An online hydrogen UPS intelligent control method and related equipment

Abstract

The present application discloses an online hydrogen energy UPS intelligent control method and related equipment, which relate to the field of intelligent control. The method includes: obtaining external environment information of a target online hydrogen energy UPS; obtaining internal operating environment information of a server corresponding to the target online hydrogen energy UPS as a backup power supply; obtaining external factor power outage risk information based on the external environment information; obtaining internal factor power outage risk information based on the internal operating environment information; determining comprehensive power outage risk information based on the external factor power outage risk information and the internal factor power outage risk information; determining a target control strategy based on the comprehensive power outage risk information; and controlling the target online hydrogen energy UPS based on the target control strategy.

Description

Technical Field

[0001] This specification relates to the field of intelligent control, and more specifically, the present application relates to an online hydrogen UPS intelligent control method and related equipment. Background Art

[0002] In existing hydrogen UPS (Uninterruptible Power Supply) applications, although the advantages of hydrogen fuel cells, such as environmental protection and low operating costs, have been utilized, their startup and control strategies often rely on more traditional methods. Typically, these systems are started only after detecting a failure in the main power supply, and use preset fixed parameters to manage power switching and energy distribution.

[0003] This approach has the following limitations:

[0004] 1. Response speed: Traditional hydrogen UPS systems may have delays when switching to backup power, especially when power risks are not predicted in advance.

[0005] 2. Energy efficiency: Fixed operating strategies may not be able to optimize the use of hydrogen fuel, especially under changing external environment and internal load conditions.

[0006] 3. Lack of forecasting: The lack of real-time monitoring and forecasting of external environmental changes (such as extreme weather or unstable power grid) and internal demand (such as server load fluctuations) makes it difficult to achieve refined management. Summary of the invention

[0007] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0008] In the first aspect, the present application proposes an online hydrogen UPS intelligent control method, the method comprising:

[0009] Obtain external environment information of the target online hydrogen energy UPS;

[0010] Obtaining the internal operating environment information of the server corresponding to the target online hydrogen energy UPS as a backup power supply;

[0011] Acquiring external factor power outage risk information based on the external environment information;

[0012] Acquiring internal factor power failure risk information based on the internal operating environment information;

[0013] Determine comprehensive power outage risk information based on the external factor power outage risk information and the internal factor power outage risk information;

[0014] Determining a target control strategy based on the comprehensive power outage risk information;

[0015] The target online hydrogen energy UPS is controlled based on the target control strategy.

[0016] In a feasible implementation manner, the external environmental information includes weather condition information, power grid status information and regional factor information, the internal environmental information includes server load information, system thermal management information and server aging information, and the target control strategy includes target operating power and emergency response cycle.

[0017] In a feasible implementation manner, obtaining the external factor power outage risk information based on the external environment information includes:

[0018] Fill missing values ​​and perform Z-score standardization operations on the weather condition information to obtain standardized weather condition information;

[0019] Performing missing value interpolation and minimum and maximum normalization operations on the power grid status information to obtain normalized power grid status information;

[0020] Performing a one-hot encoding operation on the regional factor information to obtain regional factor standardization information;

[0021] Extracting weather condition features from the weather condition standardized information based on a time series analysis method;

[0022] Extracting grid operation characteristics from the grid status standardized information based on graph theory;

[0023] Extracting regional factor features from the regional factor standardized information based on principal component analysis;

[0024] The external factor power outage risk information is obtained based on the weather condition characteristics, the power grid operation characteristics, the regional factor characteristics and the target random forest model.

[0025] In a feasible implementation manner, the obtaining the external factor power outage risk information according to the weather condition characteristics, the power grid operation characteristics, the regional factor characteristics and the target random forest model includes:

[0026] Performing a splicing operation on the weather condition characteristics, the power grid operation characteristics and the regional factor characteristics to determine the basic characteristics of the external environment;

[0027] In the case where at least two of the extracted weather condition features, the power grid operation features and the regional factor features exceed their corresponding feature thresholds, generating an external environment binary feature corresponding to the external environment based on the external environment features exceeding the feature thresholds;

[0028] The external environment basic features and the external environment binary features are input into a target random forest model to obtain the power outage risk information of the external factors.

[0029] In a feasible implementation manner, obtaining the internal factor power failure risk information based on the internal operating environment information includes:

[0030] Preprocessing the server load information using a sliding window averaging method, and extracting load peak features from the processed server load information;

[0031] Performing a normalization operation according to the system thermal management information, and extracting a temperature anomaly frequency feature and a temperature change rate feature of the normalized system thermal management information;

[0032] Determining an aging score feature and a maintenance and failure association feature based on the server aging information;

[0033] The load peak feature, temperature anomaly number feature, temperature change rate feature, aging score feature and maintenance and fault association feature are input into the target SVM classifier to obtain the internal factor power outage risk information.

[0034] In a feasible implementation manner, the above-mentioned determination of the comprehensive power outage risk information based on the above-mentioned external factor power outage risk information and the above-mentioned internal factor power outage risk information includes:

[0035] Obtaining the geographical location information, server type information and server backup information of the server corresponding to the above-mentioned target online hydrogen energy UPS as a backup power supply;

[0036] Determine first weight information corresponding to the external factor power outage risk information and second weight information corresponding to the internal factor power outage risk information based on the geographical location information, the server type information and the server backup information;

[0037] Comprehensive power outage risk information is determined based on the first weight information, the second weight information, the external factor power outage risk information, and the internal factor power outage risk information.

[0038] In a feasible implementation manner, the first weight information w is determined based on the following formula: 外部 and the second weight information w 内部 :

[0039] W外部 =α·S 地理

[0040] W 内部 =β·S 类型 +γ·S 备份

[0041] α, β, γ are the preset initial coefficients, S 地理 is the geographic location coefficient, S 类型 is the server type coefficient and S 备份 The server backup factor.

[0042] In a feasible implementation manner, the above-mentioned determination of the target control strategy based on the above-mentioned comprehensive power outage risk information includes:

[0043] When the above-mentioned comprehensive power outage risk information is low risk, the above-mentioned target control strategy is low operating power and low emergency response cycle; and / or,

[0044] When the above-mentioned comprehensive power outage risk information is medium risk, the above-mentioned target control strategy is medium operating power and medium emergency response cycle; and / or,

[0045] When the above-mentioned comprehensive power outage risk information is high risk, the above-mentioned target control strategy is high operating power and high emergency response cycle.

[0046] In the second aspect, the present application also proposes an online hydrogen UPS intelligent control device, comprising:

[0047] A first acquisition unit is used to acquire external environment information of a target online hydrogen energy UPS;

[0048] The second acquisition unit is used to acquire the internal operating environment information of the server corresponding to the target online hydrogen energy UPS as a backup power supply;

[0049] A third acquisition unit, configured to acquire external factor power outage risk information based on the external environment information;

[0050] A fourth acquisition unit, configured to acquire internal factor power failure risk information based on the internal operating environment information;

[0051] A first determining unit, configured to determine comprehensive power outage risk information based on the external factor power outage risk information and the internal factor power outage risk information;

[0052] A second determining unit, configured to determine a target control strategy based on the comprehensive power outage risk information;

[0053] A control unit is used to control the target online hydrogen energy UPS based on the target control strategy.

[0054] In a third aspect, the present application further proposes an online hydrogen energy UPS, comprising the online hydrogen energy UPS intelligent control device as described in the second aspect.

[0055] In summary, the online hydrogen UPS intelligent control method of the embodiment of the present application includes: obtaining the external environment information of the target online hydrogen UPS; obtaining the internal operating environment information of the server corresponding to the target online hydrogen UPS as a backup power supply; obtaining the external factor power outage risk information based on the external environment information; obtaining the internal factor power outage risk information based on the internal operating environment information; determining the comprehensive power outage risk information based on the external factor power outage risk information and the internal factor power outage risk information; determining the target control strategy based on the comprehensive power outage risk information; and controlling the target online hydrogen UPS based on the target control strategy. Compared with the traditional hydrogen UPS system, the method proposed in the embodiment of the present application can identify potential power outage risks in advance by continuously monitoring the external environment and internal operating status, thereby realizing early warning and early start, reducing delays and potential service interruptions caused by power switching. According to real-time risk assessment and operating environment data, the intelligent control strategy can dynamically adjust the use of hydrogen fuel and power output, and optimize energy efficiency and operating costs. The intelligent control method proposed in the present application allows the system to flexibly adjust according to changes in the external environment and fluctuations in internal demand, thereby improving the adaptability and reliability of the system. By optimizing the use of hydrogen fuel, the environmental impact is reduced while reducing energy consumption and operating costs.

[0056] The online hydrogen UPS intelligent control method proposed in this application, and other advantages, objectives and features of this application will be partially reflected through the following description, and will also be partially understood by technical personnel in this field through research and practice of this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0058] Figure 1 A schematic diagram of the process of an online hydrogen UPS intelligent control method provided in an embodiment of the present application;

[0059] Figure 2 A schematic diagram of the structure of a user profile generation system based on user behavior provided in an embodiment of the present application;

[0060] Figure 3 A structural schematic diagram of an online hydrogen UPS provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0062] See also Figure 1 , which is a flow chart of an online hydrogen UPS intelligent control method provided in an embodiment of the present application, which may specifically include:

[0063] S110, obtaining external environment information of the target online hydrogen energy UPS;

[0064] S120, obtaining the internal operating environment information of the server corresponding to the target online hydrogen energy UPS as a backup power supply;

[0065] S130, acquiring external factor power outage risk information based on the external environment information;

[0066] S140, obtaining internal factor power failure risk information based on the internal operating environment information;

[0067] S150, determining comprehensive power outage risk information based on the external factor power outage risk information and the internal factor power outage risk information;

[0068] S160, determining a target control strategy based on the comprehensive power outage risk information;

[0069] S170. Control the target online hydrogen energy UPS based on the target control strategy.

[0070] Exemplarily, the system first collects external environmental information about the target online hydrogen UPS. This information may include local weather conditions, grid conditions, and other relevant regional factors. This data provides preliminary input for subsequent risk assessment.

[0071] The system obtains the internal operating environment information of the server that the target online hydrogen UPS serves as a backup power supply. This includes the server's load information, system thermal management information, and server aging information, which helps evaluate the risk factors inside the server that may cause power outages.

[0072] Based on the external environment information collected in step S110, the power outage risk information of external factors is analyzed and obtained, as well as the prediction of extreme weather events, the analysis of power grid stability, and the impact of regional policy changes.

[0073] The server internal operating environment information collected in step S120 is analyzed to obtain power outage risk information of internal factors, including evaluating potential power outage risks from aspects such as server load peak, system thermal state abnormality, and device aging status.

[0074] After obtaining external and internal power outage risk information, this step forms a comprehensive power outage risk assessment by combining these two pieces of information. This comprehensive assessment will consider the impact of all relevant factors and provide decision support for the subsequent control strategy.

[0075] Based on the comprehensive power outage risk information determined in step S150, this step will determine a set of target control strategies, including adjusting the target operating power and emergency response cycle of the online hydrogen UPS to optimize its performance and reduce the impact of power outages.

[0076] According to the target control strategy set in S160, the operation of the target online hydrogen UPS is actually controlled. This may include adjusting the output power of the backup power supply, changing the charging strategy of the backup power supply, or adjusting the operation of the emergency response, etc.

[0077] In summary, compared with the traditional hydrogen UPS system, the method proposed in the embodiment of the present application can identify potential power outage risks in advance by continuously monitoring the external environment and internal operating status, thereby achieving early warning and early startup, reducing delays and potential service interruptions caused by power switching. Based on real-time risk assessment and operating environment data, the intelligent control strategy can dynamically adjust the use of hydrogen fuel and power output to optimize energy efficiency and operating costs. The intelligent control method proposed in this application allows the system to be flexibly adjusted according to changes in the external environment and fluctuations in internal demand, improving the adaptability and reliability of the system. By optimizing the use of hydrogen fuel, the environmental impact is reduced while reducing energy consumption and operating costs.

[0078] In some examples, the external environmental information includes weather condition information, power grid status information, and regional factor information, the internal environmental information includes server load information, system thermal management information, and server aging information, and the target control strategy includes target operating power and emergency response cycle.

[0079] Exemplarily, the external environment information of the target online hydrogen UPS is collected to evaluate the external factors that may affect the operation of the UPS. Specifically, it includes:

[0080] Weather Condition Information: Monitors temperature, humidity, wind speed, and other meteorological conditions, with particular attention to extreme weather events that could affect grid stability and equipment operation.

[0081] Grid status information: Analyze the stability of grid power supply, including grid load, frequency fluctuations and historical power outage data, to assess the potential impact of grid failures on the UPS.

[0082] Regional factor information: Consider region-specific policies, economic activities, and construction projects, which may indirectly affect electricity supply and demand.

[0083] Obtain the internal operating environment information of the server corresponding to the UPS as a backup power supply. The key points include:

[0084] Server load information: Real-time monitoring of the server's CPU usage, memory usage, and network traffic to identify the risk of power outages caused by excessive operating load.

[0085] System thermal management information: Monitors the temperature and cooling efficiency of the server room. High temperatures or insufficient cooling system efficiency may increase the risk of equipment failure and power outages.

[0086] Server aging information: records the service life and maintenance history of server hardware. Aging hardware is more likely to fail, thus affecting the normal power supply of the UPS.

[0087] Based on the collected external and internal environmental information, determine the appropriate target control strategy to optimize the response and operating efficiency of the hydrogen UPS. The specific control strategies include:

[0088] Target operating power: Adjusts the UPS output power based on real-time environmental information and system status to meet the actual needs of the server and maximize energy efficiency.

[0089] Emergency response cycle: Set and adjust the emergency response time period to ensure that the UPS can quickly and effectively provide necessary power support when power is suddenly interrupted.

[0090] Through this integrated intelligent control method, the target online hydrogen UPS can achieve efficient and reliable power management according to complex and changing environmental conditions and internal needs. This strategy not only enhances the adaptability and reliability of the system, but also optimizes energy use and reduces operating costs.

[0091] In some examples, obtaining external factor power outage risk information based on the external environment information includes:

[0092] Fill missing values ​​and perform Z-score standardization operations on the weather condition information to obtain standardized weather condition information;

[0093] Performing missing value interpolation and minimum and maximum normalization operations on the power grid status information to obtain normalized power grid status information;

[0094] Performing a one-hot encoding operation on the regional factor information to obtain regional factor standardization information;

[0095] Extracting weather condition features from the weather condition standardized information based on a time series analysis method;

[0096] Extracting grid operation characteristics from the grid status standardized information based on graph theory;

[0097] Extracting regional factor features from the regional factor standardized information based on principal component analysis;

[0098] The external factor power outage risk information is obtained based on the weather condition characteristics, the power grid operation characteristics, the regional factor characteristics and the target random forest model.

[0099] Exemplarily, missing values ​​are filled in the collected weather condition information to ensure data integrity. Then, a Z-score standardization operation is performed, which standardizes the data by calculating the standard deviation of the data point from the mean value, so that the processed weather condition information has a standardized distribution, which is convenient for subsequent analysis.

[0100] The missing values ​​in the power grid status information are interpolated to fill the data gaps. Then the minimum and maximum standardization methods are applied to adjust the power grid status information to the range of 0 to 1 in order to unify the scale of each data indicator.

[0101] Perform one-hot encoding on the regional factor information to convert categorical variables into numerical binary variables, which helps to better process and analyze this information in subsequent machine learning models.

[0102] Time series analysis is applied to analyze the standardized weather condition information to identify important weather characteristics that may affect power supply, such as temperature fluctuations and the frequency of extreme meteorological events.

[0103] Graph theory is used to analyze the structure and connectivity of the power grid, thereby identifying key nodes and potential vulnerable points in the grid. These features help predict possible failure points in the grid.

[0104] The one-hot encoded data of regional factors were processed by principal component analysis (PCA) to extract the main components, which reflect the most influential variables in the regional factors.

[0105] The extracted weather condition features, grid operation features, and regional factor features are combined and input into a pre-trained random forest model. The random forest model uses these features to predict and assess the risk of power outages that may be caused by external environmental factors. The model outputs a score for the risk of power outages caused by external factors, providing a scientific basis for subsequent decision-making.

[0106] In some examples, the step of obtaining the external factor power outage risk information based on the weather condition characteristics, the power grid operation characteristics, the regional factor characteristics, and the target random forest model includes:

[0107] Performing a splicing operation on the weather condition characteristics, the power grid operation characteristics and the regional factor characteristics to determine the basic characteristics of the external environment;

[0108] In the case where at least two of the extracted weather condition features, the power grid operation features and the regional factor features exceed their corresponding feature thresholds, generating an external environment binary feature corresponding to the external environment based on the external environment features exceeding the feature thresholds;

[0109] The external environment basic features and the external environment binary features are input into a target random forest model to obtain the power outage risk information of the external factors.

[0110] Exemplarily, the system first extracts features from various types of external environmental information collected. This includes features extracted from weather condition information (such as temperature fluctuations, frequency of extreme weather events, etc.), features extracted from power grid operation information (such as power grid load fluctuations, frequent power fluctuations, etc.), and features extracted from regional factor information (such as policy changes, regional construction activities, etc.). These features will then be spliced ​​into a unified feature vector, called the basic feature of the external environment, which integrates all key external environmental data and provides basic data for subsequent risk assessment.

[0111] Based on the extracted weather condition features, grid operation features, and regional factor features, specific logic rules are applied to identify high-risk situations. For example, if the regional factor features show that the maintenance plan in a certain area frequently exceeds the set threshold, and the weather condition features indicate the presence of extreme weather events such as typhoons or heavy rains, the combination of these conditions will be considered a high-risk state. For this situation, the system creates a binary feature (for example, named "high-risk state") with a value of 1 (indicating high risk) or 0 (indicating normal risk).

[0112] The spliced ​​external environment basic features and the external environment binary features generated by the condition combination are input into the target random forest model. The random forest model evaluates and predicts the risk of power outages caused by external factors by analyzing these features. The risk score or probability value output by the model will be used to decide whether further preventive or emergency measures are needed.

[0113] The embodiment of the present application not only effectively utilizes the data extracted from multiple sources of information, but also enhances the accuracy and reliability of risk prediction through conditional combination logic. This intelligent risk management strategy can help the online hydrogen UPS system better cope with potential power outages and ensure the continuous operation of key facilities.

[0114] In some examples, obtaining the internal factor power failure risk information based on the internal operating environment information includes:

[0115] Preprocessing the server load information using a sliding window averaging method, and extracting load peak features from the processed server load information;

[0116] Performing a normalization operation according to the system thermal management information, and extracting a temperature anomaly frequency feature and a temperature change rate feature of the normalized system thermal management information;

[0117] Determining an aging score feature and a maintenance and failure association feature based on the server aging information;

[0118] The load peak feature, temperature anomaly number feature, temperature change rate feature, aging score feature and maintenance and fault association feature are input into the target SVM classifier to obtain the internal factor power outage risk information.

[0119] Exemplarily, the server load information is preprocessed using a sliding window averaging method, which helps smooth short-term fluctuations by averaging server load data within a certain time window, thereby more accurately reflecting the server load trend.

[0120] The processed server load information is analyzed to extract the load peak feature. The load peak refers to the highest load value recorded in a specific time window. This feature can indicate when the server may face the risk of overload.

[0121] Normalize the system thermal management information and standardize the temperature data to a uniform scale (such as between 0 and 1) to eliminate the impact of different measurement equipment or standards.

[0122] The key features of the normalized system thermal management information are extracted, including the temperature anomaly times feature (recording the number of times the temperature exceeds the preset threshold) and the temperature change rate feature (measuring the speed or amplitude of the temperature change).

[0123] Based on the server's age, maintenance frequency, and failure history, an aging score is calculated. This score reflects the risk of server performance degradation or failure due to aging. The relationship between maintenance records and failure occurrence is analyzed, and features are extracted to evaluate the effectiveness of maintenance measures and the reliability of server components.

[0124] The extracted load peak features, temperature anomaly times, temperature change rate, aging score, and maintenance and fault association features are integrated and input into the target support vector machine (SVM) classifier. The SVM classifier analyzes these features to predict the power outage risk that may be caused by internal factors of the server. The risk assessment results output by the SVM classifier will be used to guide further risk management measures to ensure that the system can prepare in advance and effectively respond to potential power outage events.

[0125] In some examples, determining the comprehensive power outage risk information based on the external factor power outage risk information and the internal factor power outage risk information includes:

[0126] Obtaining the geographical location information, server type information and server backup information of the server corresponding to the above-mentioned target online hydrogen energy UPS as a backup power supply;

[0127] Determine first weight information corresponding to the external factor power outage risk information and second weight information corresponding to the internal factor power outage risk information based on the geographical location information, the server type information and the server backup information;

[0128] Comprehensive power outage risk information is determined based on the first weight information, the second weight information, the external factor power outage risk information, and the internal factor power outage risk information.

[0129] For example, first, the system needs to collect key information about the server corresponding to the target online hydrogen UPS as a backup power supply, including:

[0130] Geographic location information involves the geographical area where the server is located, the stability of the local power grid, and whether it is often affected by natural disasters (such as earthquakes, floods, etc.).

[0131] Server type information, which details the server's specifications, performance, and technology type to assess its reliability and fault tolerance in specific situations.

[0132] Server backup information, analyze the configuration of the backup system, including the frequency, integrity and recovery capabilities of the backup.

[0133] Based on the information obtained, two types of weight information are determined:

[0134] The first weight information w 外部 It is a weight based on the geographical location information, reflecting the impact of external environmental factors (such as power grid instability, natural disasters, etc.) on the risk of power outages.

[0135] The second weight information w 内部 It is based on the weight of server type information and server backup information, reflecting the impact of internal factors (such as hardware reliability, robustness of backup system, etc.) on power outage risk.

[0136] External factors power outage risk information R 外部 It is a risk assessment based on a comprehensive analysis of external environmental factors such as weather conditions, power grid conditions and regional factors.

[0137] Internal factor power outage risk information R 内部 It is a risk assessment based on the server's operating environment information, such as the server's load condition, thermal management efficiency, hardware aging and other internal factors.

[0138] Combining the above information, the following formula can be used to calculate the comprehensive power outage risk information:

[0139] R 综合 =w 外部 ×R 外部 +w 内部 ×R 内部

[0140] Among them, R 综合 Represents the overall power outage risk score based on a comprehensive consideration of external and internal risk factors.

[0141] The method provided in this embodiment can be used to more accurately evaluate and predict risk factors that may affect the performance of the UPS system, thereby formulating more effective backup power management and emergency response strategies. The advantage of this method is that it allows for detailed analysis and weighing of various risk factors, ensuring that the UPS system can reliably provide support at critical moments.

[0142] In some examples, the first weight information w is determined based on the following formula: 外部 and the second weight information w 内部 :

[0143] W 外部 =α·S 地理

[0144] W 内部 =β·S 类型 +γ·S 备份

[0145] α, β, γ are pre-set initial coefficients, Sgeo is the geographic location coefficient, Stype is the server type coefficient and Sbackup is the server backup coefficient.

[0146] Exemplarily, the initial coefficients (α, β, γ) represent the relative importance of different factors in the weight calculation. For example, α may represent the importance of geographical location, β represents the importance of server type, and γ represents the importance of server backup configuration.

[0147] Geographical location coefficient S 地理 It is used to quantify the contribution of geographic location to the risk of power outage. High-risk areas (such as disaster-prone areas) will have higher S 地理 .

[0148] Server type factor S 类型 Reflects the impact of different types of servers on system stability due to their design and functional differences.

[0149] Server backup factor S 备份 Evaluate the performance and reliability of the backup system. High-quality and frequent backup operations will have higher S 备份 value.

[0150] Based on the above definitions of coefficients and weights, we can calculate the first weight information w 外部 and the second weight information w 内部 .

[0151] The calculation formula is as follows:

[0152] The first weight information w 外部 =α×S 地理 The impact of geographical location is directly amplified or reduced through the α coefficient, reflecting its weight in external risk assessment.

[0153] The second weight information w 内部 =β×S 类型 +γ×S 备份 This formula combines the effects of server type and backup configuration, adjusting the contribution of these two factors through the β and γ coefficients respectively.

[0154] To ensure that the weight information is used in an appropriate range (such as between 0 and 1), w 外部 and w 内部 Perform normalization.

[0155] Through the method provided in this embodiment, the specific contribution of the external environment and internal configuration to the risk of server power outage can be scientifically evaluated and integrated, thereby providing data support for formulating more accurate risk management strategies. The application of this weight calculation method helps to optimize the backup power management of the UPS system and enhance the stability and reliability of the overall system.

[0156] In some examples, determining the target control strategy based on the comprehensive power outage risk information includes:

[0157] When the above-mentioned comprehensive power outage risk information is low risk, the above-mentioned target control strategy is low operating power and low emergency response cycle; and / or,

[0158] When the above-mentioned comprehensive power outage risk information is medium risk, the above-mentioned target control strategy is medium operating power and medium emergency response cycle; and / or,

[0159] When the above-mentioned comprehensive power outage risk information is high risk, the above-mentioned target control strategy is high operating power and high emergency response cycle.

[0160] For example, in low-risk situations, the target control strategy is set to low operating power and low emergency response cycle. When the external and internal environments are relatively stable and the possibility of power outages is low, the hydrogen UPS will operate at a lower power, reducing energy consumption and extending equipment life, while reducing the demand for emergency response resources.

[0161] In the case of medium risk, the target control strategy is set to medium operating power and medium emergency response cycle. In this case, due to certain uncertainties in external or internal factors, the hydrogen UPS will be adjusted to medium power operation in order to quickly respond to possible changes in power demand while maintaining the efficiency and response speed of the system.

[0162] In high-risk situations, the target control strategy is set to high operating power and high emergency response cycle. When the comprehensive power outage risk information shows a high risk, it indicates that there may be serious power supply problems. At this time, the hydrogen UPS needs to operate at high power to ensure continuous power supply to critical loads and shorten the response cycle to respond to sudden power outages immediately.

[0163] Based on the above target control strategy, the system will automatically adjust its parameters and settings to match the current risk situation. This includes but is not limited to power output adjustment, the readiness of backup power sources, and the priority and speed of communication with critical infrastructure.

[0164] In this way, the intelligent control system of the hydrogen UPS is able to dynamically adjust its operating status and response measures based on the comprehensive power outage risk information evaluated in real time. This not only optimizes energy use and reduces operating costs, but also enhances the reliability and efficiency of the system in the face of different risk levels. The implementation of this strategy ensures the high safety and functionality of key equipment when power is unstable.

[0165] In a second aspect, the present application further proposes an online hydrogen UPS intelligent control device 20, comprising:

[0166] A first acquisition unit 21 is used to acquire external environment information of a target online hydrogen energy UPS;

[0167] The second acquisition unit 22 is used to acquire the internal operating environment information of the server corresponding to the target online hydrogen energy UPS as a backup power supply;

[0168] A third acquisition unit 23 is used to acquire external factor power outage risk information based on the external environment information;

[0169] A fourth acquisition unit 24, configured to acquire internal factor power failure risk information based on the internal operating environment information;

[0170] A first determining unit 25, configured to determine comprehensive power outage risk information based on the external factor power outage risk information and the internal factor power outage risk information;

[0171] A second determining unit 26, configured to determine a target control strategy based on the comprehensive power outage risk information;

[0172] The control unit 27 is used to control the target online hydrogen energy UPS based on the target control strategy.

[0173] In a third aspect, the present application further proposes an online hydrogen energy UPS 200, comprising the online hydrogen energy UPS intelligent control device 20 as described in the second aspect.

[0174] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0175] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0176] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0177] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0178] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0179] An embodiment of the present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes the process of predicting the decomposition rate in alumina production in the corresponding embodiment.

[0180] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0181] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0182] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0183] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0185] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0186] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An online hydrogen UPS intelligent control method, characterized in that: include: Obtain external environment information of the target online hydrogen energy UPS; Obtaining the internal operating environment information of the server corresponding to the target online hydrogen energy UPS as a backup power supply; Acquiring external factor power outage risk information based on the external environment information; Acquiring internal factor power failure risk information based on the internal operating environment information; Determine comprehensive power outage risk information based on the external factor power outage risk information and the internal factor power outage risk information; Determining a target control strategy based on the comprehensive power outage risk information; Controlling the target online hydrogen energy UPS based on the target control strategy; The external environment information includes weather condition information, power grid status information and regional factor information, the internal operating environment information includes server load information, system thermal management information and server aging information, and the target control strategy includes target operating power and emergency response cycle; The acquiring the external factor power outage risk information based on the external environment information includes: Fill missing values ​​and perform Z-score standardization operations on the weather condition information to obtain standardized weather condition information; Performing missing value interpolation and minimum and maximum normalization operations on the power grid status information to obtain normalized power grid status information; Performing a one-hot encoding operation on the regional factor information to obtain regional factor standardization information; Extracting weather condition features from the weather condition standardized information based on a time series analysis method; Extracting grid operation characteristics from the grid status standardized information based on graph theory; Extracting regional factor features from the regional factor standardized information based on principal component analysis; According to the weather condition characteristics, the power grid operation characteristics, the regional factor characteristics and the target random forest model, the external factor power outage risk information is obtained; The obtaining the internal factor power failure risk information based on the internal operating environment information includes: Preprocessing the server load information using a sliding window averaging method, and extracting load peak features from the processed server load information; Performing a normalization operation according to the system thermal management information, and extracting a temperature anomaly frequency feature and a temperature change rate feature of the normalized system thermal management information; Determining an aging score feature and a maintenance and failure association feature based on the server aging information; Input the load peak feature, temperature anomaly number feature, temperature change rate feature, aging score feature and maintenance and fault association feature into the target SVM classifier to obtain the internal factor power outage risk information; The determining of comprehensive power outage risk information based on the external factor power outage risk information and the internal factor power outage risk information includes: Obtaining geographical location information, server type information, and server backup information of the server corresponding to the target online hydrogen energy UPS as a backup power supply; Determine, based on the geographical location information, the server type information, and the server backup information, first weight information corresponding to the external factor power outage risk information and second weight information corresponding to the internal factor power outage risk information; Comprehensive power outage risk information is determined based on the first weight information, the second weight information, the external factor power outage risk information, and the internal factor power outage risk information.

2. The online hydrogen UPS intelligent control method according to claim 1 is characterized in that: The step of obtaining the external factor power outage risk information according to the weather condition characteristics, the power grid operation characteristics, the regional factor characteristics and the target random forest model includes: Performing a splicing operation on the weather condition characteristics, the power grid operation characteristics and the regional factor characteristics to determine the basic characteristics of the external environment; In the case where at least two of the extracted weather condition features, the power grid operation features and the regional factor features exceed their corresponding feature thresholds, generating an external environment binary feature corresponding to the external environment based on the external environment features exceeding the feature thresholds; The external environment basic features and the external environment binary features are input into a target random forest model to obtain the power outage risk information of the external factors.

3. The online hydrogen UPS intelligent control method according to claim 1 is characterized in that: The first weight information is determined based on the following formula w 外部 and the second weight information w 内部 : α, β, γ are the preset initial coefficients, S 地理 is the geographic location coefficient, S 类型 is the server type coefficient and S 备份 The server backup factor.

4. The online hydrogen UPS intelligent control method according to claim 1 is characterized in that: The determining of a target control strategy based on the comprehensive power outage risk information includes: When the comprehensive power outage risk information is low risk, the target control strategy is low operating power and low emergency response cycle; and / or, When the comprehensive power outage risk information is medium risk, the target control strategy is medium operating power and medium emergency response cycle; and / or, When the comprehensive power outage risk information is high risk, the target control strategy is high operating power and high emergency response cycle.

5. An online hydrogen UPS intelligent control device, characterized in that: include: A first acquisition unit is used to acquire external environment information of a target online hydrogen energy UPS; The second acquisition unit is used to acquire the internal operating environment information of the server corresponding to the target online hydrogen energy UPS as a backup power supply; A third acquisition unit, configured to acquire external factor power outage risk information based on the external environment information; A fourth acquisition unit, configured to acquire internal factor power failure risk information based on the internal operating environment information; A first determining unit, configured to determine comprehensive power outage risk information based on the external factor power outage risk information and the internal factor power outage risk information; A second determining unit, configured to determine a target control strategy based on the comprehensive power outage risk information; A control unit, configured to control the target online hydrogen energy UPS based on the target control strategy; The external environment information includes weather condition information, power grid status information and regional factor information, the internal operating environment information includes server load information, system thermal management information and server aging information, and the target control strategy includes target operating power and emergency response cycle; The acquiring the external factor power outage risk information based on the external environment information includes: Fill missing values ​​and perform Z-score standardization operations on the weather condition information to obtain standardized weather condition information; Performing missing value interpolation and minimum and maximum normalization operations on the power grid status information to obtain normalized power grid status information; Performing a one-hot encoding operation on the regional factor information to obtain regional factor standardization information; Extracting weather condition features from the weather condition standardized information based on a time series analysis method; Extracting grid operation characteristics from the grid status standardized information based on graph theory; Extracting regional factor features from the regional factor standardized information based on principal component analysis; According to the weather condition characteristics, the power grid operation characteristics, the regional factor characteristics and the target random forest model, the external factor power outage risk information is obtained; The obtaining the internal factor power failure risk information based on the internal operating environment information includes: Preprocessing the server load information using a sliding window averaging method, and extracting load peak features from the processed server load information; Performing a normalization operation according to the system thermal management information, and extracting a temperature anomaly frequency feature and a temperature change rate feature of the normalized system thermal management information; Determining an aging score feature and a maintenance and failure association feature based on the server aging information; Input the load peak feature, temperature anomaly number feature, temperature change rate feature, aging score feature and maintenance and fault association feature into the target SVM classifier to obtain the internal factor power outage risk information; The determining of comprehensive power outage risk information based on the external factor power outage risk information and the internal factor power outage risk information includes: Obtaining geographical location information, server type information, and server backup information of the server corresponding to the target online hydrogen energy UPS as a backup power supply; Determine, based on the geographical location information, the server type information, and the server backup information, first weight information corresponding to the external factor power outage risk information and second weight information corresponding to the internal factor power outage risk information; Comprehensive power outage risk information is determined based on the first weight information, the second weight information, the external factor power outage risk information, and the internal factor power outage risk information.

6. An online hydrogen UPS, characterized in that: It comprises the online hydrogen energy UPS intelligent control device as described in claim 5.

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