Enterprise energy optimization method and system based on cloud computing and Internet

Abstract

The invention discloses an enterprise energy optimization method and system based on cloud computing and the Internet, and relates to the technical field of energy, first, server operation data in a cooling state is monitored in real time, historical power consumption and temperature data stored in a database are combined, historical temperature values Tws corresponding to power consumption at historical monitoring points are obtained, and then the historical temperature values Tws are calculated; combining a regression model, calculating an abnormal state coefficient Xyc according to server operation data and historical data, preliminarily judging whether the cooling system is abnormal or not, further monitoring operation characteristics of the cooling system, calculating a mechanical fault evaluation coefficient Xgz, judging whether the cooling system is caused by a mechanical fault or not, and finally, judging whether the cooling system is abnormal or not. And detecting the pressure change condition of the cooling liquid backflow pipeline and the cooling liquid supply pipeline, judging whether leakage or blockage exists or not according to the loop pressure difference value Fyc, and if leakage or blockage is not found, indicating that the energy efficiency of the cooling equipment of the cooling system declines, and performing optimization.

Description

Technical Field

[0001] The present invention relates to the field of energy technology, and in particular to an enterprise energy optimization method and system based on cloud computing and the Internet. Background Art

[0002] The energy sector has always been a fundamental pillar of global economic development and plays a vital role in all walks of life. With the increasing global attention to sustainable development, energy management has gradually become a key factor in promoting social and economic innovation and development. In this process, enterprise energy management, as an important means to optimize resource allocation and reduce operating costs, has received increasing attention. Especially in data-intensive industries, enterprises' energy consumption is often dominated by electricity consumption. Among them, the cooling system is the main energy consumption in large data centers and plays a vital role in ensuring the stable operation of server equipment. However, traditional cooling systems often have problems of energy efficiency decline and untimely maintenance, resulting in energy waste and excessive equipment consumption. Therefore, energy efficiency optimization and maintenance prediction of cooling systems have become an important research direction in the field of enterprise energy management. By introducing cloud computing and Internet technologies, the operating status of the cooling system can be monitored, analyzed and optimized in real time, improving energy efficiency and reducing unnecessary energy waste.

[0003] Although many companies are aware of the importance of optimizing cooling system energy efficiency, traditional cooling systems often face the following problems: First, energy efficiency degradation in cooling systems is often difficult to detect in a timely manner, relying on manual inspections and regular checks, which is not only inefficient but also prone to missing potential faults. Second, there is usually no immediate screening and treatment mechanism for coolant leaks and pipe blockages, causing the system to remain in a suboptimal state for a long time, which in turn affects the energy efficiency of the entire data center and the operating performance of the servers. In addition, many companies lack systematic predictive models for cooling system maintenance, making it difficult to accurately evaluate the energy efficiency of cooling equipment based on historical data, resulting in unnecessary high-cost maintenance and replacement. Therefore, intelligent optimization, accurate prediction, and early warning of faults for cooling systems have become an urgent need to improve enterprise energy optimization management and reduce maintenance costs. Through optimization methods based on cloud computing and the Internet, combined with real-time sensor monitoring and data analysis technology, the problems of insufficient energy efficiency and untimely maintenance of traditional cooling systems can be effectively solved, thereby strengthening enterprise energy optimization management and improving overall energy saving benefits and stability. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides an enterprise energy optimization method and system based on cloud computing and the Internet, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for optimizing enterprise energy based on cloud computing and the Internet, comprising the following steps; S1. Based on multiple sets of deployed sensors, the operating status of servers in cooling state is monitored in real time. A server operation data set is constructed. Based on several data strips stored in the database, the historical temperature value Tws corresponding to the power consumption at each historical monitoring point is obtained after querying and extracting. S2. Based on the acquired server operation data set and the historical temperature value Tws corresponding to the power consumption at each historical monitoring point, and in combination with the regression model, the state abnormality coefficient Xyc is obtained and compared with the preset threshold value to preliminarily determine whether there is an abnormality in the current cooling system operation state. If so, a first screening instruction is issued. S3. After receiving the first troubleshooting instruction, the operating characteristics of the current cooling system are monitored. After dimensionless processing, the mechanical fault assessment coefficient Xgz is obtained. The coefficient is compared with a preset threshold to determine whether the abnormal operating status of the current cooling system is caused by a mechanical fault. If no mechanical fault exists, coolant leakage and blockage are considered, and a second screening instruction is issued. S4. After receiving the second troubleshooting instruction, monitor the pressure changes in the coolant return pipe and the coolant supply pipe of the current cooling system, and determine whether there is a coolant leakage and blockage problem in the pipe loop of the current cooling system based on the value of the obtained loop pressure difference value Fyc. If not, it indicates that the energy efficiency of the cooling equipment of the current cooling system has declined.

[0006] Preferably, the specific steps of S1 include: The specific steps of S1 include: S11. Based on temperature sensors deployed on servers in large data centers, and in combination with power consumption monitoring devices connected to the server hardware management interface, regularly collected server power consumption and server temperature data are transmitted to a database via a network transmission method. The database classifies the data according to timestamps and records them according to fields related to collection time, power consumption, and temperature values, and records them as data strips. Each data strip includes power consumption, temperature value, and collection time information, and is aggregated and stored on a daily basis. By using indexing technology to query and extract data strips, the historical temperature value Tws corresponding to the power consumption at each historical monitoring point is obtained. S12. Based on the deployed multiple groups of sensors, the operating status of the server in the cooling state is monitored in real time to construct a server operating data set; the server operating data set includes the actual temperature value Twd corresponding to the power consumption at each monitoring point within the real-time monitoring period.

[0007] Preferably, the specific steps of S2 include: S21. Calculate the temperature residual value Tcc corresponding to the power consumption at each monitoring point during the real-time monitoring period based on the acquired server operation data set and the historical temperature value Tws corresponding to the power consumption at each historical monitoring point. The temperature residual value Tcc is obtained specifically according to the following formula: ; Where, It is represented by the actual temperature value corresponding to the power consumption at the i-th monitoring point during the real-time monitoring period, and Tws is represented by The historical temperature values ​​corresponding to the same power conditions, It is represented as the temperature residual value corresponding to the power consumption at the i-th monitoring point within the real-time monitoring period.

[0008] Preferably, the specific step S2 further includes: S22. Based on the obtained temperature residual value Tcc corresponding to the power consumption at each monitoring point and in combination with the standardized residual calculation method of the regression model, obtain the abnormal state coefficient Xyc of the current cooling system. The abnormal state coefficient Xyc of the current cooling system is obtained by the following formula; ; Where, It is represented by the temperature residual value corresponding to the power consumption at the i-th monitoring point during the real-time monitoring period. It is expressed as the standard deviation of the temperature residual value, i=1, 2, 3, ..., n, where n is the number of monitoring points; S23. Preset an abnormality threshold Y, compare the acquired state abnormality coefficient Xyc with the preset abnormality threshold Y, and preliminarily determine whether there is an abnormality in the cooling system operation state. The specific analysis process is as follows: If the abnormal coefficient Xyc of the current cooling system is greater than or equal to the abnormal threshold Y, it is preliminarily determined that the current cooling system operation state is abnormal, and the first screening instruction will be issued. If the abnormality coefficient Xyc of the current cooling system state is less than the abnormality threshold value Y, it is preliminarily determined that there is no abnormality in the current cooling system operation state, and no additional first screening instruction is issued at this time.

[0009] Preferably, the specific steps of S3 include: S31. After receiving the first screening instruction, monitor the operating characteristics of the current cooling system according to the deployed groups of sensors, and obtain the operating characteristic data information of the current cooling system in combination with Fourier transform. The operating characteristic data information includes the vibration frequency Vzd, amplitude Fzf, and noise value Zzs generated during the operation of the current cooling system; S32. According to the acquired operation characteristic data information, after dimensionless processing, a mechanical fault assessment coefficient Xgz is obtained by fitting. The mechanical fault assessment coefficient Xgz is obtained by the following formula; ; Where Vzd represents the vibration frequency, Fzf represents the amplitude, and Zzs represents the noise value. 、 and They represent the weight values ​​of vibration frequency Vzd, amplitude Fzf and noise value Zzs respectively, and A represents the correction constant.

[0010] Preferably, the specific step S3 further includes: S33. Compare the mechanical fault assessment coefficient Xgz with a preset threshold value. If the mechanical fault assessment coefficient Xgz exceeds the preset threshold value, it indicates that the abnormality in the current cooling system operation state is caused by a mechanical fault. At this time, the problems of coolant leakage and blockage will be ruled out, and maintenance personnel will be immediately arranged to conduct a comprehensive inspection and repair of the faulty equipment in the cooling system. If the mechanical fault assessment coefficient Xgz does not exceed the preset threshold value, it indicates that the abnormality in the current cooling system operation state is not caused by a mechanical fault. At this time, the problems of coolant leakage and blockage will be considered, and a second screening instruction will be issued.

[0011] Preferably, the specific steps of S4 include: S41. After receiving the second screening instruction, real-time monitoring is performed based on the pressure changes in the coolant return pipe and the coolant supply pipe of the current cooling system to obtain a loop pressure data set, wherein the loop pressure data set includes the return pipe pressure value Fhg and the supply pipe pressure value Fgg in each monitoring period; and based on the real-time related pressure changes in the return pipe and the supply pipe in the loop pressure data set, the loop pressure difference value Fyc of the current cooling system is obtained.

[0012] Preferably, the specific step S4 further includes: S42, according to the return pipe pressure value Fhg and the supply pipe pressure value Fgg of each monitoring period in the loop pressure data set, and combined with the statistical averaging algorithm, respectively obtain the return pipe pressure mean value and the standard deviation of the return pipe pressure , and the mean supply pipeline pressure and supply pipeline pressure standard deviation ; S421, based on the average pressure of the return pipe and the standard deviation of the return pipe pressure , get the normal fluctuation range of No. 1 pressure as , k is a constant, and the specific value is set by the administrator; S422, based on the average pressure of the supply pipeline and supply pipeline pressure standard deviation , get the normal fluctuation range of No. 2 pressure as , k is a constant, and the specific value is set by the administrator; S43, according to the normal fluctuation range of No. 1 pressure Normal fluctuation range of No. 2 pressure , obtain the normal pressure fluctuation average range , the specific acquisition process is as follows; .

[0013] Preferably, the specific step S4 further includes: S44, by comparing the circuit pressure difference value Fyc of the current cooling system with the average range of normal pressure fluctuation A comparative analysis is conducted to determine whether there are any coolant leaks or blockages in the cooling system's piping circuits. The specific analysis is as follows: If the current cooling system circuit pressure difference Fyc is not within the normal pressure fluctuation average range When the icon is displayed, it indicates that there is a coolant leak or blockage in the cooling system's pipeline loop. At this time, maintenance personnel will be immediately dispatched to investigate and identify the leak and blockage points in the coolant loop pipeline. The leak will be repaired and refilled with coolant, and the blockage point in the coolant loop pipeline will be cleared. If the current cooling system circuit pressure difference Fyc is within the normal pressure fluctuation average range , it means that there is no leakage or blockage problem in the coolant pipeline circuit of the current cooling system, but the energy efficiency of the cooling equipment of the current cooling system has declined. At this time, the old cooling equipment will be replaced, or the current cooling system will be upgraded to a cooling system with liquid cooling.

[0014] An enterprise energy optimization system based on cloud computing and the Internet, including a data acquisition module, a power consumption analysis module, a fault screening module, and an energy efficiency degradation determination module; The data acquisition module monitors the operating status of the server in the cooling state in real time based on multiple sets of deployed sensors, builds a server operation data set, and obtains the historical temperature value Tws corresponding to the power consumption at each historical monitoring point after querying and extracting several data strips stored in the database; The power consumption analysis module is used to obtain the state abnormality coefficient Xyc based on the acquired server operation data set and the historical temperature value Tws corresponding to the power consumption at each historical monitoring point, combined with the regression model, and compare it with the preset threshold to preliminarily determine whether there is an abnormality in the current cooling system operation state. If so, a first screening instruction will be issued externally; The fault screening module is configured to, upon receiving the first troubleshooting instruction, monitor the operating characteristics of the current cooling system, obtain a mechanical fault assessment coefficient Xgz after dimensionless processing, compare it with a preset threshold, and determine whether the abnormality in the current cooling system operating state is caused by a mechanical fault. If no mechanical fault exists, the module considers coolant leakage and blockage as problems and issues a second screening instruction. The energy efficiency degradation judgment module is used to monitor the pressure changes in the coolant return pipe and the coolant supply pipe of the current cooling system after receiving the second troubleshooting instruction, and judge whether there is leakage and blockage of the coolant in the pipe loop of the current cooling system based on the numerical value of the obtained loop pressure difference value Fyc. If not, it indicates that the energy efficiency of the cooling equipment of the current cooling system has declined.

[0015] The present invention provides a method and system for enterprise energy optimization based on cloud computing and the Internet, which has the following beneficial effects: (1) Through step-by-step process analysis, comprehensive monitoring, accurate diagnosis and efficient optimization of the cooling system are achieved, which improves the efficiency of enterprise energy optimization management. First, by deploying multiple sets of sensors, the operating status of the server is monitored in real time, and an accurate server operation data set is generated, which provides reliable data support for subsequent power consumption analysis. Then, combined with the regression model, the relationship between power consumption and temperature is analyzed, the state abnormality coefficient Xyc is calculated, and it is compared with the preset abnormality threshold Y to timely detect the operation abnormality of the cooling system. This process realizes the early detection of cooling system abnormalities and triggers the first screening instruction, ensuring the efficient response and fault warning of the system. After receiving the first screening instruction, the operating characteristics of the cooling system are monitored in detail, and the mechanical fault evaluation coefficient Xgz is calculated after dimensionless processing. Combined with the standard for judgment, it can accurately distinguish whether the mechanical failure is caused by coolant leakage or blockage. The cooling system anomalies are detected, which reduces the misjudgments and missed judgments in traditional manual troubleshooting and improves the accuracy of fault diagnosis. Then, through real-time monitoring of loop pressure changes, the energy efficiency decline of the cooling system is further evaluated. If no leakage or blockage is found, if the equipment energy efficiency declines, a warning will be issued in time and equipment replacement or upgrade will be recommended. Through this series of intelligent and precise monitoring and screening measures, the stability of the cooling system is effectively improved, downtime and energy waste are reduced, and the service life of the equipment is extended. In addition, energy consumption is optimized, unnecessary equipment maintenance and replacement are avoided, and the operating costs of the enterprise are reduced. In summary, the optimization method based on cloud computing and the Internet, combined with real-time monitoring and data analysis technology of sensors, can effectively solve the problems of insufficient energy efficiency and untimely maintenance of traditional cooling systems, thereby strengthening the enterprise's energy optimization management and improving overall energy saving benefits and stability.

[0016] (2) Through intelligent data collection and analysis, significant improvements have been made in the fault detection and energy efficiency optimization of the cooling system; by real-time monitoring of the server's operating status, combined with temperature and power consumption data, it can provide accurate collection data, providing a solid foundation for power consumption analysis, and through the regression model to analyze historical temperature data, the state abnormality coefficient Xyc is calculated in time. When the cooling system is abnormal, the first screening instruction will be automatically triggered, effectively avoiding the lag problem caused by manual detection. At the same time, the standardized residual calculation method based on the regression model helps to improve the accuracy of fault identification and reduce the risks caused by the failure to discover potential problems in the cooling system in a timely manner, thereby greatly reducing the probability of equipment damage. On the basis of data collection and analysis, it can also provide real-time feedback on the operating status of the cooling system, and through accurate fault diagnosis and screening, potential problem points can be screened in advance, thereby extending the service life of the equipment and improving the operating stability of the cooling system.

[0017] (3) The joint work of fault screening and energy efficiency degradation judgment provides important support for the optimization of the cooling system. Through the multi-level screening mechanism, different fault sources in the cooling system can be gradually checked. First, after receiving the first screening instruction, it can accurately determine whether the cooling system is abnormal due to mechanical failure and exclude other reasons, ensuring that maintenance personnel can perform maintenance work faster and more accurately. If it is not caused by mechanical failure, further check whether there is leakage or pipe blockage of the coolant, and issue the second screening instruction in time. This accurate fault location process improves the efficiency of troubleshooting, avoids blind maintenance, and reduces unnecessary maintenance costs for the enterprise. At the same time, by real-time monitoring of the coolant circuit pressure, it can accurately determine the flow of coolant in the cooling system. If there is no leakage or blockage problem, it can identify the problem of energy efficiency degradation of the cooling equipment, timely discover the decline in the operating efficiency of the equipment and take replacement and upgrade measures. Through these accurate diagnoses and early warnings, not only energy consumption is reduced, but also resource waste caused by equipment energy efficiency degradation is effectively avoided, saving costs for the enterprise's energy expenditure, while improving the overall cooling efficiency and further strengthening the enterprise's energy optimization management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a method for optimizing enterprise energy based on cloud computing and the Internet according to the present invention; Figure 2 This is a block diagram of an enterprise energy optimization system based on cloud computing and the Internet according to the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] Example 1 See also Figure 1 ,The present invention provides an enterprise energy optimization method based on cloud computing and the Internet, comprising the following steps; S1. Based on multiple sets of deployed sensors, the operating status of servers in cooling state is monitored in real time. A server operation data set is constructed. Based on several data strips stored in the database, the historical temperature value Tws corresponding to the power consumption at each historical monitoring point is obtained after querying and extracting. S2. Based on the acquired server operation data set and the historical temperature value Tws corresponding to the power consumption at each historical monitoring point, and in combination with the regression model, the state abnormality coefficient Xyc is obtained and compared with the preset threshold value to preliminarily determine whether there is an abnormality in the current cooling system operation state. If so, a first screening instruction is issued. S3. After receiving the first troubleshooting instruction, the operating characteristics of the current cooling system are monitored. After dimensionless processing, the mechanical fault assessment coefficient Xgz is obtained. The coefficient is compared with a preset threshold to determine whether the abnormal operating status of the current cooling system is caused by a mechanical fault. If no mechanical fault exists, coolant leakage and blockage are considered, and a second screening instruction is issued. S4. After receiving the second troubleshooting instruction, monitor the pressure changes in the coolant return pipe and the coolant supply pipe of the current cooling system, and determine whether there is a coolant leakage and blockage problem in the pipe loop of the current cooling system based on the value of the obtained loop pressure difference value Fyc. If not, it indicates that the energy efficiency of the cooling equipment of the current cooling system has declined.

[0021] In this embodiment, by combining multiple sets of sensors for real-time monitoring and advanced data analysis technology, the operating efficiency and intelligent management level of the cooling system are improved. First, it is possible to monitor the server operating status in real time and obtain the historical temperature value Tws by constructing a data set. The state abnormality coefficient Xyc is calculated in combination with the regression model to promptly detect potential abnormalities in the operation of the cooling system. This method breaks through the limitation of traditional cooling systems relying on manual inspections and can immediately issue screening instructions when abnormalities occur in the cooling system, reducing the risk of missed inspections. Furthermore, after an abnormality is discovered, detailed troubleshooting can be carried out, including mechanical fault evaluation coefficient Xgz, coolant leakage or pipeline blockage problems, thereby avoiding the cooling equipment being difficult to detect. Energy waste caused by timely detection can be accurately determined by real-time monitoring of the cooling system pipeline loop pressure to determine whether there is a leak or blockage, thereby effectively ensuring the stable operation of the cooling system and avoiding server overheating and performance degradation due to cooling system failure. More importantly, through continuous monitoring and prediction of the energy efficiency of cooling equipment, maintenance decisions can be optimized, unnecessary repair and equipment replacement costs can be reduced, thereby reducing overall operating costs. Ultimately, this method not only improves the accuracy and efficiency of the enterprise's energy optimization management, reduces energy waste, but also optimizes the enterprise's long-term energy use structure, further promoting the realization of the enterprise's energy conservation and consumption reduction goals, and has significant economic benefits, environmental benefits and social value.

[0022] Example 2 Please refer to Figure 1 , specifically: S1 specific steps include: S11. Based on temperature sensors deployed on servers in large data centers, and in combination with power consumption monitoring devices connected to the server hardware management interface, regularly collected server power consumption and server temperature data are transmitted to a database via a network transmission method. The database classifies the data according to timestamps and records them according to fields related to collection time, power consumption, and temperature values, and records them as data strips. Each data strip includes power consumption, temperature value, and collection time information, and is aggregated and stored on a daily basis. By using indexing technology to query and extract data strips, the historical temperature value Tws corresponding to the power consumption at each historical monitoring point is obtained. S12. Based on the deployed multiple groups of sensors, the operating status of the server in the cooling state is monitored in real time to construct a server operating data set; the server operating data set includes the actual temperature value Twd corresponding to the power consumption at each monitoring point within the real-time monitoring period.

[0023] In this embodiment, the implementation of step S1 can improve the operating efficiency and fault prevention capabilities of the data center server cooling system; through the joint deployment of temperature sensors and power consumption monitoring equipment, power consumption and temperature data are regularly collected and transmitted in real time, ensuring accurate monitoring of the server operating status. Data strips are classified and stored by timestamps, and relevant historical data are quickly queried and extracted through indexing technology, enabling the monitoring system to accurately locate the temperature change trend of each historical monitoring point in massive data. This data structured storage method enables rapid acquisition of historical temperatures corresponding to previous power consumption, providing a solid data foundation for long-term performance tracking and analysis of the server. Secondly, through the deployment of multiple groups of sensors, servers in a cooling state can be monitored in real time, and real-time power consumption and temperature data can be obtained, thereby forming a server operating data set, which provides strong support for real-time evaluation of the server operating status and timely response to possible anomalies. Overall, this efficient data collection and real-time monitoring system not only improves the response speed of the server cooling system, but also provides reliable data support for server operating status analysis in the data center.

[0024] Example 3 Please refer to Figure 1 , specifically: S2 specific steps include: S21. Calculate the temperature residual value Tcc corresponding to the power consumption at each monitoring point during the real-time monitoring period based on the acquired server operation data set and the historical temperature value Tws corresponding to the power consumption at each historical monitoring point. The temperature residual value Tcc is obtained specifically according to the following formula: ; Where, It is represented by the actual temperature value corresponding to the power consumption at the i-th monitoring point during the real-time monitoring period, and Tws is represented by The historical temperature values ​​corresponding to the same power conditions, It is represented as the temperature residual value corresponding to the power consumption at the i-th monitoring point within the real-time monitoring period.

[0025] Specifically, the steps of S2 also include: S22. Based on the obtained temperature residual value Tcc corresponding to the power consumption at each monitoring point and in combination with the standardized residual calculation method of the regression model, obtain the abnormal state coefficient Xyc of the current cooling system. The abnormal state coefficient Xyc of the current cooling system is obtained by the following formula; ; Where, It is represented by the temperature residual value corresponding to the power consumption at the i-th monitoring point during the real-time monitoring period. It is expressed as the standard deviation of the temperature residual value, i=1, 2, 3, ..., n, where n is the number of monitoring points; S23. Preset an abnormality threshold Y, compare the acquired state abnormality coefficient Xyc with the preset abnormality threshold Y, and preliminarily determine whether there is an abnormality in the cooling system operation state. The specific analysis process is as follows: If the abnormal coefficient Xyc of the current cooling system is greater than or equal to the abnormal threshold Y, it is preliminarily determined that the current cooling system operation state is abnormal, and the first screening instruction will be issued. If the abnormality coefficient Xyc of the current cooling system state is less than the abnormality threshold value Y, it is preliminarily determined that there is no abnormality in the current cooling system operation state, and no additional first screening instruction is issued at this time.

[0026] It should be noted that the standardized residual calculation method measures the difference between the predicted value and the actual value and standardizes it to have a unified measurement standard. In the cooling system monitoring, the standardized residual is obtained by calculating the deviation between the actual temperature corresponding to the power consumption at each monitoring point in the real-time monitoring period and the historical temperature corresponding to the same power condition in the historical data. The standardized error term is obtained. Through this process, the deviation of each data point can be compared on the same scale, which is convenient for identifying abnormal conditions.

[0027] In this embodiment, based on the real-time operation data and historical data of the server, the temperature residual value Tcc corresponding to the power consumption at each monitoring point within the real-time monitoring period is calculated. This calculation can not only help accurately judge the deviation between the actual operation state of the cooling system and the expectation, but also provide data support for subsequent abnormality detection. Next, combined with the regression model and the standardized residual calculation method, a state abnormality coefficient Xyc is further generated to reflect whether the operation state of the cooling system is abnormal and compared with the preset abnormality threshold Y. If the state abnormality coefficient Xyc value is greater than or equal to the abnormality threshold Y, the first screening instruction will be issued in time to start further troubleshooting to avoid server overheating and problems caused by abnormal cooling system. Through this series of steps, It can provide fast and accurate early warning and response when mechanical failures, coolant leakage, pipe blockage and energy efficiency decline occur in cooling equipment, thereby improving the operation and maintenance efficiency of the cooling system. Compared with traditional manual inspection methods, this method can effectively reduce human omissions and delays, improve the accuracy and real-time performance of detection, thereby reducing the risk of energy waste and equipment damage. In addition, through the accumulation and analysis of historical data, it can also continuously optimize the maintenance cycle and strategy of cooling equipment, avoid unnecessary excessive maintenance or equipment replacement, and further reduce the company's operating costs. In short, this method not only improves the energy efficiency and stability of the cooling system, but also provides enterprises with an intelligent and automated energy optimization management solution, promoting the realization of the company's energy conservation, emission reduction and sustainable development goals.

[0028] Example 4 Please refer to Figure 1 , specifically: S3 specific steps include: S31. After receiving the first screening instruction, monitor the operating characteristics of the current cooling system according to the deployed vibration sensors and noise sensors, and obtain operating characteristic data information of the current cooling system in combination with Fourier transform. The operating characteristic data information includes the vibration frequency Vzd, amplitude Fzf, and noise value Zzs generated during the operation of the current cooling system; S32. According to the acquired operation characteristic data information, after dimensionless processing, a mechanical fault assessment coefficient Xgz is obtained by fitting. The mechanical fault assessment coefficient Xgz is obtained by the following formula; ; Where Vzd represents the vibration frequency, Fzf represents the amplitude, and Zzs represents the noise value. 、 and They represent the weight values ​​of vibration frequency Vzd, amplitude Fzf and noise value Zzs respectively, and A represents the correction constant.

[0029] Specifically, the S3 steps also include: S33. Compare the mechanical fault assessment coefficient Xgz with a preset threshold value. If the mechanical fault assessment coefficient Xgz exceeds the preset threshold value, it indicates that the abnormality in the current cooling system operation state is caused by a mechanical fault. At this time, the problems of coolant leakage and blockage will be ruled out, and maintenance personnel will be immediately arranged to conduct a comprehensive inspection and repair of the faulty equipment in the cooling system. If the mechanical fault assessment coefficient Xgz does not exceed the preset threshold value, it indicates that the abnormality in the current cooling system operation state is not caused by a mechanical fault. At this time, the problems of coolant leakage and blockage will be considered, and a second screening instruction will be issued.

[0030] In this embodiment, after receiving the first screening instruction, the operating characteristics of the cooling system are monitored by using multiple sets of deployed sensors, and the vibration frequency Vzd, amplitude Fzf and noise value Zzs data generated during the operation of the current cooling system are analyzed in combination with Fourier transform technology. These data provide a scientific basis for subsequent mechanical fault assessment and can effectively identify possible mechanical fault problems in the cooling system. After dimensionless processing, the fitted mechanical fault assessment coefficient Xgz provides a quantitative indicator for judging whether a mechanical fault occurs in the cooling system. When the mechanical fault assessment coefficient Xgz exceeds the preset threshold, it can accurately determine that the mechanical fault is the cause of the cooling abnormality, and immediately start fault investigation and Equipment maintenance procedures are established to avoid problems from escalating and equipment damage. Compared with traditional manual detection methods, automated monitoring based on vibration and noise characteristics not only improves the accuracy of fault detection, but also can detect potential problems at an early stage, preventing the cooling system from suffering more serious damage and shutdown due to mechanical failure. When the mechanical failure assessment coefficient Xgz does not exceed the threshold, the possibility of mechanical failure is automatically ruled out, and attention is paid to coolant leakage and pipe blockage problems, ensuring that the troubleshooting work is fully covered and no omissions are missed. This intelligent fault diagnosis and troubleshooting process not only improves the stability and reliability of the cooling system, but also reduces maintenance costs and downtime risks, further improving the company's energy management efficiency and production continuity.

[0031] Example 5 Please refer to Figure 1 , specifically: S4 specific steps include: S41. After receiving the second screening instruction, based on the deployed gauge pressure sensor, monitor the pressure changes in the coolant return pipe and the coolant supply pipe of the current cooling system to obtain a loop pressure data set, wherein the loop pressure data set includes the return pipe pressure value Fhg and the supply pipe pressure value Fgg in each monitoring period; and obtain the loop pressure difference value Fyc of the current cooling system based on the real-time related pressure changes in the return pipe and the supply pipe in the loop pressure data set.

[0032] Specifically, the steps of S4 also include: S42, according to the return pipe pressure value Fhg and the supply pipe pressure value Fgg of each monitoring period in the loop pressure data set, and combined with the statistical averaging algorithm, respectively obtain the return pipe pressure mean value and the standard deviation of the return pipe pressure , and the mean supply pipeline pressure and supply pipeline pressure standard deviation ; S421, based on the average pressure of the return pipe and the standard deviation of the return pipe pressure , get the normal fluctuation range of No. 1 pressure as , k is a constant, and the specific value is set by the administrator; S422, based on the average pressure of the supply pipeline and supply pipeline pressure standard deviation , get the normal fluctuation range of No. 2 pressure as , k is a constant, and the specific value is set by the administrator; S43, according to the normal fluctuation range of No. 1 pressure Normal fluctuation range of No. 2 pressure , obtain the normal pressure fluctuation average range , the specific acquisition process is as follows; .

[0033] Specifically, the steps of S4 also include: S44, by comparing the circuit pressure difference value Fyc of the current cooling system with the average range of normal pressure fluctuation A comparative analysis is conducted to determine whether there are any coolant leaks or blockages in the cooling system's piping circuits. The specific analysis is as follows: If the current cooling system circuit pressure difference Fyc is not within the normal pressure fluctuation average range When the icon is displayed, it indicates that there is a coolant leak or blockage in the cooling system's pipeline loop. At this time, maintenance personnel will be immediately dispatched to investigate and identify the leak and blockage points in the coolant loop pipeline. The leak will be repaired and refilled with coolant, and the blockage point in the coolant loop pipeline will be cleared. If the current cooling system circuit pressure difference Fyc is within the normal pressure fluctuation average range , it indicates that there are no leaks or blockages in the cooling system's pipeline loop, but the cooling equipment in the current cooling system has lost energy efficiency. Although the cooling effect meets the cooling needs of normal server operation, it causes serious waste and consumption of the enterprise's energy. In this case, the old cooling equipment should be replaced, or the current cooling system should be upgraded to a liquid cooling system.

[0034] It should be noted that the return pipe pressure value Fhg refers to the pressure in the pipe where the coolant flows from the cooling area back to the cooling unit; the supply pipe pressure value Fgg refers to the pressure in the pipe where the coolant is supplied from the cooling unit to the cooling area.

[0035] In this embodiment, the ability to identify potential problems in the coolant circuit is improved by real-time monitoring and detailed analysis of the cooling system circuit pressure. This step deploys multiple sets of sensors to continuously monitor the pressure changes in the return pipe and the supply pipe, generates a circuit pressure data set, and calculates the pressure mean and standard deviation through statistical methods, providing accurate data support for judging whether the cooling system is operating normally. By further analyzing the circuit pressure difference value Fyc, it is possible to identify potential problems in the coolant circuit when the circuit pressure difference value Fyc is not within the normal pressure fluctuation mean range. When a coolant leak or pipe blockage is detected, an alarm is immediately sent to the operator to start the maintenance process. At this time, the maintenance personnel can quickly locate and repair the leak and blockage points in the coolant circuit, thereby effectively avoiding the decline in cooling system efficiency or more serious failures. At the same time, when the circuit pressure difference Fyc is still within the normal pressure fluctuation average range If the system detects that the cooling fluid is not leaking or blocked in the cooling system's piping loop, it will intelligently identify and issue recommendations for cooling equipment replacement or cooling system upgrades to reduce energy waste and unnecessary operating costs caused by equipment energy efficiency degradation. The introduction of liquid cooling technology or the upgrade of cooling equipment can significantly improve cooling efficiency and reduce energy consumption. Overall, this cooling system monitoring and optimization process not only improves the accuracy and response speed of fault detection, but also identifies and resolves potential problems in advance through intelligent data analysis, effectively avoiding long-term cooling system downtime, reducing the impact on server performance, reducing maintenance costs, and extending the life of the equipment. At the same time, the improvement in energy efficiency also saves enterprises a lot of energy costs, optimizes the enterprise's operating costs, and improves the stability and sustainability of the cooling system. It brings significant economic and environmental benefits to data centers and high-energy-consuming enterprises, and improves the optimization management effect of enterprise energy.

[0036] Example 6 Please refer to Figure 1 and Figure 2,Specifically: An enterprise energy optimization system based on cloud computing and the Internet,,including a data acquisition module, a power consumption analysis module, a fault screening module, and an energy efficiency degradation determination module; The data acquisition module monitors the operating status of the server in the cooling state in real time based on multiple sets of deployed sensors, builds a server operation data set, and obtains the historical temperature value Tws corresponding to the power consumption at each historical monitoring point after querying and extracting several data strips stored in the database; The power consumption analysis module is used to obtain the state abnormality coefficient Xyc based on the acquired server operation data set and the historical temperature value Tws corresponding to the power consumption at each historical monitoring point, combined with the regression model, and compare it with the preset threshold to preliminarily determine whether there is an abnormality in the current cooling system operation state. If so, a first screening instruction will be issued externally; The fault screening module is configured to, upon receiving the first troubleshooting instruction, monitor the operating characteristics of the current cooling system, obtain a mechanical fault assessment coefficient Xgz after dimensionless processing, compare it with a preset threshold, and determine whether the abnormality in the current cooling system operating state is caused by a mechanical fault. If no mechanical fault exists, the module considers coolant leakage and blockage as problems and issues a second screening instruction. The energy efficiency degradation judgment module is used to monitor the pressure changes in the coolant return pipe and the coolant supply pipe of the current cooling system after receiving the second troubleshooting instruction, and judge whether there is leakage and blockage of the coolant in the pipe loop of the current cooling system based on the numerical value of the obtained loop pressure difference value Fyc. If not, it indicates that the energy efficiency of the cooling equipment of the current cooling system has declined.

[0037] In this embodiment, through the collaborative work of multiple modules, an efficient and accurate cooling system monitoring and troubleshooting solution is provided. First, the data acquisition module monitors the operating status of the server in real time and generates a detailed server operation data set, providing reliable data support for subsequent power consumption analysis. The power consumption analysis module combines historical temperature data and a regression model to calculate the state abnormality coefficient Xyc. When an abnormality occurs in the cooling system operating status, it can quickly issue a first screening instruction to provide early fault warning. After receiving the first screening instruction, the fault screening module further uses dimensionless processing and mechanical fault assessment to accurately determine the root cause of the fault. If mechanical fault factors are ruled out, it will conduct a detailed inspection for coolant leakage or blockage and issue a second screening instruction to ensure that the source of the fault is fully investigated. The energy efficiency degradation determination module determines whether there is coolant leakage or pipe blockage through loop pressure monitoring and can accurately identify energy efficiency degradation problems of the cooling equipment. The linkage of this series of modules can quickly and accurately locate the problem and take corresponding measures when an abnormality occurs in the cooling system, effectively reducing downtime and improving the operating efficiency and stability of the cooling system. At the same time, through early warning and optimized maintenance processes, not only the risk of equipment failure is greatly reduced, but also the service life of the equipment is effectively extended, energy waste is reduced, and the operating costs of the enterprise are significantly reduced; in general, the optimization method based on cloud computing and the Internet, combined with real-time sensor monitoring and data analysis technology, can effectively solve the problems of insufficient energy efficiency and untimely maintenance of traditional cooling systems, thereby strengthening the enterprise's energy optimization management and improving overall energy-saving benefits and stability.

[0038] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing enterprise energy based on cloud computing and the Internet, characterized by: The following steps are included: S1. Based on multiple sets of deployed sensors, the operating status of servers in cooling state is monitored in real time. A server operation data set is constructed. Based on several data strips stored in the database, the historical temperature value Tws corresponding to the power consumption at each historical monitoring point is obtained after querying and extracting. S2. Based on the acquired server operation data set and the historical temperature value Tws corresponding to the power consumption at each historical monitoring point, and in combination with the regression model, the state abnormality coefficient Xyc is obtained and compared with the preset threshold value to preliminarily determine whether there is an abnormality in the current cooling system operation state. If so, a first screening instruction is issued. S3. After receiving the first troubleshooting instruction, the operating characteristics of the current cooling system are monitored. After dimensionless processing, the mechanical fault assessment coefficient Xgz is obtained. The coefficient is compared with a preset threshold to determine whether the abnormal operating status of the current cooling system is caused by a mechanical fault. If no mechanical fault exists, coolant leakage and blockage are considered, and a second screening instruction is issued. S4. After receiving the second troubleshooting instruction, monitor the pressure changes in the coolant return pipe and the coolant supply pipe of the current cooling system, and determine whether there is a coolant leakage and blockage problem in the pipe loop of the current cooling system based on the value of the obtained loop pressure difference value Fyc. If not, it indicates that the energy efficiency of the cooling equipment of the current cooling system has declined.

2. The enterprise energy optimization method based on cloud computing and the Internet according to claim 1, characterized in that: The specific steps of S1 include: S11. Based on temperature sensors deployed on servers in large data centers, and in combination with power consumption monitoring devices connected to the server hardware management interface, regularly collected server power consumption and server temperature data are transmitted to a database via a network transmission method. The database classifies the data according to timestamps and records them according to fields related to collection time, power consumption, and temperature values, and records them as data strips. Each data strip includes power consumption, temperature value, and collection time information, and is aggregated and stored on a daily basis. By using indexing technology to query and extract data strips, the historical temperature value Tws corresponding to the power consumption at each historical monitoring point is obtained. S12. Based on the deployed multiple groups of sensors, the operating status of the server in the cooling state is monitored in real time to construct a server operating data set; the server operating data set includes the actual temperature value Twd corresponding to the power consumption at each monitoring point within the real-time monitoring period.

3. The enterprise energy optimization method based on cloud computing and the Internet according to claim 2, characterized in that: The specific steps of S2 include: S21. Calculate the temperature residual value Tcc corresponding to the power consumption at each monitoring point during the real-time monitoring period based on the acquired server operation data set and the historical temperature value Tws corresponding to the power consumption at each historical monitoring point. The temperature residual value Tcc is obtained specifically according to the following formula: ； Where, It is represented by the actual temperature value corresponding to the power consumption at the i-th monitoring point during the real-time monitoring period, and Tws is represented by The historical temperature values ​​corresponding to the same power conditions, It is represented as the temperature residual value corresponding to the power consumption at the i-th monitoring point within the real-time monitoring period.

4. The enterprise energy optimization method based on cloud computing and the Internet according to claim 3, characterized in that: The specific steps of S2 also include: S22. Based on the obtained temperature residual value Tcc corresponding to the power consumption at each monitoring point and in combination with the standardized residual calculation method of the regression model, obtain the abnormal state coefficient Xyc of the current cooling system. The abnormal state coefficient Xyc of the current cooling system is obtained by the following formula; ； Where, It is represented by the temperature residual value corresponding to the power consumption at the i-th monitoring point during the real-time monitoring period. It is expressed as the standard deviation of the temperature residual value, i=1, 2, 3, ..., n, where n is the number of monitoring points; S23. Preset an abnormality threshold Y, compare the acquired state abnormality coefficient Xyc with the preset abnormality threshold Y, and preliminarily determine whether there is an abnormality in the cooling system operation state. The specific analysis process is as follows: If the abnormal coefficient Xyc of the current cooling system is greater than or equal to the abnormal threshold Y, it is preliminarily determined that the current cooling system operation state is abnormal, and the first screening instruction will be issued. If the abnormality coefficient Xyc of the current cooling system state is less than the abnormality threshold value Y, it is preliminarily determined that there is no abnormality in the current cooling system operation state, and no additional first screening instruction is issued at this time.

5. The enterprise energy optimization method based on cloud computing and the Internet according to claim 4, characterized in that: The specific steps of S3 include: S31. After receiving the first screening instruction, monitor the operating characteristics of the current cooling system according to the deployed groups of sensors, and obtain the operating characteristic data information of the current cooling system in combination with Fourier transform. The operating characteristic data information includes the vibration frequency Vzd, amplitude Fzf, and noise value Zzs generated during the operation of the current cooling system; S32. According to the acquired operation characteristic data information, after dimensionless processing, a mechanical fault assessment coefficient Xgz is obtained by fitting. The mechanical fault assessment coefficient Xgz is obtained by the following formula; ； Where Vzd represents the vibration frequency, Fzf represents the amplitude, and Zzs represents the noise value. 、 and They represent the weight values ​​of vibration frequency Vzd, amplitude Fzf and noise value Zzs respectively, and A represents the correction constant.

6. The enterprise energy optimization method based on cloud computing and the Internet according to claim 5, characterized in that: The specific steps of S3 also include: S33. Compare the mechanical fault assessment coefficient Xgz with a preset threshold value. If the mechanical fault assessment coefficient Xgz exceeds the preset threshold value, it indicates that the abnormality in the current cooling system operation state is caused by a mechanical fault. At this time, the problems of coolant leakage and blockage will be ruled out, and maintenance personnel will be immediately arranged to conduct a comprehensive inspection and repair of the faulty equipment in the cooling system. If the mechanical fault assessment coefficient Xgz does not exceed the preset threshold value, it indicates that the abnormality in the current cooling system operation state is not caused by a mechanical fault. At this time, the problems of coolant leakage and blockage will be considered, and a second screening instruction will be issued.

7. The enterprise energy optimization method based on cloud computing and the Internet according to claim 6, characterized in that: The specific steps of S4 include: S41. After receiving the second screening instruction, monitor the pressure changes in the coolant return pipe and the coolant supply pipe of the current cooling system to obtain a loop pressure data set, wherein the loop pressure data set includes the return pipe pressure value Fhg and the supply pipe pressure value Fgg in each monitoring period; and obtain the loop pressure difference value Fyc of the current cooling system based on the real-time related pressure changes in the return pipe and the supply pipe in the loop pressure data set.

8. The enterprise energy optimization method based on cloud computing and the Internet according to claim 7, characterized in that: The specific steps of S4 also include: S42, according to the return pipe pressure value Fhg and the supply pipe pressure value Fgg of each monitoring period in the loop pressure data set, and combined with the statistical averaging algorithm, respectively obtain the return pipe pressure mean value and the standard deviation of the return pipe pressure , and the mean supply pipeline pressure and supply pipeline pressure standard deviation ; S421, based on the average pressure of the return pipe and the standard deviation of the return pipe pressure , get the normal fluctuation range of No. 1 pressure as , k is a constant, and the specific value is set by the administrator; S422, based on the average pressure of the supply pipeline and supply pipeline pressure standard deviation , get the normal fluctuation range of No. 2 pressure as , k is a constant, and the specific value is set by the administrator; S43, according to the normal fluctuation range of No. 1 pressure Normal fluctuation range of No. 2 pressure , obtain the normal pressure fluctuation average range , the specific acquisition process is as follows; 。 9. The enterprise energy optimization method based on cloud computing and the Internet according to claim 8, characterized in that: The specific steps of S4 also include: S44, by comparing the circuit pressure difference value Fyc of the current cooling system with the average range of normal pressure fluctuation A comparative analysis is conducted to determine whether there are any coolant leaks or blockages in the cooling system's piping circuits. The specific analysis is as follows: If the current cooling system circuit pressure difference Fyc is not within the normal pressure fluctuation average range When the icon is displayed, it indicates that there is a coolant leak or blockage in the cooling system's pipeline loop. At this time, maintenance personnel will be immediately dispatched to investigate and identify the leak and blockage points in the coolant loop pipeline. The leak will be repaired and refilled with coolant, and the blockage point in the coolant loop pipeline will be cleared. If the current cooling system circuit pressure difference Fyc is within the normal pressure fluctuation average range , it means that there is no leakage or blockage problem in the coolant pipeline circuit of the current cooling system, but the energy efficiency of the cooling equipment of the current cooling system has declined. At this time, the old cooling equipment will be replaced, or the current cooling system will be upgraded to a cooling system with liquid cooling.

10. A cloud computing and Internet-based enterprise energy optimization system, used to implement the cloud computing and Internet-based enterprise energy optimization method according to any one of claims 1 to 9, characterized in that: Including data acquisition module, power consumption analysis module, fault screening module and energy efficiency degradation judgment module; The data acquisition module monitors the operating status of the server in the cooling state in real time based on multiple sets of deployed sensors, builds a server operation data set, and obtains the historical temperature value Tws corresponding to the power consumption at each historical monitoring point after querying and extracting several data strips stored in the database; The power consumption analysis module is used to obtain the state abnormality coefficient Xyc based on the acquired server operation data set and the historical temperature value Tws corresponding to the power consumption at each historical monitoring point, combined with the regression model, and compare it with the preset threshold to preliminarily determine whether there is an abnormality in the current cooling system operation state. If so, a first screening instruction will be issued externally; The fault screening module is configured to, upon receiving the first troubleshooting instruction, monitor the operating characteristics of the current cooling system, obtain a mechanical fault assessment coefficient Xgz after dimensionless processing, compare it with a preset threshold, and determine whether the abnormality in the current cooling system operating state is caused by a mechanical fault. If no mechanical fault exists, the module considers coolant leakage and blockage as problems and issues a second screening instruction. The energy efficiency degradation judgment module is used to monitor the pressure changes in the coolant return pipe and the coolant supply pipe of the current cooling system after receiving the second troubleshooting instruction, and judge whether there is leakage and blockage of the coolant in the pipe loop of the current cooling system based on the numerical value of the obtained loop pressure difference value Fyc. If not, it indicates that the energy efficiency of the cooling equipment of the current cooling system has declined.

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