Satellite communication fault monitoring method and device, electronic equipment, storage medium and program product

Abstract

This invention provides a satellite communication fault monitoring method, device, electronic equipment, storage medium, and program product. The method involves: collecting sampled values ​​of multiple performance indicators of the network element to be monitored at preset sampling periods; for each performance indicator, determining multiple threshold intervals in real time using all sampled values ​​of the performance indicator in the previous sliding time window; and performing real-time fault monitoring based on the multiple threshold intervals and the sampled values ​​of the performance indicator in the current sliding time window. By dynamically calculating multiple threshold intervals for each indicator in each sliding time window, monitoring of each indicator is achieved, avoiding false alarms and missed alarms caused by using a uniform static threshold.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and more specifically, to a satellite communication fault monitoring method, device, electronic device, storage medium, and program product. Background Technology

[0002] The Network Management System (NMS) needs to monitor a large number of network elements covering the space segment, ground segment, and user terminals in real time, continuously collect link quality indicators, and trigger fault alarms and notify maintenance personnel to intervene when the indicators exceed preset alarm thresholds.

[0003] The current mainstream implementation method generally adopts a unified static fixed threshold alarm mechanism, but this method is prone to false alarms and missed alarms. Summary of the Invention

[0004] The purpose of this invention is to provide a satellite communication fault monitoring method, device, electronic device, storage medium, and program product to improve the problems existing in the prior art.

[0005] The embodiments of the present invention can be implemented as follows: In a first aspect, the present invention provides a satellite communication fault monitoring method, comprising: At preset sampling intervals, sampled values ​​of multiple performance indicators of the network element to be monitored are collected; For each performance indicator, multiple threshold intervals are determined in real time using all sampled values ​​of the performance indicator in the previous sliding time window; the window length of the sliding time window is equal to the orbital period of the satellite currently connected to the network element to be monitored. Real-time fault monitoring is performed based on the multiple threshold intervals and the sampled values ​​of the performance indicators within the current sliding time window.

[0006] In an optional implementation, the step of determining multiple threshold intervals in real time using all sampled values ​​of the performance metric in the previous sliding time window includes: Based on all sampled values ​​of the performance index within the previous sliding time window, calculate the first mean and the first standard deviation; Based on the orbital altitude type of the satellite currently connected to the network element to be monitored, the previous sliding time window is divided into multiple monitoring periods; For each monitoring period, based on all sampled values ​​of the performance index within the monitoring period, calculate the second mean and second standard deviation corresponding to the monitoring period; Based on the first mean, the first standard deviation, and the second mean and the second standard deviation corresponding to the monitoring period, the threshold range of the performance index in the monitoring period is calculated by weighted summation.

[0007] In an optional implementation, when the orbital altitude type is low Earth orbit or medium Earth orbit, the orbital phase of the satellite currently connected to the network element under monitoring is also collected at each sampling time. The step of dividing the previous sliding time window into multiple monitoring periods according to the orbital altitude type of the satellite currently connected to the network element under monitoring includes: Starting from the orbital phase corresponding to the first sampled value of the performance index within the previous sliding time window, the first monitoring period is determined at a preset phase difference interval, and the last sampled value within the first monitoring period is obtained. Starting from the orbital phase corresponding to the last sampled value of the performance index in the nth monitoring period, the (n+1)th monitoring period is determined at a preset phase difference, and the last sampled value within the (n+1)th monitoring period is obtained. , This indicates the number of monitoring periods within the sliding time window; When the orbital altitude type is geostationary orbit, the step of dividing the previous sliding time window into multiple monitoring periods according to the orbital altitude type of the satellite currently connected to the network element to be monitored includes: The previous sliding time window is divided into multiple monitoring periods according to fixed time intervals.

[0008] In an optional implementation, the step of calculating the threshold range of the performance index within the monitoring period by weighted summation based on the first mean, the first standard deviation, and the second mean and the second standard deviation corresponding to the monitoring period includes: Calculate the first threshold interval based on the first mean and the first standard deviation; The second threshold interval is calculated based on the second mean and second standard deviation corresponding to the monitoring period. The first threshold interval and the second threshold interval are weighted and summed to obtain the threshold interval of the performance index during the monitoring period.

[0009] In an optional implementation, the threshold interval is calculated as follows:

[0010] In the formula, , Representing the first The lower and upper limits of the threshold range for each monitoring period; These are the weighting coefficients. These are the first sensitivity coefficient and the second sensitivity coefficient, respectively. , These represent the first mean and the first standard deviation, respectively. , They represent the first The second mean and the second standard deviation for each monitoring period.

[0011] In an optional implementation, each threshold interval corresponds to a monitoring period within the sliding time window. The step of performing real-time fault monitoring based on the plurality of threshold intervals and the sampled value of the performance index within the current sliding time window includes: Within the current sliding time window, each time the latest sampled value of the performance indicator is obtained, the target monitoring period in which the latest sampled value is located is determined; If the latest sampled value exceeds the threshold range corresponding to the target monitoring period, then record the performance indicator exceeding the limit event. If the number of out-of-limit events occurring in the performance index within M consecutive sampling periods exceeds a set threshold, an anomaly warning will be triggered. Based on the P sample values ​​before the latest sample value, calculate the rate of change of the performance index. If the absolute value of the rate of change exceeds a preset multiple of the standard deviation of all sample values ​​in the target monitoring period of the previous sliding time window, a degradation warning is triggered. If an abnormal warning is triggered continuously within the set time period, a fault alarm will be issued.

[0012] In a second aspect, the present invention provides a satellite communication fault monitoring device, comprising: The data acquisition module is used to collect sampled values ​​of multiple performance indicators of the network element to be monitored at preset sampling periods. The threshold calculation module is used to determine multiple threshold intervals in real time for each performance index using all sampled values ​​of the performance index in the previous sliding time window; the window length of the sliding time window is equal to the orbital period of the satellite currently connected to the network element to be monitored. The monitoring module is used to perform real-time fault monitoring based on the multiple threshold intervals and the sampled values ​​of the performance indicators in the current sliding time window.

[0013] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory stores a software program, and when the electronic device is running, the processor executes the software program to implement the method described in the first aspect above.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0015] Fifthly, the present invention provides a computer program product that, when executed by a processor, implements the method described in the first aspect above.

[0016] Compared with existing technologies, this invention provides a satellite communication fault monitoring method, device, electronic device, storage medium, and program product. The method involves: collecting sampled values ​​of multiple performance indicators of the network element to be monitored at preset sampling periods; for each performance indicator, determining multiple threshold intervals in real time using all sampled values ​​of the performance indicator in the previous sliding time window; and performing real-time fault monitoring based on the multiple threshold intervals and the sampled values ​​of the performance indicator in the current sliding time window. By dynamically calculating multiple threshold intervals for each indicator in each sliding time window, monitoring of each indicator is achieved, avoiding false alarms and missed alarms caused by using a uniform static threshold. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is one of the flowcharts illustrating a satellite communication fault monitoring method provided in an embodiment of the present invention.

[0019] Figure 2 This is a second flowchart illustrating a satellite communication fault monitoring method provided in an embodiment of the present invention.

[0020] Figure 3 This is the third flowchart illustrating a satellite communication fault monitoring method provided in an embodiment of the present invention.

[0021] Figure 4 This is a schematic diagram of a satellite communication fault monitoring device provided in an embodiment of the present invention.

[0022] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0024] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0025] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0026] Furthermore, the terms "first" and "second" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0027] It should be noted that, where there is no conflict, the features in the embodiments of the present invention can be combined with each other.

[0028] The Network Management System (NMS) needs to monitor a large number of network elements covering the space segment, ground segment, and user terminals in real time, continuously collecting link quality indicators, including Bit Error Rate (BER), Carrier-to-Noise Power Spectral Density Ratio (C / N0), Received Signal Strength Indicator (RSSI), signal propagation delay, and jitter. When any indicator exceeds a preset alarm threshold, the system triggers a fault alarm and notifies maintenance personnel to intervene.

[0029] Current mainstream implementations generally adopt a static fixed threshold alarm mechanism. That is, in the early stages of system operation, maintenance engineers manually configure a set of global alarm thresholds based on equipment specifications or experience, and then use them for a long time. Taking a typical geostationary Earth Orbit (GEO) satellite communication system as an example, its link availability monitoring usually sets a uniform BER alarm threshold, which is uniformly applied to all network nodes and is not dynamically adjusted according to time, location or load status.

[0030] However, the static fixed threshold alarm mechanism has the following objective drawbacks: (1) High false alarm rate: Satellite channel quality itself has significant periodic and time-varying fluctuations, including periodic changes in elevation angle affected by the Earth's rotation, rain attenuation period caused by rain during the rainy season (especially significant in the Ka band), and carrier interference ratio decrease caused by peak user traffic. The above fluctuations are within the normal operating range, and may frequently exceed the limits under static threshold mode, triggering a large number of false alarms, causing alarm fatigue of operation and maintenance personnel, and real fault signals are easily drowned out; (2) High risk of missed detection: Fixed thresholds are usually set based on worst-case operating conditions, resulting in a loose threshold. They lack the ability to detect early fault signs such as slow degradation of channel quality and long-term lingering below the threshold. Faults are often only detected after they evolve into real faults, missing the best warning window. (3) Differences among multiple sites cannot be taken into account: Due to differences in geographical location, climate zone and the type of services carried, the normal signal characteristics of ground sites vary significantly. A unified threshold cannot be adapted to all sites. In actual operation and maintenance, different parameters can only be configured manually for different sites, which is costly and prone to errors. (4) Lack of adaptive capability: Equipment aging, frequency planning adjustment or change of operation mode will cause long-term drift of signal characteristic baseline, static threshold cannot be automatically sensed and adjusted accordingly, and the system alarm accuracy will continue to decline over time.

[0031] Based on the discovery of the aforementioned technical problems, the inventors, through creative labor, proposed the following technical solutions to solve or improve these problems. It should be noted that the deficiencies in the solutions of the prior art are all results derived by the inventors after practical experience and careful research. Therefore, the discovery process of the aforementioned problems and the solutions proposed in the embodiments of this application below should be considered contributions made by the inventors to this application during the inventive process, and should not be construed as technical content known to those skilled in the art.

[0032] This invention provides a satellite communication fault monitoring method that abandons manually configured global static thresholds. Instead, it dynamically calculates multiple threshold ranges for each performance indicator of each network element within each sliding time window, thereby monitoring each indicator and avoiding false alarms and missed alarms caused by using a uniform static threshold.

[0033] This satellite communication fault monitoring method can be applied to a monitoring server in a satellite network management platform. This monitoring server can simultaneously monitor multiple network elements of the same or different device types. The device types of these network elements can be, but are not limited to, those in the satellite communication system: debuggers, demodulators, ground stations, clock devices, satellite terminals, etc. The following section provides a detailed description of the specific implementation of this invention using one network element as the monitoring target.

[0034] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a satellite communication fault monitoring method provided in an embodiment of the present invention. The method includes the following steps S1 to S3.

[0035] S1. Collect sampled values ​​of multiple performance indicators of the network element to be monitored at preset sampling intervals.

[0036] In this embodiment, the multi-dimensional performance indicators of the network elements to be monitored can be continuously collected through the southbound interface of the satellite network management platform according to a preset sampling period (30 seconds by default, but can also be configured and modified), forming a time-series data stream.

[0037] The southbound interface serves as an interface for "issuing configurations, collecting status data, and executing control commands," enabling the management platform (controller / network management system) to connect to, control, and monitor underlying physical devices (such as satellite payloads, inter-satellite link terminals, ground gateway station radio frequency equipment, and onboard switching units).

[0038] The performance indicators may include, but are not limited to, at least one of the following: downlink carrier-to-noise ratio C / N0 (in dB·Hz), received signal strength indication RSSI (in dBm), forward bit error rate BER (dimensionless), signal propagation delay RTT (in ms), and Doppler frequency shift (in Hz), etc.

[0039] S2. For each performance metric, use all sampled values ​​of the performance metric in the previous sliding time window to determine multiple threshold intervals in real time.

[0040] In this embodiment, within the current sliding time window, for each performance metric, multiple threshold intervals are determined in real time using all sampled values ​​of that performance metric in the previous sliding time window.

[0041] The window length of the sliding time window can be set to be equal to the orbital period of the satellite currently connected to the network element being monitored.

[0042] S3. Real-time fault monitoring is performed based on the sampled values ​​of multiple threshold intervals and performance indicators within the current sliding time window.

[0043] In this embodiment, for each performance indicator, multiple threshold intervals are calculated using all sampled values ​​of the performance indicator in the previous sliding time window within the current sliding time window. Then, using the multiple threshold intervals as a reference, the sampled values ​​of the performance indicator in the current sliding time window are monitored in real time to determine whether there is a fault.

[0044] The satellite communication fault monitoring method provided in this invention collects sampled values ​​of multiple performance indicators of the network element to be monitored at preset sampling periods. Then, for each performance indicator, multiple threshold intervals are determined in real time using all sampled values ​​of the performance indicator in the previous sliding time window. Based on the multiple threshold intervals and the sampled values ​​of the performance indicator in the current sliding time window, real-time fault monitoring is performed. By dynamically calculating multiple threshold intervals for each indicator in each sliding time window, each indicator is monitored, avoiding false alarms and missed alarms caused by using a uniform static threshold.

[0045] Among the optional implementation methods, in Figure 1 Based on this, please refer to Figure 2 In step S2, the process of "using all sampled values ​​of performance indicators in the previous sliding time window to determine multiple threshold intervals in real time" can include the following sub-steps S21 to S24.

[0046] S21. Based on all sampled values ​​of the performance index within the previous sliding time window, calculate the first mean and the first standard deviation.

[0047] For example, assuming the sliding time window has a window length of 90 minutes and a sampling period of 30 seconds, then within the previous sliding time window, including the sampled values ​​of performance index A collected at 180 sampling times, the first mean and the first standard deviation can be calculated based on the 180 sampled values ​​of performance index A, thereby capturing the trend drift of performance index A over time.

[0048] S22. Based on the orbital altitude type of the satellite currently connected to the network element to be monitored, divide the previous sliding time window into multiple monitoring periods.

[0049] In this embodiment, because satellite communication has orbital periodicity, it needs to be divided into multiple monitoring periods for subsequent calculations. Here, a monitoring period is a relative time period within a sliding time window, and each threshold interval corresponds to a monitoring period within the sliding time window.

[0050] Among them, orbital altitude types can be divided into low Earth orbit (i.e., low Earth orbit or near Earth orbit), medium Earth orbit (medium Earth orbit), and geostationary orbit.

[0051] In an optional scenario, when the orbital altitude type is low Earth orbit (LEO) or medium Earth orbit (MEO), the elevation, azimuth, slant range, and Doppler shift of these two types of satellites change drastically and regularly. Therefore, in each orbit, the relative geometric relationship between the monitored network element and the satellite strictly repeats according to the orbital phase angle (e.g., true anomaly angle / right ascension of the ascending node + mean anomaly angle). In this case, the present invention also collects the orbital phase of the satellite currently connected to the monitored network element at each sampling moment of the interval sampling period. The orbital phase can be calculated based on ephemeris data, and this orbital phase is added to the sampled value at that sampling moment and stored together.

[0052] At this point, the implementation process of step S22 is as follows: Step 1: Starting from the orbital phase corresponding to the first sampled value of the performance index within the previous sliding time window, determine the first monitoring period at a preset phase difference and obtain the last sampled value within the first monitoring period; Step 2: Starting from the orbital phase corresponding to the last sampled value of the performance index in the nth monitoring period, determine the (n+1)th monitoring period at preset phase differences and obtain the last sampled value within the (n+1)th monitoring period. , This indicates the number of monitoring periods within the sliding time window.

[0053] The preset phase difference can be: 30 or etc. are not specified here.

[0054] For example, with a preset phase difference as For example, then within the entire orbital period (i.e., the sliding time window), it should include... Each monitoring period. If the orbital phase corresponding to the first sampled value of performance index A within the previous sliding time window is... Therefore, we need to start from the first sample value and search backwards for the orbital phase. The sampled values, the period between these two sampled values ​​is the first monitoring time period, and the orbital phase is... The sampled value is the last sampled value of the first monitoring period.

[0055] Starting from the last sampled value of the first monitoring period, from the orbital phase of Start searching for orbital phase backwards. The sampled values, the period between these two sampled values ​​is the second monitoring time period, and the orbital phase is... The sampled value is the last sampled value of the second monitoring period; Starting from the last sampled value of the second monitoring period, from orbital phase 35... Start searching for orbital phase backwards. The sampled values, the interval between these two sampled values ​​is the third monitoring period, and the orbital phase is... The sampled value is the last sampled value of the third monitoring period; Similarly, this process continues until the 24th monitoring period within the previous sliding time window is determined. It should be noted that this example is merely illustrative and is not intended to be limiting.

[0056] In one optional scenario, when the orbital altitude type is geostationary orbit, since the elevation / azimuth angle of a geostationary orbit (GEO) satellite is basically constant and the orbital period = sidereal day ≈ 23h56m, that is, the orbital period is regarded as 24h, in this case, the previous sliding time window can be directly divided into multiple monitoring periods according to a fixed time interval.

[0057] For example, the previous 24-hour sliding time window can be divided into 24 monitoring periods, each hour, or the previous 24-hour sliding time window can be divided into 12 monitoring periods, each two hours. It should be noted that this is merely an example, and the size of the fixed time interval is not limited here.

[0058] S23. For each monitoring period, based on all sampled values ​​of the performance index within the monitoring period, calculate the second mean and second standard deviation corresponding to the monitoring period.

[0059] S24. Based on the first mean, the first standard deviation, and the second mean and the second standard deviation corresponding to the monitoring period, calculate the threshold range of the performance index during the monitoring period by weighted summation.

[0060] In this embodiment, for the first time window of the previous sliding time window During the monitoring period, performance index A can be used as the basis for the monitoring in the first monitoring period. Calculate the number of samples collected within the monitoring period. The second mean and second standard deviation corresponding to each monitoring period Among them, the computational performance index A is in the first... The process of setting threshold intervals for a given monitoring period may include: (1) Calculate the first threshold interval based on the first mean and the first standard deviation; (2) Based on the first The second threshold interval is calculated based on the second mean and second standard deviation corresponding to each monitoring period. (3) Perform a weighted summation on the first threshold interval and the second threshold interval to obtain the performance index A in the first threshold interval. Threshold range for each monitoring period.

[0061] Optionally, the threshold interval can be calculated as follows:

[0062] In the formula, , Representing the first The lower and upper limits of the threshold range for each monitoring period; These are weighting coefficients, which can be flexibly configured according to business scenarios; These are the first and second sensitivity coefficients, with initial default values ​​of 3.0 and 2.5 respectively, corresponding to approximately 99.7% and 98.8% confidence intervals for normal distributions. It can also be flexibly configured according to business scenarios; the smaller the value, the more sensitive the alarm, and the larger the value, the more conservative the alarm.

[0063] , These represent the first mean and the first standard deviation of the previous sliding time window, respectively. , These represent the times within the previous sliding time window. The second mean and second standard deviation for each monitoring period.

[0064] This invention considers the temporal variation trend of each performance indicator and the periodic variation trend of the track within a sliding time window, and calculates the threshold range corresponding to each monitoring period by combining the mean and standard deviation of the two aspects. This ensures the dynamic variation characteristics of the threshold range of each performance indicator of a single network element under monitoring, and reduces false alarms and missed alarms caused by subsequent fluctuations in the track periodic signal.

[0065] In optional implementations, this invention can also achieve multi-level early warning. Figure 1 Based on this, please refer to Figure 3 For each performance metric, the implementation steps of step S3 above may include the following sub-steps S31 to S35.

[0066] S31. Within the current sliding time window, each time the latest sampled value of the performance indicator is obtained, determine the target monitoring period in which the latest sampled value is located.

[0067] S32. If the latest sampled value exceeds the threshold range corresponding to the target monitoring period, record the performance index exceeding the limit event.

[0068] If the latest sampled value is greater than the upper limit of the threshold interval corresponding to the target monitoring period, or less than the lower limit of the threshold interval corresponding to the target monitoring period, then a performance indicator exceeding the limit event is recorded.

[0069] S33. If the number of out-of-limit events in a performance indicator exceeds the set threshold within M consecutive sampling periods, an abnormal warning will be triggered.

[0070] The threshold can be set to M×0.6, where M can be 5 or 10, and is not limited here. If the number of out-of-limit events of performance indicator A exceeds the set threshold within M consecutive sampling periods, it indicates that performance indicator A is continuously abnormal, triggering an anomaly warning.

[0071] S34. Based on the P sample values ​​before the latest sample value, calculate the rate of change of the performance index. If the absolute value of the rate of change exceeds the preset multiple of the standard deviation of all sample values ​​in the target monitoring period of the previous sliding time window, a deterioration warning is triggered.

[0072] In this embodiment, for performance index A, the rate of change of performance index A is calculated based on the P sample values ​​(e.g., 10 or 20) before its latest sample value. If the absolute value of the rate of change exceeds a preset multiple (e.g., 1.5 times) of the standard deviation of all sample values ​​in the target monitoring period of the previous sliding time window, a degradation warning is triggered.

[0073] For example, if the target monitoring period is the 10th monitoring period, then the standard deviation of all sampled values ​​of performance index A within the 10th monitoring period of the previous sliding time window is: If the absolute value of the rate of change exceeds If the value is 1.5 times that of the previous value, it indicates that performance indicator A is showing a continuous deterioration trend. Even if performance indicator A has not yet exceeded the limit, a deterioration warning is triggered, thus achieving early warning of the fault.

[0074] S35. If an abnormal warning is triggered continuously within the set time period, a fault alarm will be issued.

[0075] In this embodiment, the set duration can be 30 minutes, 1 hour, or 2 hours, etc. The set duration can be flexibly set according to the actual situation and is not limited here. Taking 1 hour as an example, if an abnormal warning is triggered continuously for 1 hour, it means that performance indicator A is in a long-term over-limit state, and the network element to be monitored may be in a fault state.

[0076] Optionally, when triggering an anomaly warning, degradation warning, or fault alarm, diagnostic information such as the latest sampled value, the threshold range corresponding to the target monitoring period in which the latest sampled value is located, and the mean and standard deviation of the target monitoring period in the previous sliding time window can be carried to assist maintenance personnel in quickly locating the root cause.

[0077] It should be noted that the execution order of each step in the above method embodiments is not limited to that shown in the attached figures, and the execution order of each step shall be subject to the actual application situation.

[0078] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: (1) The false alarm rate is significantly reduced: In the simulation test, after adopting the method of the present invention, the proportion of false alarms caused by the periodic fluctuation of the track signal is reduced from about 42% to less than 5%; (2) Enhanced early fault detection capability: For faults with slow channel quality degradation, the trend deterioration detection mechanism of the present invention can issue a deterioration warning before the index exceeds the limit, reserving a window for proactive intervention in operation and maintenance.

[0079] (3) No manual threshold maintenance: After a new network element is connected to the monitoring server in the satellite network management platform, the threshold range can be automatically and dynamically calculated after the historical data accumulation period (default 7 days) ends, without the need for maintenance personnel to manually configure the threshold parameters.

[0080] (4) Automatic adaptation to differences between multiple sites: The threshold range of each indicator of each network element is calculated independently, which naturally supports the differentiated adaptation of multiple geographical locations, multiple frequency bands and multiple business scenarios, without the need for business personnel to distinguish and configure.

[0081] In order to perform the corresponding steps in the above method embodiments and various possible implementations, an implementation method of a satellite communication fault monitoring device is given below.

[0082] Please see Figure 4 , Figure 4 A schematic diagram of the satellite communication fault monitoring device provided in an embodiment of the present invention is shown. The satellite communication fault monitoring device 200 includes: a data acquisition module 210, a threshold calculation module 220, and a monitoring module 230.

[0083] The acquisition module 210 is used to acquire sampled values ​​of multiple performance indicators of the network element to be monitored at preset sampling periods. The threshold calculation module 220 is used to determine multiple threshold intervals in real time for each performance index using all sampled values ​​of the performance index in the previous sliding time window; the window length of the sliding time window is equal to the orbital period of the satellite currently connected to the network element to be monitored. The monitoring module 230 is used to perform real-time fault monitoring based on the sampled values ​​of multiple threshold intervals and performance indicators within the current sliding time window.

[0084] Optionally, the threshold calculation module 220 can be used to: calculate a first mean and a first standard deviation based on all sampled values ​​of the performance index within the previous sliding time window; divide the previous sliding time window into multiple monitoring periods according to the orbital altitude type of the satellite currently connected to the network element to be monitored; for each monitoring period, calculate a second mean and a second standard deviation corresponding to the monitoring period based on all sampled values ​​of the performance index within the monitoring period; and calculate the threshold range of the performance index within the monitoring period by weighted summation based on the first mean, the first standard deviation, and the second mean and the second standard deviation corresponding to the monitoring period.

[0085] Optionally, when the orbital altitude type is low Earth orbit (LEO) or medium Earth orbit (MEO), the orbital phase of the satellite currently connected to the network element under monitoring is also collected at each sampling time. Specifically, the threshold calculation module 220 can be used to: start from the orbital phase corresponding to the first sampled value of the performance index within the previous sliding time window, determine the first monitoring period at a preset phase difference, and obtain the last sampled value within the first monitoring period; start from the orbital phase corresponding to the last sampled value of the performance index in the nth monitoring period, determine the (n+1)th monitoring period at a preset phase difference, and obtain the last sampled value within the (n+1)th monitoring period, where... , This indicates the number of monitoring periods within the sliding time window.

[0086] Optionally, when the orbital altitude type is geostationary orbit, the threshold calculation module 220 can be used to divide the previous sliding time window into multiple monitoring periods according to a fixed time interval.

[0087] Optionally, the threshold calculation module 220 can be used to: calculate a first threshold interval based on a first mean and a first standard deviation; calculate a second threshold interval based on a second mean and a second standard deviation corresponding to the monitoring period; and perform a weighted summation of the first threshold interval and the second threshold interval to obtain the threshold interval of the performance index during the monitoring period.

[0088] Optionally, the monitoring module 230 can be used to: determine the target monitoring period in which the latest sampled value of the performance indicator is located each time the latest sampled value of the performance indicator is obtained in the current sliding time window; if the latest sampled value exceeds the threshold range corresponding to the target monitoring period, record the performance indicator exceeding the limit event; if the number of times the performance indicator exceeds the limit event within M consecutive sampling periods exceeds a set threshold, trigger an abnormal warning; calculate the rate of change of the performance indicator based on the P sampled values ​​before the latest sampled value; if the absolute value of the rate of change exceeds a preset multiple of the standard deviation of all sampled values ​​in the target monitoring period of the previous sliding time window, trigger a degradation warning; if abnormal warnings are triggered continuously within a set duration, issue a fault alarm.

[0089] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the satellite communication fault monitoring device 200 described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0090] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 300 includes a processor 310, a memory 320, and a bus 330, with the processor 310 connected to the memory 320 via the bus 330.

[0091] The memory 320 can be used to store software programs or firmware, for example, the software program or firmware corresponding to the satellite communication fault monitoring device 200 described above. The processor 310 executes various functional applications and data processing by running the software program stored in the memory 320 to realize the satellite communication fault monitoring method provided in the embodiments of the present invention.

[0092] The memory 320 may be, but is not limited to, RAM (Random Access Memory), ROM (Read Only Memory), FLASH (Flash Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electric Erasable Programmable Read-Only Memory), etc.

[0093] The processor 310 can be an integrated circuit chip with signal processing capabilities, capable of executing software programs, such as the software program corresponding to the aforementioned satellite communication fault monitoring device 200. The processor 310 can be a general-purpose processor, including: CPU (Central Processing Unit), NP (Network Processor), SoC (System on Chip), etc.; it can also be: DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0094] Understandable. Figure 5 The structure shown is for illustrative purposes only; the electronic device 300 may also include components that are more advanced than those shown. Figure 5 The more or fewer components shown, or having the same Figure 5 The different configurations shown. Figure 5 The components shown can be implemented using hardware, software, or a combination thereof.

[0095] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the satellite communication fault monitoring method disclosed in the above embodiments. The computer-readable storage medium can be, but is not limited to, various media capable of storing program code, such as a USB flash drive, portable hard drive, ROM, RAM, PROM, EPROM, EEPROM, FLASH, magnetic disk, or optical disk.

[0096] This invention also provides a computer program product that, when executed by a processor, implements the satellite communication fault monitoring method disclosed in the above embodiments.

[0097] In summary, this invention provides a satellite communication fault monitoring method, device, electronic device, storage medium, and program product. It collects sampled values ​​of multiple performance indicators of the network element to be monitored at preset sampling periods. For each performance indicator, it uses all sampled values ​​of the performance indicator in the previous sliding time window to determine multiple threshold intervals in real time. Based on these multiple threshold intervals and the sampled values ​​of the performance indicator in the current sliding time window, real-time fault monitoring is performed. By dynamically calculating multiple threshold intervals for each indicator in each sliding time window, each indicator can be monitored, avoiding false alarms and missed alarms caused by using a uniform static threshold.

[0098] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for monitoring satellite communication faults, characterized in that, include: At preset sampling intervals, sampled values ​​of multiple performance indicators of the network element to be monitored are collected; For each performance metric, multiple threshold intervals are determined in real time using all sampled values ​​of the performance metric in the previous sliding time window; The length of the sliding time window is equal to the orbital period of the satellite currently connected to the network element to be monitored; Real-time fault monitoring is performed based on the multiple threshold intervals and the sampled values ​​of the performance indicators within the current sliding time window.

2. The method according to claim 1, characterized in that, The step of determining multiple threshold intervals in real time using all sampled values ​​of the performance index in the previous sliding time window includes: Based on all sampled values ​​of the performance index within the previous sliding time window, calculate the first mean and the first standard deviation; Based on the orbital altitude type of the satellite currently connected to the network element to be monitored, the previous sliding time window is divided into multiple monitoring periods; For each monitoring period, based on all sampled values ​​of the performance index within the monitoring period, calculate the second mean and second standard deviation corresponding to the monitoring period; Based on the first mean, the first standard deviation, and the second mean and the second standard deviation corresponding to the monitoring period, the threshold range of the performance index in the monitoring period is calculated by weighted summation.

3. The method according to claim 2, characterized in that, When the orbital altitude type is low Earth orbit or medium Earth orbit, the orbital phase of the satellite currently connected to the network element under monitoring is also collected at each sampling time. The step of dividing the previous sliding time window into multiple monitoring periods according to the orbital altitude type of the satellite currently connected to the network element under monitoring includes: Starting from the orbital phase corresponding to the first sampled value of the performance index within the previous sliding time window, the first monitoring period is determined at a preset phase difference interval, and the last sampled value within the first monitoring period is obtained. Starting from the orbital phase corresponding to the last sampled value of the performance index in the nth monitoring period, the (n+1)th monitoring period is determined at a preset phase difference, and the last sampled value within the (n+1)th monitoring period is obtained. , This indicates the number of monitoring periods within the sliding time window; When the orbital altitude type is geostationary orbit, the step of dividing the previous sliding time window into multiple monitoring periods according to the orbital altitude type of the satellite currently connected to the network element to be monitored includes: The previous sliding time window is divided into multiple monitoring periods according to fixed time intervals.

4. The method according to claim 2, characterized in that, The step of calculating the threshold range of the performance index during the monitoring period by weighted summation based on the first mean, the first standard deviation, and the second mean and the second standard deviation corresponding to the monitoring period includes: Calculate the first threshold interval based on the first mean and the first standard deviation; The second threshold interval is calculated based on the second mean and second standard deviation corresponding to the monitoring period. The first threshold interval and the second threshold interval are weighted and summed to obtain the threshold interval of the performance index during the monitoring period.

5. The method according to claim 4, characterized in that, The threshold interval is calculated as follows: In the formula, , Representing the first The lower and upper limits of the threshold range for each monitoring period; These are the weighting coefficients. These are the first sensitivity coefficient and the second sensitivity coefficient, respectively. , These represent the first mean and the first standard deviation, respectively. , They represent the first The second mean and the second standard deviation for each monitoring period.

6. The method according to claim 1, characterized in that, Each threshold interval corresponds to a monitoring period within the sliding time window. The step of performing real-time fault monitoring based on the multiple threshold intervals and the sampled values ​​of the performance indicators within the current sliding time window includes: Within the current sliding time window, each time the latest sampled value of the performance indicator is obtained, the target monitoring period in which the latest sampled value is located is determined; If the latest sampled value exceeds the threshold range corresponding to the target monitoring period, then record the performance indicator exceeding the limit event. If the number of out-of-limit events occurring in the performance index within M consecutive sampling periods exceeds a set threshold, an anomaly warning will be triggered. Based on the P sample values ​​before the latest sample value, calculate the rate of change of the performance index. If the absolute value of the rate of change exceeds a preset multiple of the standard deviation of all sample values ​​in the target monitoring period of the previous sliding time window, a degradation warning is triggered. If an abnormal warning is triggered continuously within the set time period, a fault alarm will be issued.

7. A satellite communication fault monitoring device, characterized in that, include: The data acquisition module is used to collect sampled values ​​of multiple performance indicators of the network element to be monitored at preset sampling periods. The threshold calculation module is used to determine multiple threshold intervals in real time for each performance indicator using all sampled values ​​of the performance indicator in the previous sliding time window. The length of the sliding time window is equal to the orbital period of the satellite currently connected to the network element to be monitored; The monitoring module is used to perform real-time fault monitoring based on the multiple threshold intervals and the sampled values ​​of the performance indicators in the current sliding time window.

8. An electronic device, characterized in that, include: A memory and a processor, wherein the memory stores a software program, and the processor executes the software program to implement the method as described in any one of claims 1-6 when the electronic device is running.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1-6.

10. A computer program product, characterized in that, When the computer program product is executed by a processor, it implements the method of any one of claims 1-6.

Images