A data center intelligent power distribution system capacitance dynamic monitoring and early warning method

Abstract

The application discloses a kind of data center intelligent power distribution system electric capacity dynamic monitoring and early warning method, it is related to electric capacity dynamic monitoring technical field, including using current decomposition method to the power load of data center power supply system is hierarchized decomposition, obtain current, pre-increased current and charging current;Using dynamic aggregation calculation method to the current data after decomposition is gradually summarized, obtain the expected current, maximum current and expected maximum current of each level;Using load rate calculation method to the current data after aggregation is normalized, obtain the current load rate, expected current load rate, maximum current load rate and expected maximum current load rate of each level;Using three-level early warning threshold method to the load rate obtained by calculation is threshold comparison, and according to preset light load, heavy load, overload threshold triggers corresponding level capacity early warning signal;Using scene adaptation method to early warning signal is classified processing.

Description

Technical Field

[0001] This invention relates to the field of dynamic power capacity monitoring technology, and in particular to a method for dynamic monitoring and early warning of power capacity in a data center intelligent power distribution system. Background Technology

[0002] Dynamic capacity monitoring technology is a technique used to monitor and manage the power load of data center power distribution systems in real time. It continuously and accurately measures and analyzes the power consumption of equipment at various levels within the data center to predict future power demands and adjust power distribution strategies based on this information to ensure stable system operation.

[0003] In the field of dynamic power capacity monitoring, the early warning of existing data center power supply and distribution systems is mainly concentrated at the equipment level, lacking comprehensive early warning capabilities at the system level. Furthermore, traditional early warning mechanisms often only focus on the current power load situation, ignoring the potential increase in load demand in the future. At the same time, traditional early warning mechanisms are usually based on real-time data for evaluation, ignoring changes over time. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for dynamic monitoring and early warning of power consumption capacity in a smart power distribution system for data centers. This addresses the problem that existing power distribution systems for data centers mainly focus on the equipment level for early warning, lacking comprehensive early warning capabilities at the system level. Furthermore, traditional early warning mechanisms often only focus on the current power load situation, neglecting the issue of potential future increases in load demand.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for dynamic monitoring and early warning of power consumption capacity in a data center intelligent power distribution system, comprising:

[0008] The power load of the data center power supply and distribution system is hierarchically decomposed using the current decomposition method to obtain the current current, the pre-increase current, and the charging current.

[0009] The decomposed current data is summarized level by level using a dynamic aggregation calculation method to obtain the expected current, maximum current and expected maximum current at each level.

[0010] The aggregated current data is normalized using a load factor calculation method to obtain the current load factor, expected current load factor, maximum current load factor, and expected maximum current load factor for each level.

[0011] A three-level early warning threshold method is used to compare the calculated load rate with a threshold, and the corresponding level of capacity early warning signal is triggered according to the preset light load, heavy load and overload thresholds.

[0012] The warning signals are classified and processed using a scenario adaptation method, matching them with long-term planning, annual project sets, or individual project scenarios, and outputting differentiated warning solutions for different system levels.

[0013] As a preferred embodiment of the data center intelligent power distribution system power capacity dynamic monitoring and early warning method of the present invention, the method of using current decomposition to hierarchically decompose the power load of the data center power distribution system to obtain the current current, the pre-increase current and the charging current, specifically includes the following steps:

[0014] The design current is determined by analyzing the design documents for each device or system level.

[0015] The current value under normal operating conditions at each level is measured using a real-time monitoring device to obtain the current.

[0016] Calculate the corresponding pre-increase current based on the expected increase in equipment load over a future period of time.

[0017] For devices that include battery charging capabilities, their charging current is calculated separately.

[0018] As a preferred embodiment of the data center intelligent power distribution system power capacity dynamic monitoring and early warning method of the present invention, the step of using a dynamic aggregation calculation method to summarize the decomposed current data level by level to obtain the expected current, maximum current and expected maximum current at each level is as follows:

[0019] A hierarchical structure diagram is used to analyze the hierarchical structure of the data center power supply and distribution system to determine the connection relationships of equipment at each level;

[0020] Based on the hierarchical structure, the expected current, maximum current, and expected maximum current of each level are calculated layer by layer from the input port upwards using the current transfer rule.

[0021] As a preferred embodiment of the data center intelligent power distribution system power capacity dynamic monitoring and early warning method of the present invention, the method of normalizing the aggregated current data by using a load rate calculation method to obtain the current current load rate, expected current load rate, maximum current load rate and expected maximum current load rate of each level is as follows:

[0022] Based on the measured current and design current, the ratio of the two is calculated to measure the long-term stable current load rate of the equipment or material during normal operation, thus obtaining the current current load rate at each level. ;

[0023] By using the expected current and the known design current, the ratio of the two is calculated to assess the long-term stable current load factor during normal operation at a future expected point in time, thus obtaining the expected current load factor for each level. ;

[0024] Based on the maximum current and the design current, the current load factor considering battery charging is determined by calculating the ratio of the two, thus obtaining the maximum current load factor for each level. ;

[0025] Based on the expected maximum current and the design current, the ratio of the two is calculated to assess the current load rate at a future point in time, taking into account battery charging, thus obtaining the expected maximum current load rate for each level. ;

[0026] After calculating the current current load factor, expected current load factor, maximum current load factor, and expected maximum current load factor for all levels, the data is compiled into a load factor summary table.

[0027] As a preferred embodiment of the data center intelligent power distribution system power capacity dynamic monitoring and early warning method of the present invention, the method of using a three-level early warning threshold method to compare the calculated load rate with a threshold, and triggering the corresponding level of capacity early warning signal according to the preset light load, heavy load and overload thresholds, specifically includes the following steps:

[0028] Set clear threshold values ​​for light load, heavy load, and overload for each level;

[0029] The threshold value is determined based on historical data, equipment specifications, and operating experience.

[0030] For each level, the calculated current current load rate is compared with preset light load, heavy load and overload thresholds;

[0031] If the current load factor is less than the light load threshold, the system is in a light load state.

[0032] If the load falls between the light and heavy load thresholds, it is considered a heavy load condition.

[0033] If the overload threshold is exceeded, an overload warning will be triggered.

[0034] Using the same method, the expected current load rate is compared with preset light load, heavy load and overload thresholds to predict the warning status at a future point in time.

[0035] The maximum current load rate is compared with preset light load, heavy load and overload thresholds to evaluate the system's warning status considering battery charging.

[0036] The expected maximum current load rate is compared with preset light load, heavy load and overload thresholds to predict the system's warning status at a future point in time, taking into account battery charging conditions.

[0037] After completing the warning status assessment at all the above levels, the warning signal generator is used to summarize the warning status at each level and form a comprehensive warning report.

[0038] As a preferred embodiment of the data center intelligent power distribution system power capacity dynamic monitoring and early warning method of the present invention, the method of classifying and processing the early warning signal using a scenario adaptation method, matching it with long-term planning, annual project sets or single project scenarios, and outputting differentiated early warning schemes for different system levels, specifically includes the following steps:

[0039] Based on an assessment of the data center's future power load demand, determine the specific scenarios applicable to the current situation;

[0040] Based on the defined scenario, collect the corresponding basic data;

[0041] The basic data includes building information, equipment type, number of server racks, and power.

[0042] The obtained warning signals are compared with three preset scenarios using a scene matching algorithm to obtain the most suitable warning scheme.

[0043] Based on the matching results, customize specific early warning strategies for each scenario;

[0044] The early warning strategy includes a more detailed long-term capacity expansion plan in long-term planning scenarios, and a focus on short-term emergency measures in single project scenarios.

[0045] The warning report generator is used to summarize the warning plans for different scenarios at each level and generate a comprehensive warning report.

[0046] As a preferred embodiment of the data center intelligent power distribution system power capacity dynamic monitoring and early warning method of the present invention, wherein: the step of determining the specific scenario for the current situation based on the assessment of the future power load demand of the data center specifically includes:

[0047] When the predicted demand is an increase in electricity load over a period of more than three years, the applicable scenario is long-term planning.

[0048] If the electricity load demand increases within one to three years, it falls under the annual project set;

[0049] If the power supply requirement is for a single project, it is classified as a single project scenario.

[0050] As a preferred embodiment of the data center intelligent power distribution system power capacity dynamic monitoring and early warning method of the present invention, wherein: the step of collecting corresponding basic data based on a defined scenario specifically includes:

[0051] For long-term planning scenarios, it is necessary to collect building information, equipment types, number of server racks, and expected power consumption of the newly added server racks within three years.

[0052] For the annual project portfolio, in addition to the above information, the specific parameters of each installed device must also be recorded in detail;

[0053] In a single project scenario, further details are needed, including the number of devices, the number of terminals occupied, and their specifications.

[0054] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the method for dynamic monitoring and early warning of power consumption capacity of a data center intelligent power distribution system as described in the first aspect of the present invention.

[0055] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for dynamic monitoring and early warning of power consumption capacity of a data center intelligent power distribution system as described in the first aspect of the present invention.

[0056] The beneficial effects of this invention are as follows: By employing a dynamic aggregation calculation method to summarize the decomposed current data level by level, the expected current, maximum current, and expected maximum current of each level are obtained. By analyzing the hierarchical structure of the data center power supply and distribution system using a hierarchical structure diagram, a clear understanding of the entire system structure is achieved. This lays the foundation for the application of the current transfer law, achieving the effect of simplifying complexity. Based on the hierarchical structure, the expected current, maximum current, and expected maximum current of each level are calculated layer by layer from the input port upwards using the current transfer law. This achieves detailed calculation of the load conditions of each level of the system, provides specific numerical support, and achieves the goal of refined management. By using the expected current and the known design current, the ratio of the two is calculated to evaluate the long-term stable current load rate during normal operation at a future expected time point, realizing the prediction of future load conditions. This can effectively help with advance planning and achieve the purpose of preventive maintenance. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a flowchart of the dynamic monitoring and early warning method for power consumption capacity of the intelligent power distribution system in the data center in Example 1. Detailed Implementation

[0059] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0060] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0061] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0062] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for dynamic monitoring and early warning of power consumption capacity in a data center intelligent power distribution system, including the following steps:

[0063] S1. The power load of the data center power supply and distribution system is hierarchically decomposed using the current decomposition method to obtain the current current, the pre-increase current and the charging current.

[0064] Furthermore, the design current is determined for each device or system level by analyzing the design documents.

[0065] The current value under normal operating conditions at each level is measured using a real-time monitoring device to obtain the current.

[0066] Based on the expected increase in equipment load over a future period, the corresponding pre-increase current is calculated, expressed as:

[0067] ;

[0068] in, For the first The power of the newly added equipment Its operating voltage, It is a correction factor that takes into account the time decay effect. The time decay coefficient, For time;

[0069] For devices that include battery charging functionality, the charging current is calculated separately, and the expression is:

[0070] ;

[0071] in, It's the charging rate. Battery capacity;

[0072] It should be noted that by combining design document analysis with real-time monitoring devices, not only is the accuracy and timeliness of the data ensured, but the actual operating status of the data center can also be dynamically reflected. This method effectively solves the problems of lag and inaccuracy in traditional static evaluation methods, allowing the method to be adjusted and optimized according to the actual operating conditions, thus improving the flexibility and adaptability of the method.

[0073] S2. The decomposed current data is summarized level by level using a dynamic aggregation calculation method to obtain the expected current, maximum current and expected maximum current at each level.

[0074] Furthermore, a hierarchical structure diagram is used to analyze the hierarchical structure of the data center power supply and distribution system to determine the connection relationships of equipment at each level;

[0075] Based on the hierarchical structure, the expected current, maximum current, and expected maximum current of each level are calculated layer by layer from the input port upwards using the current transfer rule. The expression is as follows:

[0076]

[0077]

[0078] ;

[0079] Summarizing the above calculation results, the expected current can be obtained. Maximum current and expected maximum current ;

[0080] It should be noted that by using hierarchical structure diagram analysis and current transfer rules, not only can the connection relationship between equipment at each level be clearly displayed, but the current demand of each level under different conditions can also be accurately calculated. This method overcomes the previous problem of difficulty in accurately assessing the load at each level in complex power systems, providing solid data support for subsequent early warning and helping to achieve more refined management.

[0081] S3. The aggregated current data is normalized using the load factor calculation method to obtain the current load factor, expected current load factor, maximum current load factor and expected maximum current load factor for each level.

[0082] Furthermore, based on the design current and the measured current, the ratio of the two is calculated to measure the long-term stable current load rate of the equipment or material during normal operation, thus obtaining the current current load rate at each level. The expression is:

[0083] ;

[0084] in, For the current, Design current;

[0085] By using the expected current and the known design current, the ratio of the two is calculated to assess the long-term stable current load factor during normal operation at a future expected point in time, thus obtaining the expected current load factor for each level. The expression is:

[0086] ;

[0087] in, This is the expected current;

[0088] Based on the maximum current and the design current, the current load factor considering battery charging is determined by calculating the ratio of the two, thus obtaining the maximum current load factor for each level. ;

[0089] Based on the expected maximum current and the design current, the ratio of the two is calculated to assess the current load rate at a future point in time, taking into account battery charging, thus obtaining the expected maximum current load rate for each level. ;

[0090] After calculating the current current load rate, expected current load rate, maximum current load rate, and expected maximum current load rate for all levels, organize the data into a load rate summary table.

[0091] It should be noted that determining the load rate by calculating the ratio of design current to measured current can intuitively reflect the load status of each level under different conditions. This method not only simplifies the data analysis process but also helps managers quickly understand current and future load trends, thereby making reasonable resource allocation decisions and greatly improving management efficiency and response speed.

[0092] S4. The calculated load rate is compared with the threshold using a three-level early warning threshold method, and the corresponding level of capacity early warning signal is triggered according to the preset light load, heavy load and overload thresholds.

[0093] Furthermore, clearly defined threshold values ​​for light load, heavy load, and overload are set for each level;

[0094] Threshold values ​​are determined based on historical data, equipment specifications, and operational experience;

[0095] For each level, the calculated current current load rate is compared with preset light load, heavy load and overload thresholds;

[0096] If the current load factor is less than the light load threshold, the system is in a light load state.

[0097] If the load falls between the light and heavy load thresholds, it is considered a heavy load condition.

[0098] If the overload threshold is exceeded, an overload warning will be triggered.

[0099] Using the same method, the expected current load rate is compared with preset light load, heavy load and overload thresholds to predict the warning status at a future point in time.

[0100] The maximum current load rate is compared with preset light load, heavy load and overload thresholds to evaluate the system's warning status considering battery charging.

[0101] The expected maximum current load rate is compared with preset light load, heavy load and overload thresholds to predict the system's warning status at a future point in time, taking into account battery charging conditions.

[0102] After completing the warning status judgment at all the above levels, the warning signal generator is used to summarize the warning status at each level to form a comprehensive warning report.

[0103] It should be noted that by setting clear thresholds for light load, heavy load, and overload, and generating early warning signals based on these thresholds, data centers can take corresponding measures under different load conditions to avoid failures or downtime caused by overload. The early warning mechanism can not only detect potential risks in advance, but also provide customized solutions according to different scenarios, significantly improving the stability and reliability of the method.

[0104] S5. Use scenario adaptation to classify and process early warning signals, match them with long-term planning, annual project sets or single project scenarios, and output differentiated early warning solutions for different system levels.

[0105] Furthermore, based on an assessment of the data center's future power load requirements, specific scenarios for the current situation can be determined;

[0106] When the predicted demand is an increase in electricity load over a period of more than three years, the applicable scenario is long-term planning.

[0107] If the electricity load demand increases within one to three years, it falls under the annual project set;

[0108] If the power supply requirement is for a single project, it is classified as a single project scenario.

[0109] Based on the defined scenario, collect the corresponding basic data;

[0110] For long-term planning scenarios, it is necessary to collect building information, equipment types, number of server racks, and expected power consumption of the newly added server racks within three years.

[0111] For the annual project portfolio, in addition to the above information, the specific parameters of each installed device must also be recorded in detail;

[0112] In a single project scenario, further details are needed, including the equipment list, the number of terminals occupied, and specifications.

[0113] Basic data includes building information, equipment type, number of server racks, and power consumption;

[0114] The obtained warning signals are compared with three preset scenarios using a scene matching algorithm to obtain the most suitable warning scheme.

[0115] Based on the matching results, customize specific early warning strategies for each scenario;

[0116] Early warning strategies include more detailed long-term capacity expansion plans in long-term planning scenarios, and short-term emergency measures in single-project scenarios.

[0117] The warning report generator is used to summarize the warning plans for different scenarios at each level and generate a comprehensive warning report.

[0118] It should be noted that by matching early warning signals with specific application scenarios, differentiated early warning strategies can be formulated according to actual needs. This approach not only considers the long-term development needs of data centers but also takes into account the specific challenges that may be encountered in the short term, making the early warning scheme more in line with the actual situation. This helps to improve the rationality and effectiveness of resource allocation. At the same time, it also provides guidance for future expansion and enhances the scalability and adaptability of the system.

[0119] This embodiment also provides a computer device applicable to the dynamic monitoring and early warning method for power capacity of intelligent power distribution system in data center, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the dynamic monitoring and early warning method for power capacity of intelligent power distribution system in data center as proposed in the above embodiment.

[0120] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0121] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the method for dynamic monitoring and early warning of power consumption capacity of a data center intelligent power distribution system as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0122] In summary, this invention employs a dynamic aggregation calculation method to summarize the decomposed current data level by level, obtaining the expected current, maximum current, and expected maximum current for each level. By analyzing the hierarchical structure of the data center power supply and distribution system using a hierarchical structure diagram, a clear understanding of the entire system structure is achieved. This lays the foundation for the application of the current transfer law, simplifying complexity. Based on the hierarchical structure, the current transfer law is used to calculate the expected current, maximum current, and expected maximum current for each level from the input port upwards, enabling detailed calculations of the load conditions at each level of the system. This provides specific numerical support and achieves the goal of refined management. By using the expected current and the known design current, the ratio of the two is calculated to assess the long-term stable current load rate during normal operation at a future expected time point, enabling prediction of future load conditions. This effectively helps with advance planning and achieves the purpose of preventative maintenance.

[0123] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for dynamic monitoring and early warning of power consumption capacity in a data center intelligent power distribution system, characterized in that: include: The power load of the data center power supply and distribution system is hierarchically decomposed using the current decomposition method to obtain the current current, the pre-increase current, and the charging current. The decomposed current data is summarized level by level using a dynamic aggregation calculation method to obtain the expected current, maximum current and expected maximum current at each level. The aggregated current data is normalized using a load factor calculation method to obtain the current load factor, expected current load factor, maximum current load factor, and expected maximum current load factor for each level. A three-level early warning threshold method is used to compare the calculated load rate with a threshold, and the corresponding level of capacity early warning signal is triggered according to the preset light load, heavy load and overload thresholds. The warning signals are classified and processed using a scenario adaptation method, matching them with long-term planning, annual project sets, or individual project scenarios, and outputting differentiated warning solutions for different system levels.

2. The method for dynamic monitoring and early warning of power consumption capacity in a data center intelligent power distribution system as described in claim 1, characterized in that: The method of using current decomposition to hierarchically decompose the power load of the data center power supply and distribution system to obtain the current current, the pre-increase current, and the charging current involves the following steps: The design current is determined by analyzing the design documents for each device or system level. The current value under normal operating conditions at each level is measured using a real-time monitoring device to obtain the current. Calculate the corresponding pre-increase current based on the expected increase in equipment load over a future period of time. For devices that include battery charging capabilities, their charging current is calculated separately.

3. The method for dynamic monitoring and early warning of power consumption capacity in a data center intelligent power distribution system as described in claim 2, characterized in that: The dynamic aggregation calculation method is used to summarize the decomposed current data level by level to obtain the expected current, maximum current, and expected maximum current at each level. The specific steps are as follows: A hierarchical structure diagram is used to analyze the hierarchical structure of the data center power supply and distribution system to determine the connection relationships of equipment at each level; Based on the hierarchical structure, the expected current, maximum current, and expected maximum current of each level are calculated layer by layer from the input port upwards using the current transfer rule.

4. The method for dynamic monitoring and early warning of power consumption capacity in a data center intelligent power distribution system as described in claim 3, characterized in that: The method of normalizing the aggregated current data using the load factor calculation method yields the current load factor, expected current load factor, maximum current load factor, and expected maximum current load factor for each level. The specific steps are as follows: Based on the measured current and design current, the ratio of the two is calculated to measure the long-term stable current load rate of the equipment or material during normal operation, thus obtaining the current current load rate at each level. ; By using the expected current and the known design current, the ratio of the two is calculated to assess the long-term stable current load factor during normal operation at a future expected point in time, thus obtaining the expected current load factor for each level. ; Based on the maximum current and the design current, the current load factor considering battery charging is determined by calculating the ratio of the two, thus obtaining the maximum current load factor for each level. ; Based on the expected maximum current and the design current, the ratio of the two is calculated to assess the current load rate at a future point in time, taking into account battery charging, thus obtaining the expected maximum current load rate for each level. ; After calculating the current current load factor, expected current load factor, maximum current load factor, and expected maximum current load factor for all levels, the data is compiled into a load factor summary table.

5. The method for dynamic monitoring and early warning of power consumption capacity in a data center intelligent power distribution system as described in claim 4, characterized in that: The method employs a three-level early warning threshold to compare the calculated load rate against a threshold, and triggers corresponding level capacity early warning signals based on preset light load, heavy load, and overload thresholds. The specific steps are as follows: Set clear threshold values ​​for light load, heavy load, and overload for each level; The threshold value is determined based on historical data, equipment specifications, and operating experience. For each level, the calculated current current load rate is compared with preset light load, heavy load and overload thresholds; If the current load factor is less than the light load threshold, the system is in a light load state. If the load falls between the light and heavy load thresholds, it is considered a heavy load condition. If the overload threshold is exceeded, an overload warning will be triggered. Using the same method, the expected current load rate is compared with preset light load, heavy load and overload thresholds to predict the warning status at a future point in time. The maximum current load rate is compared with preset light load, heavy load and overload thresholds to evaluate the system's warning status considering battery charging. The expected maximum current load rate is compared with preset light load, heavy load and overload thresholds to predict the system's warning status at a future point in time, taking into account battery charging conditions. After completing the warning status assessment at all the above levels, the warning signal generator is used to summarize the warning status at each level to form a comprehensive warning report.

6. The method for dynamic monitoring and early warning of power consumption capacity in a data center intelligent power distribution system as described in claim 5, characterized in that: The method of classifying and processing early warning signals using scenario adaptation, matching them with long-term planning, annual project sets, or individual project scenarios, and outputting differentiated early warning solutions for different system levels, involves the following steps: Based on an assessment of the data center's future power load demand, determine the specific scenarios applicable to the current situation; Based on the defined scenario, collect the corresponding basic data; The basic data includes building information, equipment type, number of server racks, and power. The obtained warning signals are compared with three preset scenarios using a scene matching algorithm to obtain the most suitable warning scheme. Based on the matching results, customize specific early warning strategies for each scenario; The early warning strategy includes a more detailed long-term capacity expansion plan in long-term planning scenarios, and a focus on short-term emergency measures in single project scenarios. The warning report generator is used to summarize the warning plans for different scenarios at each level and generate a comprehensive warning report.

7. The method for dynamic monitoring and early warning of power consumption capacity in a data center intelligent power distribution system as described in claim 6, characterized in that: The specific scenario for the current situation is determined based on the assessment of the data center's future power load demand, specifically: When the predicted demand is an increase in electricity load over a period of more than three years, the applicable scenario is long-term planning. If the electricity load demand increases within one to three years, it falls under the annual project set; If the power supply requirement is for a single project, it is classified as a single project scenario.

8. The method for dynamic monitoring and early warning of power consumption capacity in a data center intelligent power distribution system as described in claim 7, characterized in that: The collection of relevant basic data based on a defined scenario specifically includes: For long-term planning scenarios, it is necessary to collect building information, equipment types, number of server racks, and expected power consumption of the newly added server racks within three years. For the annual project portfolio, in addition to the above information, the specific parameters of each installed device must also be recorded in detail; In a single project scenario, further details are needed, including the equipment list, the number of terminals occupied, and specifications.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the data center intelligent power distribution system power capacity dynamic monitoring and early warning method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the data center intelligent power distribution system power capacity dynamic monitoring and early warning method according to any one of claims 1 to 8.

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