperature on equipment energy efficiency, a fair benchmark needs to be established. Under different historical operating conditions, the load rate of the equipment varies, and the load rate itself has a significant impact on energy efficiency. Directly comparing energy efficiency values ​​under different load rates makes it impossible to distinguish whether the difference in energy efficiency is caused by different load rates or different cooling water temperatures. Therefore, this invention, for each piece of equipment, identifies characteristic operating conditions from historical operating conditions based on the deviation distribution of the load rate between each historical operating condition and the current operating condition. This screening ensures that subsequent comparisons are conducted under conditions of essentially the same load rate, eliminating the interference of load rate differences on energy efficiency comparisons.

[0044] In one specific implementation of this invention, the absolute value of the difference between the load rate under the current operating condition and the load rate under each historical operating condition is used as the load rate difference index for each historical operating condition; historical operating conditions with a load rate difference index less than or equal to 5% are selected as characteristic operating conditions, which can be adjusted according to the specific implementation scenario.

[0045] Under similar historical operating conditions, the cooling water return temperature and the operating energy efficiency ratio of the equipment differ depending on the historical operating condition. Therefore, this invention defines the characteristic operating condition corresponding to the maximum operating energy efficiency ratio as the optimal characteristic operating condition. This represents the best energy efficiency performance historically achieved by the equipment under the same or similar load conditions, and the corresponding cooling water return temperature is the theoretical optimal water temperature at that load rate. Therefore, this invention substitutes the load rate and cooling water return temperature of each device under the optimal characteristic operating condition into the energy efficiency reference response surface to determine the corresponding optimal operating energy efficiency ratio.

[0046] It should be noted that if the maximum operating energy efficiency ratio corresponds to multiple characteristic operating conditions, the characteristic operating condition corresponding to the minimum load rate difference index is selected as the optimal characteristic operating condition.

[0047] Considering that the theoretical operating energy efficiency ratio reflects the energy efficiency that the equipment should achieve under the current actual cooling water temperature, while the optimal operating energy efficiency ratio reflects the energy efficiency that the equipment should achieve under the theoretical optimal cooling water temperature, the difference between the two is entirely due to the deviation of the cooling water temperature from the theoretical optimal value and is unrelated to the equipment's own condition. Therefore, this invention determines the cooling-side transmission deviation of each piece of equipment based on the difference between the theoretical operating energy efficiency ratio and the optimal operating energy efficiency ratio.

[0048] In one specific implementation of this invention, the absolute value of the difference between the theoretical operating energy efficiency ratio and the optimal operating energy efficiency ratio is used as the cooling-side transmission deviation for each device.

[0049] In one specific implementation of this invention, the cooling-side transmission deviation is used as the numerator, and the overall unit efficiency deviation is used as the denominator. The cooling-side contribution ratio of each device is determined by ratio calculation.

[0050] It should be noted that when the overall unit efficiency deviation is 0, it indicates that the equipment has no energy efficiency issues, and no further attribution analysis is needed. Furthermore, since the overall unit efficiency deviation is the difference between the actual operating energy efficiency and the baseline value of the energy efficiency response surface, it comprehensively reflects the impact of two factors on equipment energy efficiency: the deviation of cooling water temperature from the theoretical optimum and the deterioration of the equipment's own condition (such as heat exchanger scaling, insufficient refrigerant, and decreased compressor efficiency). The cooling-side transmission deviation, however, only reflects the energy efficiency loss caused by the deviation of cooling water temperature from the theoretical optimum and is unrelated to the equipment's own condition. It is a component separated from the overall unit efficiency deviation; therefore, the cooling-side transmission deviation is always less than or equal to the overall unit efficiency deviation.

[0051] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the contribution ratio of the cooling tower includes: The cooling water return temperature is the final heat dissipation condition provided to the chiller unit by the cooling side, comprehensively reflecting the combined effect of the cooling tower and the cooling water pump. In actual operation, a decrease in the cooling tower's heat dissipation capacity or a mismatch in the pump flow rate will lead to an increase in the cooling water return temperature. Therefore, this invention quantifies the gap between the overall cooling side performance and historical normal levels by analyzing the deviation of the cooling water return temperature of the refrigeration system under current operating conditions relative to historical operating conditions, thereby determining the total performance deviation of the cooling side under current operating conditions.

[0052] The difference between the cooling water outlet temperature and the outdoor wet-bulb temperature is called the approximation degree in refrigeration engineering, and it is a core indicator for measuring the heat dissipation capacity of a cooling tower. Therefore, this invention determines the approximation degree under current operating conditions based on the difference between the cooling water outlet temperature and the outdoor wet-bulb temperature. Since the function of a cooling tower is to dissipate heat from the cooling water to the atmosphere, ideally, the cooling water outlet temperature should be close to the outdoor wet-bulb temperature. When the cooling tower's heat dissipation capacity decreases due to factors such as dirty packing, reduced fan efficiency, or uneven water distribution, the direct manifestation is an increase in the approximation degree. Therefore, this invention determines the corresponding cooling tower contribution based on the difference between the approximation degree and the preset ideal approximation degree.

[0053] In one specific implementation of this invention, the absolute value of the difference between the cooling water outlet temperature and the outdoor wet-bulb temperature is taken as the approximation degree under the current operating condition; the approximation degree of the cooling tower under the optimal characteristic operating condition is selected as the preset ideal approximation degree; and the absolute value of the difference between the approximation degree under the current operating condition and the preset ideal approximation degree is taken as the corresponding cooling tower contribution.

[0054] Since the total performance deviation on the cooling side is the combined result of the cooling tower and the water pump, and the cooling tower contribution only reflects the influence of the cooling tower component, this invention determines the cooling tower contribution ratio under the current operating condition based on the total performance deviation on the cooling side and the cooling tower contribution ratio. The cooling tower contribution ratio is positively correlated with the cooling tower contribution ratio and negatively correlated with the total performance deviation on the cooling side.

[0055] In one specific implementation of this invention, the contribution of the cooling tower is used as the numerator, and the total performance deviation on the cooling side is used as the denominator. The proportion of the cooling tower's contribution is determined by a ratio calculation.

[0056] It should be noted that if the total performance deviation on the cooling side is 0, it indicates that there are no performance problems on the cooling side as a whole (including the cooling tower and cooling water pump), and no further attribution analysis of the cooling tower and water pump is needed. If the total unit efficiency deviation is also 0, it indicates that there is no energy loss in the system as a whole; if the total unit efficiency deviation is greater than 0, it indicates that the energy loss is attributable to a fault in each piece of equipment. Furthermore, since the total performance deviation on the cooling side comprehensively reflects the impact of the cooling tower's heat dissipation capacity and the flow matching of the cooling water pump on the cooling water return temperature, the cooling tower contribution only reflects the increase in cooling water return temperature caused by insufficient cooling tower heat dissipation capacity, and is unrelated to the cooling water pump and piping. It is a component separated from the total performance deviation on the cooling side; therefore, the cooling tower contribution is always less than or equal to the total performance deviation on the cooling side.

[0057] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the total performance deviation on the cooling side includes: Hospital refrigeration systems typically contain multiple chiller units, and the load rate and operating energy efficiency ratio of each unit may differ. Focusing only on individual devices while ignoring the overall system operation makes it impossible to comprehensively assess the performance of the cooling side (cooling towers and cooling water pumps), as the cooling side serves the entire refrigeration system rather than a specific device. Therefore, in this embodiment of the invention, under each historical operating condition, the system load rate of the refrigeration system is determined based on the weighted average of the load rates of all devices; and the system operating energy efficiency ratio of the refrigeration system is determined based on the weighted average of the operating energy efficiency ratios of all devices.

[0058] As an example, system load rate The calculation formula can be expressed as:

[0059] in, Indicates the total number of devices in the refrigeration system; Indicates the first The rated operating power of each piece of equipment can be obtained from the factory parameters of each piece of equipment; This represents the sum of the rated operating power of all equipment in the refrigeration system; Indicates the first Load rate of the equipment.

[0060] It should be noted that the calculation method for system operating energy efficiency ratio is the same as that for system load rate. Simply replace the load rate in the above formula with operating energy efficiency ratio to obtain the system operating energy efficiency ratio.

[0061] Cooling water return temperature is influenced by both system load rate and external meteorological conditions (outdoor wet-bulb temperature). The fluctuation range of cooling water return temperature varies under different system load rates. A higher load rate results in greater heat dissipation from the refrigeration system, leading to a corresponding increase in cooling water return temperature; conversely, a lower load rate results in a corresponding decrease in cooling water return temperature. Directly comparing cooling water return temperatures under different load rates makes it impossible to distinguish whether the temperature difference is caused by changes in load rate or by changes in the cooling system's own performance. Therefore, this invention identifies similar operating conditions from historical operating conditions based on the deviation distribution between the system load rate of each historical operating condition and the current operating condition. This screening process eliminates the influence of system load rate differences on cooling water return temperature, ensuring that subsequent comparisons are conducted under the same or similar system load conditions, thus allowing changes in cooling water return temperature to accurately reflect changes in the cooling system's own performance.

[0062] In one specific implementation of this invention, the absolute value of the difference between the system load rate under the current operating condition and the system load rate under each historical operating condition is used as the system load rate difference index for each historical operating condition; historical operating conditions with a system load rate difference index of less than or equal to 5% are selected as similar operating conditions, which can be adjusted according to the specific implementation scenario.

[0063] Among historical operating conditions with similar system load rates, the cooling water return temperature varies under different conditions, resulting in different overall system energy efficiency ratios. The operating condition with the highest system energy efficiency ratio represents the best energy efficiency performance achieved by the entire refrigeration system under the same system load rate. The cooling water return temperature corresponding to this operating condition is the theoretically optimal cooling water return temperature under that load rate. Therefore, this invention uses the similar operating condition corresponding to the maximum system energy efficiency ratio among the similar operating conditions as the optimal similar operating condition.

[0064] It should be noted that if the maximum operating energy efficiency ratio corresponds to multiple similar operating conditions, the similar operating condition corresponding to the minimum system load rate difference index shall be selected as the optimal similar operating condition.

[0065] The cooling water return temperature under current operating conditions reflects the actual heat dissipation effect of the cooling side at the current moment, while the cooling water return temperature under optimal similar operating conditions reflects the best heat dissipation effect previously achieved by the cooling side under the same system load rate. The difference between the two excludes the influence of changes in system load rate and purely reflects the degree of deviation of the cooling side's own performance. Therefore, this invention determines the total performance deviation of the cooling side based on the difference between the cooling water return temperature of each device under current operating conditions and the cooling water return temperature under the optimal similar operating conditions.

[0066] In one specific implementation of this invention, the absolute value of the difference between the cooling water return temperature under the current operating condition and the cooling water return temperature under the optimal similar operating condition is taken as the total performance deviation on the cooling side.

[0067] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the contribution ratio of the cooling water pump includes: As cooling water flows from the cooling tower outlet through the cooling water pump and delivery pipelines, and finally returns to the chiller unit, a certain temperature rise occurs due to heat generated by the pump's work and heat absorption by the pipelines. Under ideal operating conditions, when the pump operates at a frequency matching the current load, this temperature rise should remain within the design range or the historical best level. The difference between the cooling water return temperature and the cooling water outlet temperature directly reflects the actual temperature rise of the cooling water during delivery and circulation, and is a direct physical quantity for measuring the working quality of the pump and delivery process. Therefore, this invention determines the temperature rise value under the current operating conditions based on the difference between the cooling water return temperature and the cooling water outlet temperature; and determines the corresponding cooling water pump contribution based on the difference between the stated temperature rise value and the preset ideal temperature rise value. In one specific implementation of this invention, the absolute value of the difference between the cooling water return temperature and the cooling water outlet temperature under the current operating condition is taken as the temperature rise value under the current operating condition; the temperature rise value of the cooling water pump under the optimal characteristic operating condition is selected as the preset ideal temperature rise value; and the absolute value of the difference between the temperature rise value under the current operating condition and the preset ideal temperature rise value is taken as the corresponding cooling water pump contribution.

[0068] The total performance deviation on the cooling side is the combined result of the cooling tower and the water pump, while the contribution of the cooling water pump only reflects the influence of the water pump component. Therefore, this invention determines the contribution ratio of the cooling water pump under the current operating condition based on the total performance deviation on the cooling side and the contribution of the cooling water pump; the contribution ratio of the cooling water pump is positively correlated with the contribution of the cooling water pump and negatively correlated with the total performance deviation on the cooling side.

[0069] In one specific implementation of this invention, the contribution of the cooling water pump is used as the numerator, and the total performance deviation on the cooling side is used as the denominator. The contribution ratio of the cooling water pump is determined by a ratio calculation. The case where the total performance deviation on the cooling side is 0 has already been analyzed in the process of obtaining the contribution ratio of the cooling tower and will not be repeated here.

[0070] It should be noted that since the total performance deviation on the cooling side comprehensively reflects the impact of the cooling tower's heat dissipation capacity and the flow matching of the cooling water pump on the cooling water return temperature, the cooling water pump contribution only reflects the additional increase in cooling water return temperature caused by the mismatch in cooling water pump flow rate, and is unrelated to the cooling tower and piping. It is a component separated from the total performance deviation on the cooling side; therefore, the cooling water pump contribution is always less than or equal to the total performance deviation on the cooling side.

[0071] Preferably, in some possible implementations of the embodiments of the present invention, the process of diagnosing the faults of the refrigeration system based on the contribution ratio of the cooling side, the contribution ratio of the cooling tower, and the contribution ratio of the cooling water pump includes: Since the contribution ratio of the cooling side reflects the weight of the energy loss caused by the deviation of the cooling water temperature from the ideal level in the overall efficiency deviation of the unit, for each piece of equipment, if the contribution ratio of the cooling side is less than the preset cooling side diagnostic threshold, it is determined to be an internal fault of the equipment, indicating that the main reason for the decline in energy efficiency is the aging of the equipment (such as scaling of heat exchange tubes, insufficient refrigerant, and decreased compressor efficiency), rather than a cooling side problem.

[0072] In one specific implementation of this invention, the preset cooling-side diagnostic threshold is set to 30%, which can be adjusted according to the specific implementation scenario. The larger the preset cooling-side diagnostic threshold, the more energy efficiency problems will be attributed to the cooling side.

[0073] When the contribution of the cooling side is greater than or equal to the preset cooling side diagnostic threshold, it indicates that the main cause of the energy efficiency problem lies in the cooling side. At this point, it is necessary to further determine the specific source of the fault within the cooling side.

[0074] The cooling tower contribution ratio reflects the weight of insufficient cooling tower heat dissipation capacity (i.e., deviation from the ideal level) in the overall cooling-side problem. If the cooling-side contribution ratio is greater than or equal to the preset cooling-side diagnostic threshold, and the cooling tower contribution ratio is greater than the preset cooling tower diagnostic threshold, then it is determined to be a cooling tower failure, meaning that in the cooling-side problem, the cooling tower failure is the main factor leading to the reduction of refrigeration system energy efficiency.

[0075] In one specific implementation of this invention, the preset cooling tower diagnostic threshold is set to 70%, which can be adjusted according to the specific implementation scenario. The smaller the preset cooling tower diagnostic threshold, the easier it is to determine that the cooling tower problem is dominant, which may lead to the water pump problem being masked, and may easily cause the misjudgment of prioritizing the cleaning of the cooling tower while ignoring the adjustment of the water pump frequency.

[0076] Similarly, the contribution ratio of the cooling water pump reflects the weight of insufficient cooling water pump power (i.e., temperature rise deviating from the ideal level) in the overall cooling-side problem. If the cooling-side contribution ratio is greater than or equal to the preset cooling-side diagnostic threshold, and the cooling water pump contribution ratio is greater than the preset cooling water pump diagnostic threshold, then it is determined to be a cooling water pump failure, meaning that in the cooling-side problem, the cooling water pump failure is the main factor leading to the reduction of refrigeration system energy efficiency.

[0077] In one specific implementation of this invention, the preset cooling water pump diagnostic threshold is set to 70%, which can be adjusted according to the specific implementation scenario. The smaller the preset cooling water pump diagnostic threshold, the easier it is to determine that the cooling water pump problem is the dominant one, which may lead to the cooling tower problem being masked, and may easily cause misjudgment of prioritizing the adjustment of the water pump frequency while ignoring the cooling tower cleaning.

[0078] If the contribution ratio of the cooling side is greater than or equal to a preset cooling side diagnostic threshold, and the contribution ratio of the cooling tower is less than or equal to a preset cooling tower diagnostic threshold, and the contribution ratio of the cooling water pump is less than or equal to a preset cooling water pump diagnostic threshold, then it is determined to be a cooling side coupling mismatch. This means that there is indeed a performance problem on the cooling side (the contribution ratio of the cooling side exceeds the threshold), but the contribution ratios of the cooling tower and the water pump have not reached their respective judgment thresholds. Possible reasons include: both the cooling tower and the water pump have minor problems, and their combined effect leads to a decrease in cooling side performance, but neither is significant; or there are other coupling factors (such as scaling in the cooling water pipes, improper valve opening, etc.) that simultaneously affect the performance of the cooling tower and the water pump.

[0079] It should be noted that if the contribution ratio of the cooling side is greater than or equal to the preset cooling side diagnostic threshold, and the contribution ratio of the cooling tower is less than or equal to the preset cooling tower diagnostic threshold, it indicates that there is indeed a performance problem on the cooling side, but the cooling tower is not the problem. Further analysis of the cooling water pump is required. If the contribution ratio of the cooling water pump is greater than the preset cooling water pump diagnostic threshold, it is determined to be a cooling water pump failure; if the contribution ratio of the cooling water pump is less than or equal to the preset cooling water pump diagnostic threshold, it is determined to be a cooling side coupling mismatch.

[0080] Based on the same inventive concept, this invention also proposes a cloud platform-based green and low-carbon energy multi-source data standardization diagnostic system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a cloud platform-based green and low-carbon energy multi-source data standardization diagnostic method.

[0081] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0082] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A standardized diagnostic method for green and low-carbon energy consumption based on multi-source data from a cloud platform, characterized in that, The method includes: Obtain the outdoor wet-bulb temperature, cooling water return temperature, and cooling water outlet temperature of the refrigeration system under each historical operating condition, as well as the load rate and operating energy efficiency ratio of each device in the refrigeration system; determine the energy efficiency benchmark response surface of each device based on the data fitting between the load rate, cooling water return temperature and operating energy efficiency ratio of each device under each historical operating condition. Based on the discrepancy between the operating energy efficiency ratio of each device under the current operating conditions and the discreteness of the energy efficiency reference response surface, the overall unit efficiency deviation of each device is determined; based on the energy efficiency reference response surface, and considering the difference in the influence of different cooling water return temperatures under the current and historical operating conditions, the cooling-side transmission deviation of each device is determined; based on the relative relationship between the cooling-side transmission deviation and the overall unit efficiency deviation, the cooling-side contribution ratio of each device is determined. The contribution percentage of the cooling tower is determined based on the deviation between the cooling water outlet temperature and the outdoor wet-bulb temperature under the current operating conditions; the contribution percentage of the cooling water pump is determined based on the deviation between the cooling water return temperature and the cooling water outlet temperature under the current operating conditions. Based on the contribution ratios of the cooling side, cooling tower, and cooling water pump, fault diagnosis of the refrigeration system is performed.

2. The method for standardized diagnosis of green and low-carbon energy consumption based on cloud platform multi-source data according to claim 1, characterized in that, The method for obtaining the energy efficiency reference response surface includes: Based on the load rate, cooling water return temperature and operating energy efficiency ratio of each device under various historical operating conditions, surface fitting is performed in three-dimensional space to determine the energy efficiency benchmark response surface of each device.

3. The method for standardized diagnosis of green and low-carbon energy consumption based on cloud platform multi-source data according to claim 1, characterized in that, The method for obtaining the overall efficiency deviation of the unit includes: Substitute the load rate and cooling water return temperature of each device under the current operating conditions into the energy efficiency benchmark response surface to determine the corresponding theoretical operating energy efficiency ratio; Based on the difference between the operating energy efficiency ratio of each device under current operating conditions and the theoretical operating energy efficiency ratio, the corresponding total efficiency deviation of the unit is determined.

4. The method for standardized diagnosis of green and low-carbon energy consumption based on cloud platform multi-source data according to claim 3, characterized in that, The method for obtaining the transmission deviation on the cooling side includes: For each device, based on the distribution of the deviation between the load rate under each historical operating condition and the load rate under the current operating condition, characteristic operating conditions are determined from the historical operating conditions; the characteristic operating condition corresponding to the maximum operating energy efficiency ratio among the characteristic operating conditions is taken as the optimal characteristic operating condition. Substitute the load rate and cooling water return temperature under the optimal characteristic operating conditions into the energy efficiency benchmark response surface to determine the corresponding optimal operating energy efficiency ratio. Based on the difference between the theoretical operating energy efficiency ratio and the optimal operating energy efficiency ratio, the cooling-side transmission deviation of each device is determined.

5. The method for standardized diagnosis of green and low-carbon energy consumption based on cloud platform multi-source data according to claim 1, characterized in that, The method for obtaining the contribution ratio of the cooling tower includes: Based on the deviation of the cooling water return temperature of the refrigeration system under the current operating conditions from the cooling water return temperature under historical operating conditions, determine the total performance deviation of the cooling side under the current operating conditions. Based on the difference between the cooling water outlet temperature and the outdoor wet-bulb temperature under the current operating conditions, the approximation degree under the current operating conditions is determined; based on the difference between the approximation degree and the preset ideal approximation degree, the corresponding cooling tower contribution is determined. The contribution ratio of the cooling tower under the current operating condition is determined based on the total performance deviation of the cooling side and the contribution of the cooling tower. The contribution ratio of the cooling tower is positively correlated with the contribution of the cooling tower and negatively correlated with the total performance deviation of the cooling side.

6. The method for standardized diagnosis of green and low-carbon energy consumption based on cloud platform multi-source data according to claim 5, characterized in that, The method for obtaining the total performance deviation on the cooling side includes: Under each historical operating condition, the system load rate of the refrigeration system is determined based on the weighted average of the load rates of all devices; the system operating energy efficiency ratio of the refrigeration system is determined based on the weighted average of the operating energy efficiency ratios of all devices. Based on the deviation distribution of system load rate between each historical operating condition and the current operating condition, similar operating conditions are determined from the historical operating conditions; the similar operating condition corresponding to the maximum system operating energy efficiency ratio among the similar operating conditions is taken as the optimal similar operating condition. The total performance deviation on the cooling side is determined based on the difference between the cooling water return temperature under the current operating conditions and the cooling water return temperature under the optimal similar operating conditions.

7. The method for standardized diagnosis of green and low-carbon energy consumption based on cloud platform multi-source data according to claim 5, characterized in that, The process for obtaining the contribution percentage of the cooling water pump includes: The temperature rise under the current operating conditions is determined based on the difference between the cooling water return temperature and the cooling water outlet temperature; the corresponding cooling water pump contribution is determined based on the difference between the temperature rise and the preset ideal temperature rise. The contribution ratio of the cooling water pump under the current operating condition is determined based on the total performance deviation of the cooling side and the contribution of the cooling water pump. The contribution ratio of the cooling water pump is positively correlated with the contribution of the cooling water pump and negatively correlated with the total performance deviation of the cooling side.

8. The method for standardized diagnosis of green and low-carbon energy consumption based on cloud platform multi-source data according to claim 1, characterized in that, The process of diagnosing refrigeration system faults based on the contribution ratios of the cooling side, cooling tower, and cooling water pump includes: For each device, if the contribution ratio of the cooling side is less than the preset cooling side diagnostic threshold, it is determined to be an internal device fault. If the contribution ratio of the cooling side is greater than or equal to the preset cooling side diagnostic threshold, and the contribution ratio of the cooling tower is greater than the preset cooling tower diagnostic threshold, then the cooling tower is determined to be faulty. If the contribution ratio of the cooling side is greater than or equal to the preset cooling side diagnostic threshold, and the contribution ratio of the cooling water pump is greater than the preset cooling water pump diagnostic threshold, then the cooling water pump is determined to be faulty. If the contribution ratio of the cooling side is greater than or equal to the preset cooling side diagnostic threshold, and the contribution ratio of the cooling tower is less than or equal to the preset cooling tower diagnostic threshold, and the contribution ratio of the cooling water pump is less than or equal to the preset cooling water pump diagnostic threshold, then it is determined to be a cooling side coupling mismatch.

9. A standardized diagnostic method for green and low-carbon energy consumption based on a cloud platform, as described in claim 2, is characterized in that... A local weighted regression algorithm is used for surface fitting.

10. A cloud-based green and low-carbon energy consumption multi-source data standardization diagnostic system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the cloud platform-based green and low-carbon energy consumption multi-source data standardization diagnostic method as described in any one of claims 1 to 9.

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