Data center power monitoring method and system based on remote management

Abstract

The invention provides a data center power monitoring method and system based on remote management, and the method comprises the steps: determining a power utilization predicted value of a power utilization device in a real-time target time period based on historical power utilization data and real-time power utilization data of a target power utilization device; a linear programming algorithm is adopted, the power consumption predicted value is combined, the total power supply amount is distributed, and a real-time power distribution value of the target power consumption equipment is determined; the performance index of the target electric equipment is obtained, the operation parameters of the target electric equipment are adjusted according to the real-time power distribution value and the performance index, and the operation parameters meet the operation requirements of the target electric equipment. According to the application, the power consumption data of the target power consumption equipment can be monitored, the power consumption predicted value in the future time is predicted in combination with the historical power consumption data, and the power is distributed to the total power supply in combination with the power consumption predicted value, so that the operation parameters of each target power consumption equipment meet the operation parameters, and the overall operation efficiency of the power consumption equipment is improved.

Description

Technical Field

[0001] The present application relates to the field of remote management, and in particular to a data center power monitoring method and system based on remote management. Background Art

[0002] In modern industry and energy management, optimizing power resource allocation and improving equipment operating efficiency are crucial. This area is directly related to controlling production costs and maintaining a sustainable environment. However, many current industrial control software systems rely on static power distribution methods and simple monitoring methods, making them difficult to adapt to complex operating environments and dynamically changing power demands, resulting in wasted resources and reduced equipment performance. The core challenge facing industrial control software lies in achieving real-time power consumption monitoring and dynamic power allocation adjustments. Due to a lack of accurate energy consumption data collection and analysis capabilities, industrial control software often cannot accurately grasp the actual power usage of each device. This further leads to uneven power distribution. Some devices may have limited operation due to insufficient power, while others may suffer unnecessary power losses due to excess power. Summary of the Invention

[0003] In view of this, the present application provides a data center power monitoring method and system based on remote management, the main purpose of which is to solve the problem that industrial control software is often unable to accurately grasp the actual power consumption of each device, resulting in unbalanced power distribution and low overall operating efficiency of the equipment group.

[0004] To achieve the above objectives, the present application discloses, in a first aspect, a data center power monitoring method based on remote management, the method comprising: Determining a predicted power consumption value of the target power-consuming device within a real-time target time period based on the historical power consumption data of the target power-consuming device and the real-time power consumption data; Using a linear programming algorithm, combined with the power consumption forecast value, to allocate the total power supply, and determine the real-time power allocation value for the target power-consuming device; The performance index of the target electric device is obtained, and the operating parameters of the target electric device are adjusted according to the real-time power distribution value and the performance index, so that the operating parameters meet the operating requirements of the target electric device.

[0005] In a second aspect of the present application, an embodiment provides a data center power monitoring system based on remote management, the system comprising: A first determining module is configured to determine a predicted power consumption value of a target power-consuming device within a real-time target time period based on historical power consumption data of the target power-consuming device and the real-time power consumption data; A second determining module is configured to allocate the total power supply using a linear programming algorithm in combination with the power consumption forecast value, and determine a real-time power allocation value for the target power-consuming device; The adjustment module is used to obtain the performance index of the target electrical equipment and adjust the operating parameters of the target electrical equipment according to the real-time power distribution value and the performance index, so that the operating parameters meet the operating requirements of the target electrical equipment.

[0006] In a third aspect of the present application, an embodiment provides an electronic device, including: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute any one of the methods disclosed in the first aspect.

[0007] In a fourth aspect of the present application, an embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method described in the first aspect is implemented.

[0008] In summary, according to the technical solution disclosed in this application, this application discloses a data center power monitoring method based on remote management, which determines the power consumption forecast value of the power consumer within the real-time target time period based on the historical power consumption data and real-time power consumption data of the target power consumer; adopts a linear programming algorithm, combined with the power consumption forecast value, to allocate the total power supply, and determine the real-time power allocation value for the target power consumer; obtains the performance index of the target power consumer, and adjusts the operating parameters of the target power consumer according to the real-time power allocation value and the performance index, so that the operating parameters meet the operating requirements of the target power consumer. This application can monitor the power consumption data of the target power consumer, and predict the power consumption forecast value in the future time in combination with the historical power consumption data, and allocate power to the total power supply in combination with the power consumption forecast value, so that the operating parameters of each target power consumer meet the operating parameters, thereby improving the overall operating efficiency of the power consumer.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0012] Figure 1 A flow chart of a data center power monitoring method based on remote management provided by an embodiment of the present application is shown; Figure 2 The structure diagram of the data center power monitoring system based on remote management provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0013] In order to more clearly understand the above-mentioned objectives, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0014] In order to solve the problem that industrial control software often cannot accurately grasp the actual power consumption of each device, resulting in uneven power distribution and low overall operating efficiency of the device group, this application provides the following embodiments to solve the above problem: This embodiment provides a data center power monitoring method based on remote management, such as Figure 1 FIG. 1 is a flow chart of the method of this embodiment, and the method of this embodiment may specifically include the following steps: Step 101: Determine a predicted power consumption value of a target power-consuming device within a real-time target time period based on historical power consumption data and real-time power consumption data of the target power-consuming device.

[0015] In some embodiments, determining a predicted power consumption value of the target electric device within a real-time target time period based on historical power consumption data and real-time power consumption data of the target electric device includes: Acquire real-time power consumption data of the power-consuming device group; use the energy consumption analysis model to determine the target power-consuming device in the power-consuming device group, where the target power-consuming device is a high-consuming device or a low-consuming device in the power-consuming device group.

[0016] A distributed sensor network collects current and voltage values from each device in a cluster of electrical users in real time to generate an initial power consumption data stream. Raw data is obtained from sensors for each device's operating state and load, and the data stream is stored in a pre-established database, resulting in a structured initial power consumption dataset. Data cleaning tools are used to preprocess the initial power consumption dataset, correcting any noise and missing values. If missing values are detected in a device's data record, the data is interpolated using the average values of adjacent time points to determine a complete power consumption data record. Time series analysis tools are used to segment the complete power consumption data record. The average value of each data segment is obtained for each device's operating state over different time periods to determine the device's energy consumption distribution characteristics. Data visualization tools are used to generate real-time charts based on these energy consumption distribution characteristics. Device energy consumption values exceeding a preset threshold are highlighted, resulting in intuitive energy consumption distribution results.

[0017] For example, when using a distributed sensor network to collect real-time device current and voltage values, imagine multiple motors in an industrial production plant, each equipped with current and voltage sensors, collecting data once per second to form an initial stream of electricity usage data. The sensors transmit the collected raw data to a cloud database, where it is stored as a structured dataset containing fields such as timestamp, device ID, current value, and voltage value. This approach ensures high-frequency data collection and centralized management, providing a reliable foundation for subsequent analysis.

[0018] For example, in time series analysis and segmented processing, a day's electricity usage data can be divided into three time periods based on the device's operating status: on, standby, and off. The average energy consumption for each period can be calculated. For example, if a motor is on from 8:00 AM to 12:00 PM, with an average current of 10.2 A and a voltage of 220.3 V, the calculated average power is approximately 2247 watts. However, in standby mode from 12:00 PM to 2:00 PM, the average power drops to 500 watts. This segmented analysis clearly demonstrates the energy consumption distribution characteristics of the device in different states, helping to identify periods of high energy consumption.

[0019] One possible implementation involves visualizing energy consumption distribution characteristics using real-time charting tools to generate line or bar charts showing hourly energy consumption changes. For example, if the preset energy consumption threshold is 2000 watts, when a motor's energy consumption reaches 2300 watts at 10:00 AM, the bar at that point in the chart will be highlighted in red, alerting users to the abnormally high energy consumption. This intuitive display not only allows managers to quickly identify problems but also provides data support for energy-saving optimization.

[0020] For example, the above-mentioned methods have demonstrated significant technical benefits in practical applications. Time series segmentation analysis helps identify equipment operating patterns and optimize power usage plans; while real-time visualization charts and threshold annotations provide timely warnings of abnormal conditions, reducing the risk of energy waste. These links support each other, forming a complete chain from data collection to result presentation, ultimately improving the efficiency and accuracy of equipment energy management.

[0021] When processing power consumption data streams, a preliminary summary of each device's power consumption is performed. Data sorting tools can then be used to categorize the operating time periods and load states within the data stream. For example, in an industrial workshop, the data stream contains minute-by-minute power consumption information for multiple devices. The data sorting tool extracts the corresponding power consumption data based on the device's operating time period, such as the power-on period from 8:00 AM to 12:00 PM, and load status, such as full load or light load, to form a classified power consumption dataset. This classification method helps break down complex data streams into more targeted subsets, laying the foundation for subsequent analysis.

[0022] For example, when using a data comparison tool to compare against preset thresholds, a device's normal power consumption range can be set between 500 and 2000 watts. If a device's power consumption reaches 2300 watts during a certain period, exceeding the upper limit, it will be marked as abnormally high. If another device's power consumption is only 300 watts, below the lower limit, it will be marked as abnormally low. These marked devices will form a preliminary abnormal device set. This comparison mechanism can quickly screen out potentially problematic devices, improving the efficiency of anomaly detection.

[0023] For example, when conducting a secondary review of the initial set of abnormal devices, the status judgment tool analyzes the device's operating environment and historical records. For example, if a device is marked as having abnormally high power consumption, but verification reveals that its operating environment is in a high-temperature state and historical records show that power consumption often fluctuates in similar environments, the device will not be confirmed as a device requiring attention if the abnormal state does not persist for more than the preset 30 minutes. Conversely, if another device's abnormally high power consumption persists for more than 30 minutes without a reasonable environmental explanation, it will be added to the final abnormal device list. This secondary review method effectively reduces misjudgments and ensures the accuracy of abnormality judgments.

[0024] When sorting through the final list of abnormal devices, the sorting tool ranks devices based on the severity of the abnormality and monitoring priority. For example, if there are three devices on the list, exceeding thresholds by 10%, 20%, and 5%, respectively, and with monitoring priorities of high, medium, and low, the sorting tool prioritizes the device with a 20% excess and a high priority. For these devices, operating parameters such as current, voltage, and operating hours are obtained to determine the priority sequence for adjustment. This sorting mechanism ensures that resources are focused on the devices most in need of adjustment, improving management efficiency. This method, from data sorting to final sorting, forms a complete process for identifying and prioritizing abnormal devices, ensuring the systematic and reliable analysis of electricity usage data.

[0025] In industrial workshop scenarios, when extracting historical power usage records and current load information from a repository for equipment marked as abnormal, the data can be categorized by time period. For example, if the repository contains power usage data for the past year, the data can be categorized by daily work hours, such as 8:00 AM to 12:00 PM and 1:00 PM to 5:00 PM. The power usage for each period can be sorted to form a historical power usage dataset. This categorization helps more clearly observe the power usage patterns of equipment in different time periods, providing a foundation for subsequent analysis.

[0026] For example, using data comparison tools, you can compare classified historical electricity usage data sets with real-time load data, focusing on peak usage and load fluctuations. For example, suppose a device's historical average morning power consumption is 1500 watts, but real-time data shows a recent rise to 1800 watts during the same period, along with frequent load fluctuations. This suggests a possible increase in electricity demand. By plotting a power usage curve, you can visually visualize this upward trend, providing a basis for subsequent forecasting.

[0027] For example, when using a forecasting tool to estimate power demand for a future time period, it can combine device status and operating parameters for analysis. For example, if a device is currently fully loaded and operating parameters indicate elevated temperatures, the forecasting tool might infer that power demand will continue to rise over the next week. If the forecast indicates that power demand may reach 2000 watts, exceeding the preset threshold of 1800 watts, this is flagged as potentially abnormal demand. This forecasting approach can identify potential problem devices in advance.

[0028] For example, when making secondary adjustments to the initial forecast, the correction tool incorporates historical peak power consumption data. For example, if the highest peak value in the historical data is 1900 watts and the initial forecast is 2000 watts, the correction tool might adjust the forecast to 1950 watts based on historical fluctuations, as the final forecast value. This secondary adjustment improves forecast accuracy and avoids overly aggressive or conservative judgments. This approach forms a complete power demand analysis process, from historical data classification to final forecast determination, that effectively identifies potential anomalies and provides reliable forecast results.

[0029] Step 102 : using a linear programming algorithm and combining the power consumption forecast value, the total power supply is allocated to determine the real-time power allocation value for the target power-consuming equipment.

[0030] Based on a pre-established power supply database, the current total supply data and the power consumption forecast data of each device are obtained. The forecast data is compared with the supply limit. If the total forecast demand exceeds the supply limit, the quota is preliminarily divided according to the device priority to obtain an initial allocation ratio table. Based on the initial allocation ratio table, a linear programming tool is used to dynamically adjust the power quota of each device, increasing the quota ratio for devices with higher power consumption forecasts and reducing the quota ratio for devices with lower forecasts to determine the adjusted quota allocation list. Based on the quota allocation list, the device load data is obtained in combination with a real-time monitoring tool. If the demand fluctuation of a certain device during operation exceeds the preset threshold range, the quota of the device is fine-tuned twice to obtain a real-time revised allocation detail. Based on the real-time revised allocation details, the load balancing tool is used to finally distribute the power resources, match the power demand of each device with the actual supply situation, and determine the final power allocation plan.

[0031] For example, in an industrial workshop power management scenario, a pre-established power supply database can be used to obtain current total supply data and forecasted power consumption for each device. Suppose the database shows a current total supply of 10,000 watts, while the forecast model indicates a total demand of 12,000 watts for each device, significantly exceeding the supply limit. In this case, a preliminary division of power by device priority is necessary to generate an initial allocation table. For example, suppose there are three devices in the workshop, with priorities listed as Device A, Device B, and Device C, respectively. The initial allocation ratios might be 50%, 30%, and 20%, meaning that Device A receives 5,000 watts, Device B receives 3,000 watts, and Device C receives 2,000 watts.

[0032] For example, when using linear programming tools for dynamic adjustments, quotas can be increased for devices with higher predicted power consumption, while those with lower predicted power consumption can be reduced. For example, if device A's predicted demand is 6,000 watts, device B's is 4,000 watts, and device C's is 2,000 watts, adjustments might result in device A's quota being increased to 5,500 watts, device B's to 3,500 watts, and device C's to 1,000 watts, forming the adjusted quota allocation list. This adjustment approach better aligns with actual demand and ensures the operational stability of high-priority devices.

[0033] For example, by integrating real-time monitoring tools to collect device load data, if a device's operating demand fluctuates beyond a preset threshold, a secondary fine-tuning process is necessary. For example, if the preset threshold for device B is 3,000 to 4,000 watts, and real-time load data suddenly reaches 4,500 watts, exceeding the upper limit, the quota can be fine-tuned from 3,500 watts to 4,000 watts, resulting in a real-time revised allocation. This fine-tuning mechanism allows for timely response to sudden changes in device operation, ensuring flexible power distribution.

[0034] For example, during the final distribution phase, load balancing tools are used to match power resources and determine the final allocation. Suppose, after adjustment, the quotas for devices A, B, and C are 5,500 watts, 4,000 watts, and 1,000 watts, respectively, while the actual supply remains at 10,000 watts. The load balancing tool will further fine-tune the supply based on real-time load conditions to ensure that the demand and supply of each device match. If the actual demand of device C drops to 800 watts, the remaining 200 watts can be reallocated to device A or B. This approach optimizes resource utilization and reduces waste.

[0035] For example, at the business level, power allocation is crucial for ensuring the continuity of equipment operation and production efficiency in the workshop. Through a comprehensive process of database data acquisition, prioritization, dynamic adjustments, real-time fine-tuning, and final matching, we can effectively address supply and demand imbalances. In particular, prioritizing power needs for high-priority equipment can avoid production interruptions. For low-priority equipment, appropriately reducing power allocations can help balance overall resource allocation.

[0036] Step 103 : Acquire the performance index of the target electrical equipment, and adjust the operating parameters of the target electrical equipment according to the real-time power distribution value and the performance index, so that the operating parameters meet the operating requirements of the target electrical equipment.

[0037] In some embodiments, adjusting the operating parameters of the target powered device according to the real-time power allocation value and the performance indicator includes: Adjust the operating parameters of the target electrical equipment according to the real-time power distribution value to obtain the working parameters of the target electrical equipment; determine whether the working parameters meet the performance indicators; if the working parameters do not meet the performance indicators, adjust the working parameters to the operating parameters, and the operating parameters meet the performance indicators.

[0038] Based on the real-time power distribution plan, the operating frequency and power setting data for each device is retrieved from a pre-established device database. This data is compared with the device's rated values. If a device's operating frequency or power setting exceeds the rated range, the control module generates preliminary correction instructions and determines the list of parameters to be adjusted. Based on the list of parameters to be adjusted, the device load data fed back by the real-time monitoring tool is obtained and compared with the preset threshold range. If the load fluctuation of a device exceeds the threshold range, the control module sends specific correction instructions to obtain the adjusted operating parameter configuration. Based on the adjusted operating parameter configuration, the current power matching status is obtained from the device operation log and the matching status is verified for consistency with the power distribution plan. If the matching results show deviations, the load balancing tool is used to fine-tune resources and determine the final operating parameter values. Based on the final operating parameter values, the control module distributes parameters to each device, obtains load feedback from the device under the new operating state, and determines the stability of the device operation.

[0039] For example, in power management scenarios in industrial workshops, adjusting equipment operating parameters and optimizing power distribution are critical. Retrieving operating frequency and power setting data from the equipment database is the first step. Consider three core devices in a workshop: Device X, Device Y, and Device Z. Their rated frequencies are 50 Hz, 60 Hz, and 55 Hz, respectively, and their rated powers are 2000 W, 3000 W, and 1500 W. Comparison reveals that Device X's actual operating frequency is 55 Hz, exceeding the rated range. The control module generates a preliminary correction instruction to adjust the frequency back to 50 Hz and creates a list of parameters to be adjusted.

[0040] For example, by comparing real-time load data with threshold values, adjustments can be refined. For example, suppose the load threshold for device Y is set between 2500 and 3500 watts, but the real-time monitoring tool reports that its load has reached 3800 watts, significantly exceeding the upper limit. The control module will issue a specific correction instruction to reduce the power setting to 3200 watts to ensure the device operates within a safe range. This approach enables timely response to load fluctuations and ensures stable device operation.

[0041] For example, after adjusting operating parameter configurations, the current power matching status needs to be extracted from the device operation log and verified for consistency. Suppose device Z is allocated 1500 watts, but the log shows that it is actually using only 1200 watts, indicating a discrepancy. The load balancing tool will reallocate the remaining 300 watts to other devices to ensure efficient overall power utilization. This verification mechanism helps identify potential mismatches and optimize resource allocation.

[0042] For example, after delivering final operating parameter values and obtaining load feedback, the control module applies the adjusted parameters to the device. For example, suppose that after adjusting the frequency of device X back to 50 Hz, load feedback indicates stable operation and current fluctuations within normal ranges. This indicates that the parameter adjustment is effective, maintaining long-term stable operation of the device while reducing the risk of failures caused by improper parameters.

[0043] For example, from a business perspective, comparing operating frequency and power settings, real-time monitoring of load data, and parameter consistency verification together form a complete closed-loop device management system. This closed-loop management system is based on the fact that device operating parameters may deviate from rated values due to environmental or load changes. Consequently, multi-level adjustments are implemented to ensure optimal device operation. This approach not only improves device reliability but also effectively extends equipment life, providing continuous assurance for workshop production.

[0044] After adjusting the operating parameters of the target electrical equipment according to the real-time power distribution value and the performance index, the method further includes: Determine the load demand of the target electrical equipment; determine the actual load of the target electrical equipment based on the operating parameters; and determine the equipment efficiency distribution of the target electrical equipment based on the load demand and the actual load.

[0045] Based on the real-time power distribution plan, the operating frequency and power setting data for each device is retrieved from a pre-established device database. This data is compared with the device's rated values. If a device's operating frequency or power setting exceeds the rated range, the control module generates preliminary correction instructions and determines the list of parameters to be adjusted. Based on the list of parameters to be adjusted, the device load data fed back by the real-time monitoring tool is obtained and compared with the preset threshold range. If the load fluctuation of a device exceeds the threshold range, the control module sends specific correction instructions to obtain the adjusted operating parameter configuration. Based on the adjusted operating parameter configuration, the current power matching status is obtained from the device operation log and the matching status is verified for consistency with the power distribution plan. If the matching results show deviations, the load balancing tool is used to fine-tune resources and determine the final operating parameter values. Based on the final operating parameter values, the control module distributes parameters to each device, obtains load feedback from the device under the new operating state, and determines the stability of the device operation.

[0046] For example, in power management scenarios in industrial workshops, adjusting equipment operating parameters and optimizing power distribution are critical. Retrieving operating frequency and power setting data from the equipment database is the first step. Consider three core devices in a workshop: Device X, Device Y, and Device Z. Their rated frequencies are 50 Hz, 60 Hz, and 55 Hz, respectively, and their rated powers are 2000 W, 3000 W, and 1500 W. Comparison reveals that Device X's actual operating frequency is 55 Hz, exceeding the rated range. The control module generates a preliminary correction instruction to adjust the frequency back to 50 Hz and creates a list of parameters to be adjusted.

[0047] For example, by comparing real-time load data with threshold values, adjustments can be refined. For example, suppose the load threshold for device Y is set between 2500 and 3500 watts, but the real-time monitoring tool reports that its load has reached 3800 watts, significantly exceeding the upper limit. The control module will issue a specific correction instruction to reduce the power setting to 3200 watts to ensure the device operates within a safe range. This approach enables timely response to load fluctuations and ensures stable device operation.

[0048] For example, after adjusting operating parameter configurations, the current power matching status needs to be extracted from the device operation log and verified for consistency. Suppose device Z is allocated 1500 watts, but the log shows that it is actually using only 1200 watts, indicating a discrepancy. The load balancing tool will reallocate the remaining 300 watts to other devices to ensure efficient overall power utilization. This verification mechanism helps identify potential mismatches and optimize resource allocation.

[0049] For example, after delivering final operating parameter values and obtaining load feedback, the control module applies the adjusted parameters to the device. For example, suppose that after adjusting the frequency of device X back to 50 Hz, load feedback indicates stable operation and current fluctuations within normal ranges. This indicates that the parameter adjustment is effective, maintaining long-term stable operation of the device while reducing the risk of failures caused by improper parameters.

[0050] In some embodiments, based on the requirements for building an operational database, adjusted power usage data and performance indicators are retrieved from a pre-established storage tool. The data is categorized and organized, and a structured historical data archive is generated using a data integration tool to verify the archive's integrity. Using the historical data archive, real-time data from the current device is retrieved from a real-time monitoring tool. Differences between the historical data archive and the real-time data are compared. If the difference exceeds a preset threshold, a difference report is generated using a data logging tool to determine the impact of the difference on operational stability. Using the difference report, data related to operational stability is retrieved from peak load records. Correlation analysis is performed on the data and performance trends. A trend analysis tool is used to generate a long-term performance trend chart to determine the chart's fluctuation characteristics. Based on the fluctuation characteristics of the performance trend chart, data related to device lifespan is retrieved from the device's operation log. The data is compared against a preset threshold. If the data exceeds the threshold, a prediction tool is used to generate a maintenance cycle prediction result to determine the reference basis for the prediction result.

[0051] For example, in power management scenarios for industrial workshops, building an operational database to support equipment optimization is a critical step. By obtaining adjusted power usage data and performance indicators from storage tools, data classification tools can be used to organize information such as voltage, current, and power by time period and device type, creating a structured historical data archive. For example, if the voltage data for a device is 220 volts, which is within the normal range, the archive's integrity can be verified by comparing data loss rates to ensure no omissions.

[0052] For example, by comparing historical data archives with real-time data, real-time monitoring tools can reveal the current operating status of a device. For example, if the average power of a device in the historical archive is 10 kilowatts, while the real-time data shows 12 kilowatts, exceeding the preset threshold by 1 kilowatt, the data logging tool will generate a discrepancy report to analyze its potential impact on operational stability, such as the possibility of device overload.

[0053] For example, when analyzing the correlation between discrepancy reports and peak load records, you can extract peak data. For example, if the load demand is 80%, but the actual operating power is higher, it may affect stability. Use trend analysis tools to generate long-term performance trend charts. If the chart shows frequent power fluctuations, characterized by periodic increases, it is necessary to pay attention to the continuity of equipment operation.

[0054] For example, by comparing the fluctuation characteristics of performance trend charts with equipment life data, the cumulative operating hours of the equipment can be extracted from the operation log. For example, if the cumulative operating hours are 8,000 hours, which is close to the preset life threshold of 9,000 hours, the prediction tool can generate a maintenance cycle prediction result and recommend early scheduling of maintenance based on historical failure rates and current fluctuation characteristics. This approach helps extend the service life of the equipment.

[0055] Specifically, the process of generating historical archives using data integration tools can be achieved through tiered storage, with data archived by day, week, and month to ensure efficient queries. Verification mechanisms can also be implemented to confirm archive integrity. If the data missing rate on a given day is less than 1%, it is considered complete, ensuring the reliability of subsequent analysis.

[0056] Specifically, when generating a difference report, you can set multi-dimensional indicators, such as temperature, current, etc. Assuming that the temperature difference is 5 degrees Celsius, which exceeds the threshold of 2 degrees Celsius, it needs to be marked as a high-risk item in the report to remind you to pay attention to heat dissipation issues, thereby improving operational stability.

[0057] In some embodiments, after determining the lifespan trend of the target electrical equipment, the method further includes: updating the load demand and the actual load in the historical electrical consumption data; and determining the lifespan trend of the target electrical equipment based on the updated historical electrical consumption data.

[0058] Based on the requirements for building the operational database, adjusted power usage data and performance indicators are retrieved from a pre-established storage tool. This data is categorized and organized, and a structured historical data archive is generated using a data integration tool to verify the archive's integrity. Using this historical data archive, real-time data from the current device is retrieved from the real-time monitoring tool. Differences between the historical data archive and the real-time data are compared. If the difference exceeds a preset threshold, a difference report is generated using a data logging tool to determine the impact of the difference on operational stability. Using this difference report, data related to operational stability is retrieved from peak load records. Correlation analysis is performed on this data with performance trends. A long-term performance trend chart is generated using a trend analysis tool to determine the fluctuation characteristics of the chart. Based on the fluctuation characteristics of the performance trend chart, data related to device lifespan is retrieved from the device operation log and compared against a preset threshold. If the data exceeds the threshold, a prediction tool is used to generate a maintenance cycle prediction result to determine the reference basis for the prediction result.

[0059] For example, in power management scenarios for industrial workshops, building an operational database to support equipment optimization is a critical step. By obtaining adjusted power usage data and performance indicators from storage tools, data classification tools can be used to organize information such as voltage, current, and power by time period and device type, creating a structured historical data archive. For example, if the voltage data for a device is 220 volts, which is within the normal range, the archive's integrity can be verified by comparing data loss rates to ensure no omissions.

[0060] For example, by comparing historical data archives with real-time data, real-time monitoring tools can reveal the current operating status of a device. For example, if the average power of a device in the historical archive is 10 kilowatts, while the real-time data shows 12 kilowatts, exceeding the preset threshold by 1 kilowatt, the data logging tool will generate a discrepancy report to analyze its potential impact on operational stability, such as the possibility of device overload.

[0061] For example, when analyzing the correlation between discrepancy reports and peak load records, you can extract peak data. For example, if the load demand is 80%, but the actual operating power is higher, it may affect stability. Use trend analysis tools to generate long-term performance trend charts. If the chart shows frequent power fluctuations, characterized by periodic increases, it is necessary to pay attention to the continuity of equipment operation.

[0062] For example, by comparing the fluctuation characteristics of performance trend charts with equipment life data, the cumulative operating hours of the equipment can be extracted from the operation log. For example, if the cumulative operating hours are 8,000 hours, which is close to the preset life threshold of 9,000 hours, the prediction tool can generate a maintenance cycle prediction result and recommend early scheduling of maintenance based on historical failure rates and current fluctuation characteristics. This approach helps extend the service life of the equipment.

[0063] Specifically, the process of generating historical archives using data integration tools can be achieved through tiered storage, with data archived by day, week, and month to ensure efficient queries. Verification mechanisms can also be implemented to confirm archive integrity. If the data missing rate on a given day is less than 1%, it is considered complete, ensuring the reliability of subsequent analysis.

[0064] Specifically, when generating a difference report, you can set multi-dimensional indicators, such as temperature, current, etc. Assuming that the temperature difference is 5 degrees Celsius, which exceeds the threshold of 2 degrees Celsius, it needs to be marked as a high-risk item in the report to remind you to pay attention to heat dissipation issues, thereby improving operational stability.

[0065] In some embodiments, determining a service executed in at least one target powered device; Determine the maintenance cycle of operating parameters based on business, life trends and operating parameters.

[0066] Based on business cycles and business continuity requirements, combined with equipment life predictions and parameter adjustment effects, the maintenance frequency and optimization space of equipment in different business stages are analyzed. If the prediction results show that the equipment is approaching its maintenance cycle or the adjustment effect is lower than the business stability standard, it is marked as a high-priority device, and an adjustment plan for its operating parameters and maintenance plan is determined. Based on the recorded data of the business cycle, historical load information related to business continuity is obtained from a pre-established storage tool. This information is classified and organized, and a structured load profile is generated using a data integration tool to determine the integrity of the profile. Using this load profile, operational data related to the equipment lifespan is obtained from the equipment operation log. This data is compared with a preset threshold range. If it exceeds the threshold range, a lifespan assessment report is generated using the data recording tool to determine the risk level in the report. Using this lifespan assessment report, adjustment information related to operating parameters is obtained from the historical parameter adjustment records. The degree of match between this information and stability standards is compared. If the degree of match is below the preset threshold, a parameter analysis tool is used to generate an adjustment proposal to determine the feasibility of the proposal. Based on this adjustment proposal, data related to maintenance frequency is obtained from the maintenance plan archive and compared with the priority-marked conditions. If the high-priority conditions are met, an optimized maintenance schedule is generated using the schedule generation tool, and the execution order of the schedule is determined.

[0067] For example, in the power management scenario of an industrial workshop, historical load information can be extracted from storage tools for recorded data during the business cycle, focusing on the operating pressure of equipment in different time periods. First, load information is categorized and organized by day, week, and month using data integration tools to form a structured load profile. The integrity of the profile can be verified by checking the data missing rate. If the data missing rate for a particular month is less than 0.5%, it is considered complete, ensuring the reliability of subsequent analysis.

[0068] For example, based on load profiles, operational data, such as cumulative operating hours or peak load, is extracted from equipment operation logs and compared against pre-set thresholds. For example, if a piece of equipment has accumulated 8,500 hours of operation, approaching the 9,000-hour threshold, a lifespan assessment report can be generated using the data logging tool. The report can categorize risk levels based on the magnitude of the excess. If the threshold is approached by more than 10%, the risk is marked as high, indicating the need for significant attention.

[0069] For example, when analyzing a lifespan assessment report, relevant information is extracted from historical parameter adjustment records, such as voltage or current adjustment records, and compared with stability standards. For example, if a device's voltage is adjusted to 230 volts, while the standard range is 220 volts ±5 volts, and the matching degree falls below a preset threshold, the parameter analysis tool will generate an adjustment recommendation. This recommendation might include lowering the voltage to 225 volts and providing supporting evidence, such as historical data showing that similar adjustments resulted in more stable device operation.

[0070] For example, for recommended adjustments, maintenance frequency data is extracted from maintenance plan archives, such as if a piece of equipment is maintained on average every six months. This data is then compared against the priority marking criteria. If the data indicates that recent loads have been consistently high, meeting the high-priority criteria, the plan generation tool generates an optimized maintenance schedule and determines the execution order, such as prioritizing the equipment for inspection next week. This approach helps address potential issues promptly.

[0071] Specifically, when generating load profiles, a tiered storage approach can be used to archive data by time period to ensure efficient queries, while also implementing a verification mechanism to confirm integrity. When generating lifespan assessment reports, multi-dimensional indicators, such as operating temperature or power fluctuations, can be incorporated to comprehensively assess risk levels. When formulating adjustment recommendations, historical case studies and current operating status can be combined to provide a variety of optional parameter adjustments. When optimizing maintenance schedules, time windows can be flexibly adjusted based on workshop production plans to ensure business continuity.

[0072] In some embodiments, After adjusting the operating parameters of the target electrical equipment according to the real-time power distribution value and the performance index, the method further includes: Obtain historical operation records related to the equipment life from pre-established storage archives, classify and organize the records, generate a structured life assessment archive through a data integration tool, and determine the integrity of the archive. Based on the life assessment archive, obtain energy consumption data related to high-risk equipment from the operation monitoring log, compare the data with the preset threshold range, and if it exceeds the threshold range, generate an energy consumption anomaly report through the data recording tool to determine the risk level in the report. Using the energy consumption anomaly report, obtain adjustment information related to the power monitoring frequency from the historical records of parameter adjustments, match the information with business needs, and if the degree of match is lower than the preset threshold, generate a frequency adjustment plan through the parameter analysis tool to obtain the applicable basis of the plan. Using the frequency adjustment plan, obtain planning data related to power distribution from the strategy planning archive, compare the data with the conditions of high-risk equipment, and if the priority conditions are met, generate the allocation strategy for the next cycle through the plan generation tool to determine the execution order of the strategy.

[0073] For example, in an industrial power management scenario, when obtaining historical operating records related to equipment life from pre-established storage archives, data integration tools can be used to categorize and organize these records by equipment type and operating age, creating a structured life assessment archive. Archive integrity can be verified by checking the data loss rate. If the historical record loss rate for a particular piece of equipment is less than 0.2%, it is considered complete, providing a reliable foundation for subsequent analysis.

[0074] For example, when extracting energy consumption data for high-risk equipment from operational monitoring logs based on lifespan assessment records, one can focus on the equipment's peak power consumption and duration. For example, if a device's average daily energy consumption is 500 kWh, and the preset threshold is 450 kWh, exceeding this range will generate an energy consumption anomaly report through the data logging tool. The report can then categorize the risk level based on the magnitude of the excess. For example, an excess of more than 10% will be marked as high risk and require priority action.

[0075] For example, when analyzing parameter adjustment history using energy consumption anomaly reports, data related to power monitoring frequency can be extracted. For example, if a device's current monitoring frequency is hourly, while business demand requires monitoring every 30 minutes during peak hours, and the match falls below a preset threshold, the parameter analysis tool will generate a frequency adjustment plan, recommending increasing the monitoring frequency to every 30 minutes. This can be based on historical data showing similar energy consumption anomalies that were promptly detected after adjustments, ensuring the plan's rationale.

[0076] For example, when extracting power allocation data from strategic planning archives using frequency adjustment plans, the power usage priorities of various devices within the workshop can be monitored. If a high-risk device meets the priority criteria, such as if its operation significantly impacts core production processes, the plan generation tool generates an allocation strategy for the next cycle and determines the execution order. For example, this strategy prioritizes stable power resources for this device to ensure uninterrupted operation.

[0077] For example, in an industrial power management scenario, when obtaining historical operating records related to equipment life from pre-established storage archives, data integration tools can be used to categorize and organize these records by equipment type and operating age, creating a structured life assessment archive. Archive integrity can be verified by checking the data loss rate. If the historical record loss rate for a particular piece of equipment is less than 0.2%, it is considered complete, providing a reliable foundation for subsequent analysis.

[0078] For example, when extracting energy consumption data for high-risk equipment from operational monitoring logs based on lifespan assessment records, one can focus on the equipment's peak power consumption and duration. For example, if a device's average daily energy consumption is 500 kWh, and the preset threshold is 450 kWh, exceeding this range will generate an energy consumption anomaly report through the data logging tool. The report can then categorize the risk level based on the magnitude of the excess. For example, an excess of more than 10% will be marked as high risk and require priority action.

[0079] For example, when analyzing parameter adjustment history using energy consumption anomaly reports, data related to power monitoring frequency can be extracted. For example, if a device's current monitoring frequency is hourly, while business demand requires monitoring every 30 minutes during peak hours, and the match falls below a preset threshold, the parameter analysis tool will generate a frequency adjustment plan, recommending increasing the monitoring frequency to every 30 minutes. This can be based on historical data showing similar energy consumption anomalies that were promptly detected after adjustments, ensuring the plan's rationale.

[0080] For example, when extracting power allocation data from strategic planning archives using frequency adjustment plans, the power usage priorities of various devices within the workshop can be monitored. If a high-risk device meets the priority criteria, such as if its operation significantly impacts core production processes, the plan generation tool generates an allocation strategy for the next cycle and determines the execution order. For example, this strategy prioritizes stable power resources for this device to ensure uninterrupted operation.

[0081] This application also discloses a data center power monitoring system based on remote management, such as Figure 2 Shown, including: A first determining module 21 is configured to determine a predicted power consumption value of a target electrical device within a real-time target time period based on the historical power consumption data of the target electrical device and the real-time power consumption data; The second determining module 22 is configured to allocate the total power supply using a linear programming algorithm in combination with the power consumption forecast value, and determine a real-time power allocation value for the target power-consuming device; The adjustment module 23 is configured to obtain the performance index of the target electrical device and adjust the operating parameters of the target electrical device according to the real-time power distribution value and the performance index, so that the operating parameters meet the operating requirements of the target electrical device.

[0082] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0083] Optionally, the physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a Wi-Fi module, and the like. The user interface may include a display screen and an input unit such as a keyboard. Optional user interfaces may also include a USB interface and a card reader interface. Optionally, the network interface may include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0084] Those skilled in the art will understand that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0085] Based on the above Figure 1 The method shown, the embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the method corresponding to any embodiment is implemented. The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device and supports the operation of information processing programs and other software and / or programs. The network communication module is used to realize communication between the components inside the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0086] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform, or by hardware. By applying the solution of this embodiment, compared with the current existing technology, this embodiment discloses a data center power monitoring method based on remote management, which determines the power consumption forecast value of the power consumption equipment within the real-time target time period based on the historical power consumption data and real-time power consumption data of the target power consumption equipment; adopts a linear programming algorithm, combined with the power consumption forecast value, to allocate the total power supply, and determine the real-time power allocation value for the target power consumption equipment; obtains the performance index of the target power consumption equipment, and adjusts the operating parameters of the target power consumption equipment according to the real-time power allocation value and the performance index, so that the operating parameters meet the operating requirements of the target power consumption equipment. The present application can monitor the power consumption data of the target power consumption equipment, and predict the power consumption forecast value in the future time based on the historical power consumption data, and allocate power to the total power supply based on the power consumption forecast value, so that the operating parameters of each target power consumption equipment meet the operating parameters, thereby improving the overall operating efficiency of the power consumption equipment.

[0087] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a..." do not exclude the presence of other identical elements in the process, method, article or device that includes the elements.

[0088] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand and implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments described herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

Claims

1. A data center power monitoring method based on remote management, characterized in that: include: Determining a predicted power consumption value of a target power-consuming device within a real-time target time period based on historical power consumption data and real-time power consumption data of the target power-consuming device includes: Obtain real-time power consumption data of power-consuming equipment groups; Determine a target electrical device in the electrical device group by using an energy consumption analysis model, wherein the target electrical device is a high-consumption device or a low-consumption device in the electrical device group; Using a linear programming algorithm, combined with the power consumption forecast value, to allocate the total power supply, and determine the real-time power allocation value for the target power-consuming device; Acquire a performance indicator of the target electrical device, and adjust an operating parameter of the target electrical device according to the real-time power allocation value and the performance indicator, wherein the operating parameter satisfies an operating requirement of the target electrical device; Updating the load demand and actual load in the historical power consumption data; Determine the lifespan trend of the target electrical equipment based on the updated historical electricity consumption data.

2. The method according to claim 1, characterized in that The adjusting the operating parameters of the target electrical equipment according to the real-time power distribution value and the performance indicator includes: adjusting the operating parameters of the target electrical equipment according to the real-time power distribution value to obtain the operating parameters of the target electrical equipment; determining whether the operating parameters meet the performance indicators; If the operating parameters do not meet the performance indicators, adjust the operating parameters to operating parameters, and the operating parameters meet the performance indicators.

3. The method according to claim 1, characterized in that After adjusting the operating parameters of the target electrical device according to the real-time power distribution value and the performance indicator, the method further includes: Determining the load demand of the target electrical equipment; Determining an actual load of the target electrical device based on the operating parameters; Determine the equipment efficiency distribution of the target electrical equipment according to the load demand and the actual load.

4. The method according to claim 1, wherein After determining the lifespan trend of the target electrical equipment, the method further includes: determining a service executed in at least one of the target electric devices; A maintenance period for the operating parameters is determined based on the business, the lifespan trend, and the operating parameters.

5. The method according to claim 4, characterized in that After adjusting the operating parameters of the target electrical device according to the real-time power distribution value and the performance indicator, the method further includes: The model parameters of the energy consumption analysis model are updated according to the life trend and the maintenance cycle.

6. A data center power monitoring system based on remote management, characterized in that: include: A first determining module is configured to determine a predicted power consumption value of a target power-consuming device within a real-time target time period based on historical power consumption data of the target power-consuming device and the real-time power consumption data; A second determining module is configured to allocate the total power supply using a linear programming algorithm in combination with the power consumption forecast value, and determine a real-time power allocation value for the target power-consuming device; The adjustment module is used to obtain the performance index of the target electrical equipment and adjust the operating parameters of the target electrical equipment according to the real-time power distribution value and the performance index, so that the operating parameters meet the operating requirements of the target electrical equipment.

7. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

Images