Monitoring method and system of unattended satellite base station

Abstract

The invention belongs to the technical field of base station monitoring, and provides an unattended satellite base station monitoring method and system, and the method comprises the steps: generating an external power supply state risk value according to external power supply state feature data; generating an internal power supply capability pre-estimated risk value according to the internal power supply capability characteristic data; according to the external power supply state risk value, the internal power supply capability pre-estimated risk value, the external power supply cost data and the internal power supply cost data, generating a decision value so as to monitor the unattended satellite base station; according to the method, the defects that a traditional monitoring scheme depends on single threshold judgment and lacks power supply quality trend analysis and cruising ability prediction are effectively overcome, the accuracy and the foresight of risk assessment are remarkably improved, optimal balance is achieved between economical cost and power supply risks, mistaken switching and switching lag are reduced, and the power supply reliability is improved. The power supply reliability and the operation stability of the base station in an unattended environment are enhanced, and the overall operation and maintenance efficiency and the economical efficiency of the system are improved.

Description

Technical Field

[0001] This invention belongs to the field of base station monitoring technology, and in particular relates to a monitoring method and system for unattended satellite base stations. Background Technology

[0002] With the widespread application of satellite communication technology in remote areas, offshore platforms, and emergency communications, the construction scale of unmanned satellite base stations continues to expand. These base stations are typically deployed in areas with harsh natural environments, inconvenient transportation, and where it is difficult for personnel to maintain a presence for extended periods. Their continuous and stable operation is highly dependent on a reliable and economical power supply. Therefore, achieving intelligent monitoring and autonomous switching of the power supply status of satellite base stations has become a core challenge in ensuring their long-term reliable operation.

[0003] Currently, monitoring practices for the power supply status of unattended base stations mainly rely on simple judgment mechanisms based on fixed thresholds. For example, when the external power supply voltage momentarily drops below a certain preset value, the system immediately switches to backup power, or triggers the internal power generation equipment to start based solely on a static threshold of the remaining battery power. These solutions generally suffer from limitations such as insufficient analysis of power supply fluctuations, lack of battery endurance prediction, and weak consideration of economic costs. Specifically, existing methods rely excessively on instantaneous measurements of a single parameter, failing to effectively capture the continuous evolution trend of power quality and the dynamics of potential risks. This leads to frequent erroneous switching operations during brief voltage fluctuations or instantaneous current overloads. Simultaneously, internal power supply capabilities, such as the actual battery endurance and the success rate of power generation equipment startup, lack quantitative modeling and forward-looking prediction, resulting in severely delayed or overly sensitive switching decisions, significantly impacting the continuity and stability of base station operation. Furthermore, power supply cost factors, including real-time electricity price fluctuations in the external power grid and fuel consumption costs of internal power generation, are often ignored in the decision-making process. This prevents the system from achieving a reasonable balance between power supply risk control and economic optimization, thus restricting the overall efficiency and resource utilization efficiency of the base station in the long term. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a monitoring method and system for unattended satellite base stations, solving the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a monitoring method for an unattended satellite base station, specifically comprising:

[0006] Acquire external power supply status characteristic data, internal power supply capacity characteristic data, external power supply cost data, and internal power supply cost data for satellite base stations;

[0007] An external power supply analysis model is established based on the external power supply status characteristic data to generate an external power supply status risk value; the external power supply status characteristic data includes the external power supply voltage value and the external power supply current value.

[0008] An internal power supply analysis model is established based on the internal power supply capacity characteristic data, and an estimated risk value for internal power supply capacity is generated.

[0009] A decision analysis model is established based on the external power supply status risk value, the internal power supply capacity estimated risk value, external power supply cost data, and internal power supply cost data to generate decision values.

[0010] Based on the decision value, unattended satellite base stations are monitored.

[0011] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0012] Further technical solutions: The method for generating the external power supply status risk value specifically includes:

[0013] A voltage fluctuation index is generated based on the external power supply voltage value;

[0014] The current overload index is generated based on the external power supply current value;

[0015] An external power supply analysis model is established based on the voltage fluctuation index and the current overload index to generate an external power supply status risk value.

[0016] Further technical solution: The specific method for generating the voltage fluctuation index includes:

[0017] Through the formula: ;

[0018] Generation voltage fluctuation index ;

[0019] In the formula, This represents the external supply voltage value at time k. This represents the external supply voltage value at time k-1, where N is the number of data points in the time window. This indicates the rated voltage of the external power supply;

[0020] The method for generating the current overload index specifically includes:

[0021] Through the formula: ;

[0022] Generation current overload index ;

[0023] In the formula, This represents the external supply current value at time k. This represents the maximum input current allowed for normal operation of the base station.

[0024] Further technical solution: The specific expression of the external power supply analysis model is as follows: ;

[0025] In the expression, This indicates the risk value of the external power supply status. This indicates the voltage qualification rate of the external power supply. This represents the voltage fluctuation index. This indicates the current overload index. , , All are weighting coefficients, and ;

[0026] Among them, the voltage qualification rate of the external power supply The specific methods of obtaining it include:

[0027] Through the formula: ;

[0028] Generating the voltage qualification rate of external power supply ;

[0029] In the formula, This represents the external supply voltage value at time k. This indicates the minimum voltage required for the base station to operate normally. This indicates the highest voltage at which the base station operates normally. This is a conditional indicator function.

[0030] Further technical solutions: The method for generating the estimated risk value of the internal power supply capacity specifically includes:

[0031] Based on the internal power supply capacity characteristic data, the estimated internal power supply range is generated; the internal power supply capacity characteristic data includes the remaining battery capacity percentage and the power generation capacity of the internal power generation components.

[0032] Based on the estimated range of internal power supply, a battery power supply capability risk index is generated.

[0033] An internal power supply analysis model is established based on the battery power supply capacity risk index to generate an estimated risk value for internal power supply capacity.

[0034] The specific expression of the internal power supply analysis model is as follows: In the expression, This represents the estimated risk value of internal power supply capacity. This represents the risk index of the battery's power supply capacity. This indicates the startup success rate of the base station's power generation equipment. This represents the weighting coefficient for the risk of battery power supply.

[0035] Further technical solutions: The method for generating the estimated battery life using the internal power supply specifically includes:

[0036] Through the formula: Generate estimated battery life based on internal power supply. ;

[0037] In the formula, the internal power supply estimates the battery life. This refers to the maximum number of consecutive hours, T, where the battery charge is positive at the end of all time periods. It represents the set of positive integers, and t represents the time period index. This represents the available battery charge at the start of time period t+1;

[0038] Wherein, the available power of the battery at the beginning of the (t+1)th time period. The specific methods of obtaining it include:

[0039] Through the formula: ;

[0040] Generate the available battery charge at the start of time period t+1. ;

[0041] In the formula, This represents the available battery charge at time t. This represents the total amount of electricity that all the power generation equipment at the base station can replenish for the battery at time t. This represents the total amount of electricity consumed by the base station load from the battery at time t. This indicates the rated capacity of the battery. This represents the percentage of remaining battery charge at time t. This indicates the degree of aging of the battery. This represents the average power generation of new energy power generation equipment at time t. This indicates the charging efficiency of the new energy power generation equipment for charging the battery. This represents the duration at time t. This represents the average power output of the fuel-powered generator at time t. This indicates the charging efficiency of the fuel cell power generation equipment in charging the battery. This represents the average load power of the base station at time t.

[0042] Further technical solution: The specific method for generating the battery power supply capability risk index includes:

[0043] Through the formula: ;

[0044] Generate a risk index for battery power supply capacity ;

[0045] In the formula, This indicates the estimated battery life powered by the internal power supply. This indicates the critical battery life of the internal power supply.

[0046] Further technical solution: The method for generating the decision value specifically includes:

[0047] Based on the risk value of the external power supply status and the estimated risk value of the internal power supply capacity, risk-based decision components are generated;

[0048] Based on external power supply cost data and internal power supply cost data, generate decision components based on economic costs;

[0049] A decision analysis model is established based on risk-based decision components and economic cost-based decision components to generate decision values;

[0050] The specific expression of the decision analysis model is as follows: ;

[0051] In the expression, D represents the decision value. This represents the risk-based decision weight. This represents the decision component based on economic costs. This represents the weighting coefficient of the risk-based decision component.

[0052] Further technical solution: The method for generating the risk-based decision components specifically includes:

[0053] Through the formula: ;

[0054] Generate risk-based decision components ;

[0055] In the formula, This indicates the risk value of the external power supply status. This represents the estimated risk value of internal power supply capacity. This represents the weighting coefficient for the risk of external power supply status;

[0056] The specific methods for generating the decision components based on economic costs include:

[0057] Through the formula: ;

[0058] Generate decision components based on economic costs. ;

[0059] In the formula, This represents the unit cost of external power supply. This represents the unit cost of internal power supply. This represents the threshold for the difference in power supply costs. This represents the cost of a single power supply switching operation. This represents the cost threshold for a single power supply switching operation.

[0060] A monitoring system for an unattended satellite base station, the system being used to execute the aforementioned monitoring method for an unattended satellite base station, specifically including:

[0061] The data acquisition unit is used to acquire external power supply status characteristic data, internal power supply capability characteristic data, external power supply cost data, and internal power supply cost data of the satellite base station;

[0062] The external power supply analysis unit is used to establish an external power supply analysis model and generate an external power supply status risk value based on external power supply status characteristic data; wherein, the external power supply status characteristic data includes external power supply voltage value and external power supply current value;

[0063] The internal power supply analysis unit is used to establish an internal power supply analysis model based on the internal power supply capacity characteristic data and generate an estimated risk value for the internal power supply capacity.

[0064] The decision analysis unit is used to establish a decision analysis model and generate decision values ​​based on the external power supply status risk value, the internal power supply capacity estimated risk value, external power supply cost data, and internal power supply cost data.

[0065] The monitoring unit is used to monitor unattended satellite base stations based on decision values.

[0066] This invention provides a monitoring method and system for unattended satellite base stations, which has the following advantages compared with the prior art:

[0067] This invention generates decision values ​​to guide power supply switching by comprehensively collecting external power supply status characteristic data, internal power supply capacity characteristic data, and power supply cost data, thus achieving dynamic, quantitative, and intelligent monitoring of the power supply status of satellite base stations. This method effectively overcomes the shortcomings of traditional monitoring schemes that rely on single threshold judgments and lack analysis of power supply quality trends and prediction of endurance. By introducing quantitative indicators such as voltage fluctuation index, current overload index, and estimated internal power supply endurance, it significantly improves the accuracy and foresight of risk assessment. At the same time, it achieves an optimal balance between economic costs and power supply risks, thereby reducing false handovers and handover delays, enhancing the power supply reliability and operational stability of base stations in unattended environments, and improving the overall operation and maintenance efficiency and economy of the system. Attached Figure Description

[0068] Figure 1 This is a flowchart illustrating a monitoring method for an unattended satellite base station provided by the present invention.

[0069] Figure 2 This is a flowchart illustrating step S20 of the present invention.

[0070] Figure 3 This is a flowchart illustrating step S30 of the present invention.

[0071] Figure 4 This is a flowchart illustrating step S40 of the present invention.

[0072] Figure 5 This is a schematic diagram of the structure of a monitoring system for an unattended satellite base station provided by the present invention.

[0073] Figure 6 A schematic diagram of the external power supply analysis unit provided by the present invention.

[0074] Figure 7 This is a schematic diagram of the internal power supply analysis unit provided by the present invention.

[0075] Figure 8 This is a schematic diagram of the structure of the decision analysis unit provided by the present invention. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0077] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0078] Please see Figure 1 The present invention provides a monitoring method for an unattended satellite base station, which specifically includes the following steps:

[0079] Step S10: Obtain the external power supply status characteristic data, internal power supply capability characteristic data, external power supply cost data, and internal power supply cost data of the satellite base station;

[0080] Step S20: Establish an external power supply analysis model based on the external power supply status characteristic data and generate an external power supply status risk value; wherein, the external power supply status characteristic data includes the external power supply voltage value and the external power supply current value;

[0081] Step S30: Establish an internal power supply analysis model based on the internal power supply capacity characteristic data, and generate an estimated risk value for the internal power supply capacity;

[0082] Step S40: Establish a decision analysis model based on the external power supply status risk value, the internal power supply capacity estimated risk value, the external power supply cost data, and the internal power supply cost data, and generate decision values;

[0083] Step S50: Monitor the unattended satellite base station based on the decision value;

[0084] Among them, the internal power supply cost data refers to the economic costs incurred by using the backup power supply inside the satellite base station, including fuel consumption costs, equipment maintenance costs, battery depreciation costs, etc.

[0085] Existing technologies generally lack dynamic prediction and quantitative assessment of internal power supply capabilities, often only activating backup power when the battery charge is extremely low, leading to delayed switching decisions. This application obtains internal power supply capability characteristic data in step S10 and establishes an internal power supply analysis model in step S30, enabling the prediction and risk quantification of internal power supply capabilities. This allows the invention to generate an estimated risk value for internal power supply capabilities based on the remaining battery capacity percentage, and to make decisions in conjunction with external risks, avoiding the predicament of being unable to switch in time due to insufficient internal power reserves when external power supply quality is poor.

[0086] Furthermore, existing technologies generally neglect power supply cost factors, failing to achieve an optimal balance between risk and economy. This application obtains external and internal power supply cost data in step S10, and establishes a decision analysis model in step S40 to comprehensively consider power supply risk and economic costs, generating a decision value. Therefore, the technical solution of this application achieves intelligent monitoring and switching of power supply for unattended satellite base stations, solving the problems of relying on a single parameter, lack of predictability, and neglect of economic considerations in existing technologies, thereby improving the efficiency and effectiveness of base station operation.

[0087] Traditional unattended satellite base station power supply monitoring methods lack detailed quantitative analysis of dynamic changes in power supply status when assessing external power supply risks, especially specific indicators of voltage fluctuations and current overloads. This results in risk assessments relying solely on simple data and failing to fully capture the continuous and trend-based risks of external power supply, thus affecting the accuracy and reliability of monitoring decisions.

[0088] For this, please refer to Figure 2 The present invention further proposes a method for generating the external power supply status risk value, specifically including:

[0089] Step S21: Generate a voltage fluctuation index based on the external power supply voltage value;

[0090] Step S22: Generate the current overload index based on the external power supply current value;

[0091] Step S23: Establish an external power supply analysis model based on the voltage fluctuation index and current overload index, and generate an external power supply status risk value;

[0092] The method for generating the external power supply status risk value aims to comprehensively assess its stability and reliability by quantifying various key indicators of external power supply, thereby providing data support for subsequent power supply decisions.

[0093] Step S21 aims to quantify the stability of the external power supply voltage. The voltage fluctuation index reflects the amplitude, frequency, or degree of deviation of the voltage from its rated value within a certain time window.

[0094] Step S22 aims to quantify the overload risk of the external power supply current. The current overload index can reflect the degree, duration, or frequency of the current exceeding the safe threshold.

[0095] The purpose of step S23 is to comprehensively consider the risk indicators of voltage and current to form a comprehensive risk assessment of the external power supply status.

[0096] This application's solution introduces a voltage fluctuation index and a current overload index to provide a more refined and comprehensive quantitative assessment of the external power supply status. Specifically, after obtaining the external power supply voltage and current values, firstly, based on the external power supply voltage value, a voltage fluctuation index is generated by calculating its changes within a specific time window. This index dynamically captures voltage stability, avoiding the limitations of relying solely on instantaneous voltage values. Secondly, based on the external power supply current value, a current overload index is generated by analyzing whether it exceeds the maximum allowable value and the extent of the exceedance. This index accurately identifies current anomalies and potential overload risks. Finally, using the voltage fluctuation index and current overload index as inputs, an external power supply analysis model is established. This model comprehensively considers voltage stability and current safety, generating a unified external power supply status risk value through internal logical operations or algorithms. This risk value not only reflects the instantaneous status of the external power supply but also includes its dynamic trends and potential risk levels. In this way, this solution overcomes the problems of incomplete risk assessment and lack of dynamic analysis in traditional methods, providing a more accurate and reliable basis for subsequent power supply decisions. This meticulous risk quantification, combined with the acquisition of various power supply data, the establishment of internal power supply analysis models, and the final decision analysis model, enables the entire unattended satellite base station monitoring method to shift from a single, static judgment to a multi-dimensional, dynamic, and comprehensive assessment when evaluating external power supply risks, thereby significantly improving the scientific nature and effectiveness of power supply monitoring decisions.

[0097] Through the above technical solution, this application effectively addresses the problems of incomplete external power supply risk assessment and lack of dynamic analysis in traditional methods. By introducing voltage fluctuation index and current overload index and incorporating them into the external power supply analysis model, a refined and dynamic quantitative assessment of the external power supply status is achieved. This allows the monitoring system to move beyond simple threshold judgments of instantaneous voltage or current, and instead comprehensively capture the continuous and trend-based risks of external power supply, as well as potential overload situations. Therefore, the generated external power supply status risk values ​​are more accurate and reliable, providing a more solid data foundation for power supply monitoring of unattended satellite base stations. This enables more timely and accurate power supply mode switching decisions, significantly improving the stability and reliability of base station power supply and reducing the risk of operational interruptions due to power supply anomalies.

[0098] In the scheme of this application, voltage fluctuation index and current overload index are proposed to generate external power supply status risk value. However, in the process of its implementation, how to accurately quantify voltage fluctuation and current overload to accurately assess external power supply risk is not clear, which may lead to risk assessment relying on incomplete instantaneous data and lacking dynamism and reliability.

[0099] In response, this invention further proposes a method for generating the voltage fluctuation index, specifically including:

[0100] Through the formula: ;

[0101] Generation voltage fluctuation index ;

[0102] In the formula, This represents the external supply voltage value at time k. This represents the external supply voltage value at time k-1, where N is the number of data points in the time window. This indicates the rated voltage of the external power supply;

[0103] The method for generating the current overload index specifically includes:

[0104] Through the formula: ;

[0105] Generation current overload index ;

[0106] In the formula, This represents the external supply current value at time k. This indicates the maximum allowable input current for normal base station operation;

[0107] The rated voltage of the external power supply refers to the standard external power supply voltage value expected during the design and operation of the unattended satellite base station.

[0108] The maximum allowable input current for normal operation of a base station refers to the highest current value that the external power supply system can safely withstand under normal operating conditions for unattended satellite base station equipment; exceeding this value may cause equipment damage or power supply system failure.

[0109] This application's solution introduces specific mathematical formulas to accurately calculate the voltage fluctuation index and current overload index, thereby solving the problems of inaccurate quantification of external power supply risk assessment and lack of dynamism and reliability due to reliance on instantaneous data in traditional methods. In the monitoring method for unattended satellite base stations, firstly, external power supply voltage and current values ​​are continuously collected, and these real-time data are input into the corresponding calculation module. For the generation of the voltage fluctuation index, the system continuously acquires the external power supply voltage value at the current time k and the external power supply voltage value at the previous time k-1 within a preset time window N. By calculating the average of the absolute values ​​of these continuous voltage value differences and normalizing it with the rated voltage of the external power supply, a quantitative index reflecting the degree of continuous voltage fluctuation is obtained. This method avoids the limitations of relying solely on a single instantaneous voltage value for judgment and can more comprehensively capture the voltage change trend and risk over time. Simultaneously, for the generation of the current overload index, the system also acquires the external power supply current value at the current time k within a time window N. This current value is compared with the maximum allowable input current for normal base station operation. The overload ratio is calculated only when the actual current exceeds the maximum allowable current, and a max function is used to ensure that only overload conditions are considered. These overload ratios are then averaged. This method directly quantifies the severity and duration of current overload, ensuring that the assessment is based on the actual operational limitations of the base station and effectively avoiding the problem of ignoring the cumulative effect of overload. Through the precise calculation of the two indices mentioned above, this application provides a dynamic and reliable quantitative basis for subsequently establishing an external power supply analysis model and generating external power supply status risk values. These indices can capture subtle changes and potential risks in external power supply, enabling the external power supply analysis model to more accurately assess the overall health of the external power supply, thereby providing more refined and reliable data support for power supply monitoring and decision-making for unattended satellite base stations. This dynamic quantitative method, combined with the basic external power supply status risk assessment, significantly improves the accuracy and foresight of the risk assessment, enabling base stations to respond to changes in the external power supply environment more promptly and effectively.

[0110] Through the above technical solutions, this application can accurately quantify voltage fluctuations and current overloads in external power supply, thereby solving the problems of inaccurate quantification and reliance on incomplete instantaneous data, resulting in a lack of dynamism and reliability in traditional risk assessment. Specifically, the introduction of the voltage fluctuation index enables the system to capture the continuous changes in voltage over time, rather than relying solely on instantaneous voltage values, which significantly improves the ability to identify voltage instability. The calculation of the current overload index directly quantifies the degree and duration of current exceeding the base station's allowable range, effectively avoiding potential equipment damage or system failure risks caused by cumulative overload. These precisely quantified indices, as inputs to the external power supply analysis model, make the generated external power supply status risk values ​​more dynamic and reliable, providing a more refined and forward-looking risk assessment basis for power supply monitoring of unattended satellite base stations. This enables more timely and accurate power supply mode switching decisions, ensuring the stable operation of the base station.

[0111] Traditional unattended satellite base station power supply monitoring methods lack detailed definitions of specific model expressions and voltage qualification rate quantification methods when assessing external power supply risks. This can lead to inaccurate risk value calculations, failing to fully reflect the continuity and risk trends of power supply quality, and thus affecting the accuracy and reliability of power supply monitoring decisions.

[0112] In response, this invention further proposes the following expression for the external power supply analysis model: ;

[0113] In the expression, This indicates the risk value of the external power supply status. This indicates the voltage qualification rate of the external power supply. This represents the voltage fluctuation index. This indicates the current overload index. , , All are weighting coefficients, and ;

[0114] Among them, the voltage qualification rate of the external power supply The specific methods of obtaining it include:

[0115] Through the formula: ;

[0116] Generating the voltage qualification rate of external power supply ;

[0117] In the formula, This represents the external supply voltage value at time k. This indicates the minimum voltage required for the base station to operate normally. This indicates the highest voltage at which the base station operates normally. For conditional indicator functions;

[0118] The external power supply analysis model aims to quantify and assess the risk level of external power supply for unattended satellite base stations. It calculates the external power supply status risk value by comprehensively considering the voltage qualification rate, voltage fluctuation index, and current overload index of the external power supply in a weighted summation form. This modeling method can integrate multiple factors affecting power supply stability and provide a unified risk metric.

[0119] The external power supply status risk value is a core indicator output by the external power supply analysis model, used to characterize the overall risk level of the current external power supply. A higher external power supply status risk value indicates a higher risk of instability or failure in the external power supply, which may require intervention measures.

[0120] The external power supply voltage qualification rate reflects the proportion of external power supply voltages within the range required for normal base station operation within a specific time window. It is an important parameter for measuring the stability and reliability of power supply voltage and can reflect the continuity of power supply quality.

[0121] The voltage fluctuation index is used to quantify the degree of instantaneous change in the external supply voltage; a larger voltage fluctuation index indicates that the external supply voltage is unstable and there may be frequent voltage drops or overvoltage situations.

[0122] The current overload index is used to quantify the extent to which the external power supply current exceeds the maximum allowable input current of the base station; a large current overload index indicates that there may be an overload risk in the external power supply, which may lead to equipment damage or power outage.

[0123] Weighting coefficient , , These weights are used to adjust the relative importance of each risk component (voltage qualification rate, voltage fluctuation index, current overload index) in the external power supply analysis model to the final external power supply status risk value. By setting these weights appropriately, the model can more accurately reflect the power supply risk preference of a specific base station or environment. These weight coefficients can be determined based on historical data analysis, expert experience, or by optimization through machine learning algorithms.

[0124] The voltage qualification rate of external power supply is obtained by counting the number of times the external power supply voltage value falls between the preset minimum and maximum voltage within a certain time window, and then dividing it by the total number of sampling points N to calculate the voltage qualification rate.

[0125] The conditional exponential function is a binary function used to determine whether a certain condition is true. In this application, it is used to determine whether the external power supply voltage at time k is within the voltage range required for the normal operation of the base station (i.e., between the minimum voltage required for the normal operation of the base station and the maximum voltage required for the normal operation of the base station); when the condition is true, the function outputs 1, indicating that the voltage is qualified at that time; otherwise, it outputs 0, indicating that it is unqualified.

[0126] This application proposes a comprehensive external power supply analysis model to accurately assess the external power supply status risk of unattended satellite base stations. The core of this model lies in integrating multiple key external power supply indicators—voltage qualification rate, voltage fluctuation index, and current overload index—through a weighted summation to generate an external power supply status risk value. In this way, the model can dynamically and comprehensively reflect the overall risk level of the external power supply, overcoming the limitations of traditional methods that rely solely on instantaneous threshold judgments. This refined risk assessment mechanism provides a more accurate and reliable basis for subsequent power supply monitoring decisions, enabling unattended satellite base stations to switch power supply modes more intelligently, thereby ensuring their continuous and stable operation.

[0127] Through the above technical solution, this application can effectively solve the problems of inaccurate models and inability to fully reflect the continuity of power quality and risk trends when traditional methods assess external power supply risks. The refined risk assessment mechanism provided by this technical solution significantly improves the accuracy and reliability of power supply monitoring decisions for unattended satellite base stations, avoiding power supply switching delays or frequent malfunctions caused by inaccurate risk assessments, thereby ensuring the long-term stable operation of the base station.

[0128] Traditional unattended satellite base station power supply monitoring methods lack specific quantitative processing and dynamic prediction mechanisms for assessing internal power supply capacity, such as the percentage of remaining battery capacity and the power generation of internal power generation components. This results in inaccurate risk value generation, failing to effectively reflect the endurance of internal power supply and equipment reliability. Consequently, it affects the timeliness and accuracy of power supply switching decisions, leading to decision lags or frequent malfunctions.

[0129] For this, please refer to Figure 3 The present invention further proposes a method for generating the estimated risk value of the internal power supply capacity, specifically including:

[0130] Step S31: Generate the estimated internal power supply range based on the internal power supply capability characteristic data; wherein, the internal power supply capability characteristic data includes the remaining battery capacity percentage and the power generation capacity of the internal power generation components;

[0131] Step S32: Based on the estimated battery life, generate a battery power supply capability risk index.

[0132] Step S33: Establish an internal power supply analysis model based on the battery power supply capacity risk index and generate an estimated risk value for the internal power supply capacity;

[0133] The specific expression of the internal power supply analysis model is as follows: In the expression, This represents the estimated risk value of internal power supply capacity. This represents the risk index of the battery's power supply capacity. This indicates the startup success rate of the base station's power generation equipment. This represents the weighting coefficient for the risk of battery power supply;

[0134] The method for generating the internal power supply capacity risk value aims to accurately assess the continuous power supply capacity and potential risks of the unattended satellite base station under the condition of external power supply interruption or instability by quantitatively analyzing and modeling various key parameters of the internal power supply system. Its role is to provide a scientific basis for the power supply switching decision of the base station and ensure the stable operation of the base station.

[0135] Specifically, the core of step S31 is to dynamically predict the sustainable power supply time of the base station's internal power supply system (mainly batteries and internal power generation components) under the current load conditions. Its role is to provide a key time dimension indicator for subsequent risk assessment.

[0136] Internal power supply capability characteristic data is the basic information for assessing internal power supply capability, and its accuracy directly affects the reliability of subsequent risk assessment. This data can be collected in real time by sensors inside the base station, such as obtaining the remaining battery capacity percentage through the battery management system (BMS) and obtaining the power generation power of the internal power generation components through the generator controller.

[0137] The remaining battery capacity percentage reflects the proportion of the battery's current usable electricity to its total rated capacity. It is a key indicator for assessing the battery's power supply capability, and this percentage can be read directly through the battery management system (BMS).

[0138] The power output of the internal power generation component represents the electrical energy output capability that the backup power generation equipment (such as diesel generators, solar panels, etc.) configured inside the base station can provide when in operation; this power value can be reported in real time by the controller of the power generation component itself, or measured by a power sensor installed at the output end of the power generation component;

[0139] The estimated internal power supply duration indicates the maximum time that the base station's internal power supply system can independently maintain the base station's normal operation under current or predicted load conditions. Its function is to quantify the sustainability of the internal power supply.

[0140] Step S32 aims to transform the abstract concept of battery life into a quantifiable risk indicator so that it can be uniformly processed in the decision-making model. Its function is to intuitively reflect the degree of risk of insufficient battery power supply.

[0141] The battery power supply capability risk index is a dimensionless value used to measure the degree of risk that the battery's power supply capability cannot meet the continuous operation requirements of the base station under the current condition.

[0142] Step S33 is a key step in the comprehensive assessment of the overall risk of the internal power supply system. It combines the risk of the battery with the reliability of the power generation equipment, and its role is to provide a comprehensive assessment result of the internal power supply risk.

[0143] The internal power supply analysis model is a mathematical expression or algorithm used to comprehensively assess the overall risk of the internal power supply system of an unattended satellite base station. Its role is to quantify the reliability and sustainability of the internal power supply. The estimated risk value of the internal power supply capacity is the final output of the internal power supply analysis model, representing the overall potential risk level of the base station's internal power supply system.

[0144] The startup success rate of base station power generation equipment reflects the probability that the backup power generation equipment (such as diesel generators) inside the base station can operate successfully when needed. Its function is to evaluate the reliability of the internal power generation components. This success rate can be obtained by statistical analysis of historical operating data, such as counting the number of successful startup attempts in the past 100 attempts.

[0145] Weighting factor for battery power supply risk This coefficient is used to adjust the relative importance of the battery power supply capability risk index in the internal power supply analysis model. Its function is to allow the system to give different degrees of emphasis to the risks of batteries and power generation equipment according to actual needs or strategy preferences.

[0146] This application addresses the problem of inaccurate internal power supply risk assessment in traditional methods by introducing a refined assessment mechanism for internal power supply capabilities. In this application, the estimated risk value of internal power supply capabilities dynamically reflects the actual battery endurance and the reliability of the power generation equipment. This allows the decision analysis model to obtain more accurate input, thereby generating more reliable decision values ​​to guide the switching of power supply modes for unattended satellite base stations. This refined internal power supply risk assessment enables more timely and accurate power supply switching decisions for base stations, avoiding decision lags or frequent erroneous actions caused by insufficient internal power supply capability assessment, and significantly improving the reliability and operational efficiency of base station power supply.

[0147] Through the above technical solution, this application enables a more accurate and dynamic assessment of the internal power supply capabilities of unattended satellite base stations. By introducing the estimated internal power supply endurance and battery power supply capability risk index, and combining this with the startup success rate of the power generation equipment, this solution overcomes the shortcomings of traditional methods in terms of coarse and inaccurate internal power supply risk assessment. This refined risk assessment mechanism allows the base station to identify potential risks to its internal power supply earlier and more accurately, thus providing a reliable basis for power supply switching decisions. When combined with monitoring methods for unattended satellite base stations, it can significantly improve the timeliness and accuracy of power supply switching decisions, effectively avoiding decision lags or frequent erroneous actions caused by insufficient assessment of internal power supply capabilities, thereby ensuring the continuous and stable operation of unattended satellite base stations, reducing maintenance costs, and improving overall operational efficiency.

[0148] In the scheme of this application, an internal power supply estimated endurance is proposed to evaluate the internal power supply capacity. However, in its implementation, the existing method cannot dynamically predict the battery endurance, resulting in inaccurate decision-making. Specifically, it relies on static parameters and ignores the dynamic interaction of power changes, power generation equipment contribution and load consumption, thus failing to reliably determine the maximum continuous power supply duration.

[0149] In response, this invention further proposes a method for generating the estimated battery life using internal power supply, specifically including:

[0150] Through the formula: Generate estimated battery life based on internal power supply. ;

[0151] In the formula, the internal power supply estimates the battery life. This refers to the maximum number of consecutive hours, T, where the battery charge is positive at the end of all time periods. It represents the set of positive integers, and t represents the time period index. This represents the available battery charge at the start of time period t+1;

[0152] Wherein, the available power of the battery at the beginning of the (t+1)th time period. The specific methods of obtaining it include:

[0153] Through the formula: ;

[0154] Generate the available battery charge at the start of time period t+1. ;

[0155] In the formula, This represents the available battery charge at time t. This represents the total amount of electricity that all the power generation equipment at the base station can replenish for the battery at time t. This represents the total amount of electricity consumed by the base station load from the battery at time t. This indicates the rated capacity of the battery. This represents the percentage of remaining battery charge at time t. This indicates the degree of aging of the battery. This represents the average power generation of new energy power generation equipment at time t. This indicates the charging efficiency of the new energy power generation equipment for charging the battery. This represents the duration at time t. This represents the average power output of the fuel-powered generator at time t. This indicates the charging efficiency of the fuel cell power generation equipment in charging the battery. This represents the average load power of the base station at time t;

[0156] The method for generating the estimated power supply duration of an unattended satellite base station aims to quantify the maximum time that the internal power supply system can continuously provide power to the base station when the external power supply is interrupted or unstable. Its role is to provide key predictive data for power supply switching decisions and ensure the continuity of base station operation.

[0157] The core of the formula for calculating the estimated battery life using internal power supply lies in finding the largest continuous time period T such that the available battery power is such that at all times t within that time period... It remains positive at all times. This ensures that the battery provides the necessary power throughout the predicted range, preventing power outages due to depletion of the battery.

[0158] The method for obtaining the available battery power at the start of time period t+1, namely the formula set, is used to dynamically calculate the available battery power at the start of time period t+1. Its function is to accurately simulate the charging and discharging process of the battery in different time periods, comprehensively considering the initial battery power, the charging contribution of the power generation equipment, and the consumption of the base station load. This makes the prediction of battery endurance more closely resemble actual operating conditions; among them, the formula... Used to calculate the battery's available capacity at time t. It combines the battery's rated capacity, current remaining percentage of charge, and aging status indicator to reflect the battery's actual available energy, making the assessment of the battery's condition more accurate and taking into account the battery's health status;

[0159] formula This is used to calculate the total amount of electricity that all power generation equipment at the base station can replenish to the battery at time t. It comprehensively considers the power generation capacity, charging efficiency, and time period of both new energy power generation equipment (such as solar panels) and fuel power generation equipment (such as diesel generators), ensuring a comprehensive assessment of the contribution of internal power generation capacity.

[0160] formula This is used to calculate the total power consumed by the base station load from the battery at time t. It quantifies the base station's power demand by using the base station's average load power and the length of the time period, making the prediction of power consumption more accurate.

[0161] Through the above technical solution, this application can dynamically and accurately predict the battery life of unattended satellite base stations, overcoming the limitations of traditional methods that rely on static parameters and ignore the dynamic interaction of power changes, power generation equipment contributions, and load consumption. This makes the assessment of internal power supply capacity more accurate, providing a reliable basis for subsequent power supply switching decisions, effectively avoiding the risk of power outages or unnecessary power supply mode switching caused by inaccurate predictions, thereby significantly improving the reliability, stability, and economy of the unattended satellite base station power supply system.

[0162] The proposed solution in this application proposes a battery power supply capability risk index to assess internal power supply risk. However, in its implementation, a specific quantitative method is lacking, which fails to accurately reflect the relationship between battery endurance and critical time, resulting in inaccurate risk assessment.

[0163] In response, this invention further proposes a method for generating the battery power supply capability risk index, specifically including:

[0164] Through the formula: ;

[0165] Generate a risk index for battery power supply capacity ;

[0166] In the formula, This indicates the estimated battery life powered by the internal power supply. This indicates the critical battery life of the internal power supply;

[0167] Among them, the battery power supply capability risk index is a quantitative indicator used to assess the reliability or risk level of battery power supply inside unattended satellite base stations. It reflects the gap between the estimated battery life in its current state and the shortest time required to maintain the normal operation of the base station;

[0168] The critical power supply endurance time refers to the minimum time that the internal power supply system of an unattended satellite base station must maintain to ensure uninterrupted operation of its critical functions (such as communication links and core equipment). For example, this time can be preset by maintenance personnel or system administrators based on factors such as the base station's service level, maintenance response time, and the average time for external power restoration. Alternatively, this critical value can be dynamically adjusted by analyzing historical base station fault data and combining it with the minimum safe operating time of the base station under different fault modes.

[0169] This application's solution introduces the estimated internal power supply duration and the critical internal power supply duration, and uses a mathematical formula to quantify their relationship into a battery power supply capability risk index. This index can dynamically and objectively reflect the actual risk level of battery power supply. Specifically, after the system obtains the estimated internal power supply duration, it compares it with the preset critical internal power supply duration. If the estimated internal power supply duration is greater than or equal to the critical internal power supply duration, it indicates that the battery power supply capability is sufficient to meet the minimum operating requirements of the base station, and the battery power supply capability risk index is calculated as 0. Conversely, if the estimated internal power supply duration is less than the critical internal power supply duration, the battery power supply capability risk index will dynamically increase according to the ratio of the two, accurately quantifying the degree of power insufficiency. This battery power supply capability risk index is then input into the internal power supply analysis model, combined with other factors such as the start-up success rate of the base station's power generation equipment, to jointly generate an estimated internal power supply capability risk value. This quantitative and dynamic risk assessment mechanism enables the monitoring method of unattended satellite base stations to more accurately determine the health status of the internal power supply system, providing a reliable basis for subsequent power supply mode switching decisions, thereby effectively solving the problem of inaccurate risk assessment in traditional methods.

[0170] Through the above technical solution, this application provides a specific and quantitative method to generate a battery power supply capability risk index, making the assessment of internal power supply risks more accurate and objective. This method can dynamically reflect the relationship between battery endurance and critical time, avoiding the inaccurate risk assessment problems caused by the lack of specific quantitative means in traditional methods. By comparing the estimated endurance with the critical endurance time and outputting the risk index, this application can provide more reliable and timely risk warnings for power supply monitoring of unattended satellite base stations, thereby supporting smarter and more optimized power supply switching decisions and effectively ensuring the continuous and stable operation of the base station.

[0171] The proposed solution in this application generates decision values ​​to monitor power supply switching. However, in its implementation, it does not clearly distinguish between risk and cost components, which may lead to incomplete decision-making or failure to achieve an optimal balance between risk and economy.

[0172] For this, please refer to Figure 4 The present invention further proposes a method for generating the decision value, specifically including:

[0173] Step S41: Generate risk-based decision components based on the risk value of the external power supply status and the predicted risk value of the internal power supply capacity;

[0174] Step S42: Generate decision components based on economic costs based on external power supply cost data and internal power supply cost data;

[0175] Step S43: Establish a decision analysis model based on the risk-based decision components and the economic cost-based decision components, and generate decision values;

[0176] The specific expression of the decision analysis model is as follows: ;

[0177] In the expression, D represents the decision value. This represents the risk-based decision weight. This represents the decision component based on economic costs. This represents the weighting coefficient of the risk-based decision component;

[0178] The proposed solution decomposes the power supply decision-making process into two independent but interrelated dimensions: risk assessment and economic assessment. These two dimensions are then weighted and fused to generate a comprehensive decision value. Specifically, the method first generates a risk-based decision component based on the risk value of the external power supply status and the predicted risk value of the internal power supply capacity. This process quantifies the stability of the external power supply and the reliability of the internal backup power supply, avoiding the limitations of relying solely on instantaneous threshold judgments and enabling a more accurate assessment of the potential risks of the current power supply method. Simultaneously, the method also generates an economic cost-based decision component based on external and internal power supply cost data. This allows the system to fully consider the operating costs of different power supply methods when making power supply switching decisions, thereby optimizing long-term operational economic benefits while ensuring the reliable operation of the base station. Finally, a decision analysis model is established to weightedly combine the risk-based and economic cost-based decision components to generate a comprehensive decision value. This decision analysis model, by introducing weighting coefficients, allows for flexible adjustment of the priority of risk and economic considerations in decision-making based on actual needs. For example, during extreme weather or critical missions, the weight of risk factors can be increased to ensure power supply reliability; while in daily operation, the weight of economic considerations can be appropriately increased to reduce operating costs. This hierarchical, weighted, and comprehensive decision-making mechanism enables power supply monitoring of unattended satellite base stations to achieve more intelligent, comprehensive, and adaptive power supply mode switching, effectively solving the problems of incomplete decision-making and inability to balance risk and economic considerations in traditional methods.

[0179] Through the above technical solution, this application effectively addresses the problems of incomplete decision-making and the inability to achieve an optimal balance between risk and economy in traditional unattended satellite base station power supply monitoring. Specifically, by decomposing the decision-making process into risk-based and cost-based decision components and weighted fusion, the system can comprehensively assess the stability of external power supply and the reliability of internal power supply when switching power supply modes. This avoids decision lag or malfunctions caused by relying solely on instantaneous threshold judgments. Furthermore, it fully considers the operating and switching costs of different power supply modes, thereby optimizing operating costs while ensuring the reliability of base station power supply. This comprehensive decision-making mechanism significantly improves the intelligence level and scientific nature of unattended satellite base station power supply monitoring, ensuring the long-term stable operation and economic benefits of the base station.

[0180] The proposed solution in this application uses a decision value to combine risk and cost factors for power supply switching decisions. However, in its implementation, the lack of specific quantification methods may lead to inaccurate decisions or an inability to effectively balance risks and costs.

[0181] In response, this invention further proposes a method for generating the risk-based decision components, specifically including:

[0182] Through the formula: ;

[0183] Generate risk-based decision components ;

[0184] In the formula, This represents the estimated risk value of internal power supply capacity. This represents the estimated risk value of internal power supply capacity. This represents the weighting coefficient for the estimated risk of internal power supply capacity;

[0185] The specific methods for generating the decision components based on economic costs include:

[0186] Through the formula: ;

[0187] Generate decision components based on economic costs. ;

[0188] In the formula, This represents the unit cost of external power supply. This represents the unit cost of internal power supply. This represents the threshold for the difference in power supply costs. This represents the cost of a single power supply switching operation. This represents the cost threshold for a single power supply switching operation;

[0189] Specifically, the risk-based decision component aims to quantify the degree of risk faced by the power supply system, taking into account the risks of both external and internal power supply.

[0190] The weighting coefficient for external power supply status risk is used to adjust the relative importance of external power supply risk in the overall risk assessment. It can be set empirically by maintenance personnel based on factors such as the grid stability and historical outage frequency of the base station's geographical location, or adaptively optimized and adjusted through machine learning algorithms based on historical decision-making effects and actual operating data. This allows the system to flexibly adjust the proportion of external power supply risk and internal power supply risk in decision-making according to the actual operating environment and strategy preferences.

[0191] The decision component based on economic cost aims to quantify the differences in economic costs among different power supply methods and the cost impact of switching operations. It can calculate costs by acquiring real-time data such as external electricity prices, fuel costs of internal power generation equipment, and maintenance costs, or by using a pre-set cost model combined with the switching frequency and expected lifespan of the power supply method to conduct long-term cost assessments. This provides economic dimension input to the decision analysis model, ensuring that decisions take operating costs into account.

[0192] The unit cost of external power supply refers to the cost required to obtain a unit of electricity from an external power grid. It can be calculated or predicted by reading real-time electricity price data from the power grid company, including peak-valley pricing and tiered pricing, or by averaging or forecasting historical electricity bill data, to reflect the economic overhead of using external power supply.

[0193] The unit cost of internal power supply refers to the cost required to supply a unit of electricity through the base station's internal backup power supply (such as generators or batteries). It can be estimated by calculating the generator fuel consumption cost, battery charging and discharging loss cost, equipment maintenance cost, etc., or by using a preset internal power supply cost model combined with parameters such as fuel price and equipment efficiency, to reflect the economic expenditure of using internal backup power supply.

[0194] The power supply cost difference threshold is a benchmark value used to normalize the cost difference between external and internal power supply. It can be set by the system administrator or maintenance personnel based on factors such as the base station's average daily power consumption and the local average electricity price, or by statistical analysis of historical cost data to determine a reasonable range of cost differences as the threshold. This ensures the consistency of the dimensions of cost differences in decision-making and avoids an imbalance in the impact of excessively large or small values ​​on decision-making.

[0195] The cost of a single power supply handover operation refers to the expense incurred in performing a power supply mode switch operation (such as switching from external power supply to internal power supply, or vice versa). This cost can include implicit costs such as equipment wear and tear, system outage risks, and manual maintenance, as well as explicit costs such as generator start-up fuel consumption. Alternatively, it can be estimated using historical maintenance records and equipment lifespan data, taking into account the potential impact of the handover operation on the base station's service quality, in order to quantify the economic cost of the handover operation itself and avoid frequent system handovers due to minor cost differences.

[0196] The cost threshold for a single power supply handover operation serves as a benchmark value for normalizing the cost of such operations. It can be set based on factors such as the base station's operational strategy, tolerance for handover frequency, and the average cost of handover operations. Alternatively, a reasonable threshold can be determined through statistical analysis of historical handover cost data to ensure consistency in the dimensionality of handover cost within the decision-making process and to allow the system to adjust based on its sensitivity to handover costs.

[0197] This application's solution quantifies risk-based and cost-based decision components by introducing specific mathematical formulas. The risk-based decision component is obtained by a weighted sum of the external power supply status risk value and the estimated internal power supply capacity risk value. This makes the risk assessment more comprehensive, covering potential instability factors in the power supply system and avoiding decision bias caused by relying on a single risk indicator. The cost-based decision component calculates and normalizes the difference between the unit cost of external and internal power supply, while also considering and normalizing the cost of a single power supply switching operation. This calculation method quantifies the power supply cost difference and the economic impact of switching operations, ensuring that the decision-making process comprehensively considers long-term operating costs and immediate operational expenses, preventing resource waste or frequent switching due to neglecting economic factors. Subsequently, the risk-based and cost-based decision components are input into the decision analysis model to generate the final decision value. This mechanism enables the decision value to accurately reflect the risk and economic cost of the power supply status, thereby achieving an optimal balance in power supply switching decisions.

[0198] Through the above technical solution, this application provides a precise quantitative method for power supply monitoring of unattended satellite base stations, effectively solving the problems of inaccurate decision-making or inability to effectively balance risk and cost in traditional solutions. This solution generates risk-based and cost-based decision components by performing refined modeling and calculation of external power supply status risk values, internal power supply capacity estimated risk values, external power supply cost data, and internal power supply cost data. This allows the decision-making process to not only comprehensively assess power supply reliability but also consider operational economy, thereby achieving an optimal balance between power supply reliability and economy. Furthermore, by introducing configurable weight coefficients and thresholds, this solution enhances the flexibility and adaptability of decision-making, enabling adjustments based on the actual operating environment and strategy preferences of different base stations. This avoids unnecessary switching or cost-impacted impacts on efficiency, significantly improving the intelligence level and operational efficiency of power supply monitoring for unattended satellite base stations.

[0199] Please see Figure 5 The present invention also proposes a monitoring system for an unattended satellite base station, characterized in that the system is used to execute the above-mentioned monitoring method for an unattended satellite base station, specifically including:

[0200] The data acquisition unit 10 is used to acquire external power supply status characteristic data, internal power supply capability characteristic data, external power supply cost data, and internal power supply cost data of the satellite base station.

[0201] The external power supply analysis unit 20 is used to establish an external power supply analysis model and generate an external power supply status risk value based on the external power supply status characteristic data; wherein, the external power supply status characteristic data includes the external power supply voltage value and the external power supply current value;

[0202] Internal power supply analysis unit 30 is used to establish an internal power supply analysis model based on internal power supply capacity characteristic data and generate an estimated risk value of internal power supply capacity.

[0203] The decision analysis unit 40 is used to establish a decision analysis model and generate decision values ​​based on the external power supply status risk value, the internal power supply capacity estimated risk value, external power supply cost data, and internal power supply cost data.

[0204] The monitoring unit 50 is used to monitor the unattended satellite base station based on the decision value.

[0205] For preferred options, please refer to [link / reference]. Figure 6 The present invention further proposes that the external power supply analysis unit 20 specifically includes:

[0206] Voltage analysis module 21 is used to generate a voltage fluctuation index based on the external power supply voltage value;

[0207] The current analysis module 22 is used to generate a current overload index based on the external power supply current value;

[0208] The state risk value generation module 23 is used to establish an internal power supply analysis model based on the voltage fluctuation index and the current overload index, and generate external power supply state risk values.

[0209] For preferred options, please refer to [link / reference]. Figure 7 The present invention further proposes that the internal power supply analysis unit 30 specifically includes:

[0210] The battery life estimation module 31 is used to generate an estimated battery life based on the internal power supply capability characteristic data; wherein, the internal power supply capability characteristic data includes the remaining battery capacity percentage and the power generation power of the internal power generation components.

[0211] The battery power supply capacity analysis module 32 is used to generate a battery power supply capacity risk index based on the estimated driving time of the internal power supply.

[0212] The internal power supply capacity prediction risk value generation module 33 is used to establish an internal power supply analysis model based on the battery power supply capacity risk index and generate an internal power supply capacity prediction risk value.

[0213] For preferred options, please refer to [link / reference]. Figure 8 The present invention further proposes that the decision analysis unit 40 specifically includes:

[0214] The risk decision component analysis module 41 is used to generate risk-based decision components based on the risk value of the external power supply status and the estimated risk value of the internal power supply capacity.

[0215] The economic decision component analysis module 42 is used to generate decision components based on economic costs according to external power supply cost data and internal power supply cost data.

[0216] The comprehensive analysis module 43 is used to establish a decision analysis model based on risk-based decision components and economic cost-based decision components, and generate decision values.

[0217] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A monitoring method for an unattended satellite base station, characterized in that, The method specifically includes: Acquire external power supply status characteristic data, internal power supply capacity characteristic data, external power supply cost data, and internal power supply cost data for satellite base stations; An external power supply analysis model is established based on the external power supply status characteristic data to generate an external power supply status risk value; the external power supply status characteristic data includes the external power supply voltage value and the external power supply current value. An internal power supply analysis model is established based on the internal power supply capacity characteristic data, and an estimated risk value for internal power supply capacity is generated. A decision analysis model is established based on the external power supply status risk value, the internal power supply capacity estimated risk value, external power supply cost data, and internal power supply cost data to generate decision values. Based on the decision value, unattended satellite base stations are monitored.

2. The monitoring method for an unattended satellite base station according to claim 1, characterized in that, The specific methods for generating the external power supply status risk value include: A voltage fluctuation index is generated based on the external power supply voltage value; The current overload index is generated based on the external power supply current value; An external power supply analysis model is established based on the voltage fluctuation index and the current overload index to generate an external power supply status risk value.

3. The monitoring method for an unattended satellite base station according to claim 2, characterized in that, The voltage fluctuation index is generated in the following ways: Through the formula: ; Generation voltage fluctuation index ; In the formula, This represents the external supply voltage value at time k. This represents the external supply voltage value at time k-1, where N is the number of data points in the time window. This indicates the rated voltage of the external power supply; The method for generating the current overload index specifically includes: Through the formula: ; Generation current overload index ; In the formula, This represents the external supply current value at time k. This represents the maximum input current allowed for normal operation of the base station.

4. The monitoring method for an unattended satellite base station according to claim 2, characterized in that, The specific expression of the external power supply analysis model is as follows: ; In the expression, This indicates the risk value of the external power supply status. This indicates the voltage qualification rate of the external power supply. This represents the voltage fluctuation index. This indicates the current overload index. , , All are weighting coefficients, and ; Among them, the voltage qualification rate of the external power supply The specific methods of obtaining it include: Through the formula: ; Generating the voltage qualification rate of external power supply ; In the formula, This represents the external supply voltage value at time k. This indicates the minimum voltage required for the base station to operate normally. This indicates the highest voltage at which the base station operates normally. This is a conditional indicator function.

5. The monitoring method for an unattended satellite base station according to claim 1, characterized in that, The specific methods for generating the estimated risk value of the internal power supply capacity include: Based on the internal power supply capacity characteristic data, the estimated internal power supply range is generated; the internal power supply capacity characteristic data includes the remaining battery capacity percentage and the power generation capacity of the internal power generation components. Based on the estimated range of internal power supply, a battery power supply capability risk index is generated. An internal power supply analysis model is established based on the battery power supply capacity risk index to generate an estimated risk value for internal power supply capacity. The specific expression of the internal power supply analysis model is as follows: In the expression, This represents the estimated risk value of internal power supply capacity. This represents the risk index of the battery's power supply capacity. This indicates the startup success rate of the base station's power generation equipment. This represents the weighting coefficient for the risk of battery power supply.

6. The monitoring method for an unattended satellite base station according to claim 5, characterized in that, The method for generating the estimated battery life using the internal power supply specifically includes: through the formula: Generate estimated battery life based on internal power supply. ; In the formula, the internal power supply estimates the battery life. This refers to the maximum number of consecutive hours, T, where the battery charge is positive at the end of all time periods. It represents the set of positive integers, and t represents the time period index. This represents the available battery charge at the start of time period t+1; Wherein, the available power of the battery at the beginning of the (t+1)th time period. The specific methods of obtaining it include: Through the formula: ; Generate the available battery charge at the start of time period t+1. ; In the formula, This represents the available battery charge at time t. This represents the total amount of electricity that all the power generation equipment at the base station can replenish for the battery at time t. This represents the total amount of electricity consumed by the base station load from the battery at time t. This indicates the rated capacity of the battery. This represents the percentage of remaining battery charge at time t. This indicates the degree of aging of the battery. This represents the average power generation of new energy power generation equipment at time t. This indicates the charging efficiency of the new energy power generation equipment for charging the battery. This represents the duration at time t. This represents the average power output of the fuel-powered generator at time t. This indicates the charging efficiency of the fuel cell power generation equipment in charging the battery. This represents the average load power of the base station at time t.

7. The monitoring method for an unattended satellite base station according to claim 5, characterized in that, The specific methods for generating the battery power supply capacity risk index include: Through the formula: ; Generate a risk index for battery power supply capacity ; In the formula, This indicates the estimated battery life powered by the internal power supply. This indicates the critical battery life of the internal power supply.

8. The monitoring method for an unattended satellite base station according to claim 1, characterized in that, The specific methods for generating the decision values ​​include: Based on the risk value of the external power supply status and the estimated risk value of the internal power supply capacity, risk-based decision components are generated; Based on external power supply cost data and internal power supply cost data, generate decision components based on economic costs; A decision analysis model is established based on risk-based decision components and economic cost-based decision components to generate decision values; The specific expression of the decision analysis model is as follows: ; In the expression, D represents the decision value. This represents the risk-based decision weight. This represents the decision component based on economic costs. This represents the weighting coefficient of the risk-based decision component.

9. The monitoring method for an unattended satellite base station according to claim 8, characterized in that, The specific methods for generating the risk-based decision components include: Through the formula: ; Generate risk-based decision components ; In the formula, This indicates the risk value of the external power supply status. This represents the estimated risk value of internal power supply capacity. This represents the weighting coefficient for the risk of external power supply status; The specific methods for generating the decision components based on economic costs include: Through the formula: ; Generate decision components based on economic costs. ; In the formula, This represents the unit cost of external power supply. This represents the unit cost of internal power supply. This represents the threshold for the difference in power supply costs. This represents the cost of a single power supply switching operation. This represents the cost threshold for a single power supply switching operation.

10. A monitoring system for an unattended satellite base station, characterized in that, The system is used to perform the monitoring method for unattended satellite base stations as described in any one of claims 1-9, specifically including: The data acquisition unit is used to acquire external power supply status characteristic data, internal power supply capability characteristic data, external power supply cost data, and internal power supply cost data of the satellite base station; The external power supply analysis unit is used to establish an external power supply analysis model and generate an external power supply status risk value based on external power supply status characteristic data; wherein, the external power supply status characteristic data includes external power supply voltage value and external power supply current value; The internal power supply analysis unit is used to establish an internal power supply analysis model based on the internal power supply capacity characteristic data and generate an estimated risk value for the internal power supply capacity. The decision analysis unit is used to establish a decision analysis model and generate decision values ​​based on the external power supply status risk value, the internal power supply capacity estimated risk value, external power supply cost data, and internal power supply cost data. The monitoring unit is used to monitor unattended satellite base stations based on decision values.

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