Method, system, device and medium for calculating idle time period of satellite ground station

Abstract

The present application relates to satellite TT&C technology field, provide a kind of third party satellite ground station idle period calculation method, system, equipment, medium, method includes: S1: obtaining the operation data of third party satellite ground station;S2: feature parameter is extracted from the operation data of third party satellite ground station;S3: idle degree score is calculated according to feature parameter, and idle degree score is judged as idle period or non-idle period;S4: set time window size, using sliding time window mechanism to scan for preset continuous time period, when the continuous predetermined number of time windows are all idle period, determine that third party satellite ground station is continuous idle period.The present application can quickly, accurately detect and identify the idle period of ground station, provide real-time, reliable technical support for resource scheduling between ground station and satellite, effectively reduce resource waste, improve resource utilization.

Description

Technical Field

[0001] This invention relates to the field of satellite telemetry, tracking and command technology, and in particular to a method, system, equipment, and medium for calculating idle time periods of a third-party satellite ground station. Background Technology

[0002] With the rapid development of global satellite communication technology, the number of satellites in orbit continues to increase, leading to a growing demand for ground station resources. However, the existing resource scheduling mechanism between ground stations and satellites has the following problems: 1. Low resource utilization: Many third-party satellite ground stations are idle during non-working hours, but lack effective idle time detection and sensing mechanisms, resulting in low resource utilization and waste of resources.

[0003] 2. Slow Sensing Speed: Due to the requirements of statistical sample size (statistical methods usually rely on statistical analysis (such as mean, variance, trend analysis, etc.) to determine idle periods, a sufficiently large sample size is required to ensure the reliability and accuracy of statistical results. Generally, at least one complete satellite orbit cycle, one day, or one week of data needs to be collected to establish an effective statistical model), data integrity requirements (traditional methods need to wait for a complete data cycle to ensure coverage of all possible working and idle modes, and need to collect a sufficiently long historical data to identify periodic patterns and anomalies, making it impossible to make rapid judgments based on fragmented data), and algorithm convergence limitations (such as machine learning models, time series analysis, and other traditional algorithms require a large amount of training data to converge, and the model training and parameter optimization process requires repeated iterations, consuming a lot of time and unable to achieve real-time or near-real-time rapid response), traditional idle period detection methods usually require long-term data collection and analysis, making it impossible to achieve rapid sensing, difficult to meet the needs of real-time resource scheduling, and affecting the efficiency of resource scheduling between ground stations and satellites.

[0004] 3. Insufficient Detection Accuracy: Existing methods typically employ a single threshold judgment method (based solely on resource utilization, such as CPU utilization, bandwidth utilization, signal power, etc., setting a single threshold, e.g., resource utilization <10% is judged as "idle," 10%-30% as "low load." The problem is that this method is too simplistic and cannot accurately reflect the true state of the ground station, easily misjudging low load as idle, or vice versa). Another method uses a fixed threshold range (setting a fixed threshold range to distinguish different states, e.g., idle (0-5%), low load (5-20%), normal load (20-80%), high load (80-100%). The problem is that fixed thresholds cannot adapt to the working characteristics of different ground stations, changes in demand at different times, and the load characteristics of different satellite missions). A simple statistical averaging method (judging based on the average or median of historical data, e.g., ...) is also used. The following methods are used to determine whether a station is idle: 1) If the current load is less than 50% of the historical average, it is considered idle. Problem: This ignores the dynamic changes in load and cannot distinguish between "temporarily idle" and "continuously idle," nor can it accurately identify the state of "low load but stable operation." 2) The single-indicator judgment method (based on only one indicator, such as signal power, data traffic, or number of connections) has the problem that ground station status is multi-dimensional, and a single indicator cannot fully reflect the true state, easily leading to misjudgments. This makes it difficult to accurately distinguish between the "idle" and "low load" states of ground stations, easily leading to misjudgments, affecting the accuracy of resource scheduling, resulting in unreasonable resource allocation and resource waste.

[0005] 4. Lack of intelligence: Traditional methods mainly rely on manual monitoring or simple threshold judgment, lacking intelligent perception algorithms based on machine learning and data processing, and thus cannot achieve automated resource scheduling optimization.

[0006] 5. Inflexible resource scheduling: The existing system is unable to dynamically adjust the resource allocation between satellites and ground stations based on the actual idle status of ground stations, resulting in some ground stations having idle resources while others have strained resources, leading to resource waste.

[0007] Therefore, there is a need to provide a method, system, equipment, and medium for calculating idle time periods of third-party satellite ground stations, which can quickly and accurately detect and identify idle time periods of ground stations, provide real-time and reliable technical support for resource scheduling between ground stations and satellites, effectively reduce resource waste, and improve resource utilization.

[0008] The information disclosed in the background section is only intended to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0009] The main purpose of this invention is to overcome the problems of slow perception speed, insufficient detection accuracy, lack of intelligence, inflexible scheduling, and low resource utilization of existing technologies for third-party ground stations' idle time periods. It provides a method, system, equipment, and medium for calculating the idle time periods of third-party satellite ground stations, which can quickly and accurately detect and identify the idle time periods of ground stations, provide real-time and reliable technical support for resource scheduling between ground stations and satellites, effectively reduce resource waste, and improve resource utilization.

[0010] To achieve the above objectives, the first aspect of the present invention provides a method for calculating idle time periods of a third-party satellite ground station, comprising the following steps: S1: Obtain operational data from a third-party satellite ground station; S2: Extract feature parameters from operational data of third-party satellite ground stations; S3: Calculate the idleness score based on the feature parameters, and determine whether it is an idle period or a non-idle period based on the idleness score; S4: Set the time window size and use a sliding time window mechanism to scan the preset continuous time period. When a predetermined number of consecutive time windows are all idle periods, it is determined that the third-party satellite ground station is in a continuous idle period.

[0011] According to an exemplary embodiment of the present invention, the method for calculating idle time periods of the third-party satellite ground station further includes: S5: verifying and optimizing continuous idle time periods.

[0012] According to an exemplary embodiment of the present invention, in step S1, the operational data of the third-party satellite ground station includes: communication task data, equipment status data, resource usage data, and timestamp data; The communication mission data refers to the communication mission information between the ground station and the satellite, including mission type, mission status, and mission duration. The equipment status data refers to the operating status and configuration information of the ground station equipment, including antenna status, receiver status, and transmission status. The resource occupancy data refers to the occupancy status of ground station resources, which include antennas, channels, and processing units. The timestamp data includes the time information of data collection.

[0013] According to an exemplary embodiment of the present invention, step S1 further includes: preprocessing the operational data of a third-party satellite ground station, the preprocessing including: data cleaning, data normalization and time alignment.

[0014] According to an exemplary embodiment of the present invention, in step S2, the feature parameters include communication task features, device status features, resource usage features, and time features; The characteristics of the communication task include: task data, task density, task duration, and task type distribution; The equipment status characteristics include: equipment utilization rate, equipment idle rate, equipment status changes, and equipment load status; The resource occupancy characteristics include: resource occupancy rate, resource idle rate, and resource allocation status; The time characteristics include: time period characteristics, periodic characteristics, and duration characteristics.

[0015] According to an exemplary embodiment of the present invention, in step S3, the idleness score is calculated based on the feature parameters using the following formula: Idleness score = w1 × Communication task idleness + w2 × Device status idleness + w3 × Resource usage idleness + w4 × Time characteristic idleness; Among them, w1, w2, w3, and w4 are weighting coefficients.

[0016] According to an exemplary embodiment of the present invention, the step of determining whether a period is idle or non-idle based on an idleness score includes: Set an idle time threshold. When the idle time score is lower than the idle time threshold, the time period is determined to be an idle time period; otherwise, the time period is determined to be a non-idle time period.

[0017] According to an exemplary embodiment of the present invention, in step S4, the time window size includes 5 minutes, 10 minutes, or 30 minutes.

[0018] As a second aspect of the present invention, the present invention provides a system for calculating idle time periods of a third-party satellite ground station, comprising: a data acquisition module, a feature extraction module, an idle time period calculation module, and a continuous idle time period determination module; The data acquisition module is used to acquire operational data from third-party satellite ground stations; The feature extraction module is used to extract feature parameters from the operational data of a third-party satellite ground station; The idle time period calculation module is used to calculate the idleness score based on the feature parameters, and to determine whether it is an idle time period or a non-idle time period based on the idleness score; The continuous idle period determination module is used to set the time window size and scan the preset continuous time period using a sliding time window mechanism. When the time window of the continuously predetermined data is an idle period, the third-party satellite ground station is determined to be an continuous idle period.

[0019] According to an exemplary embodiment of the present invention, the calculation system for the idle time period of the third-party satellite ground station further includes a verification and optimization module, which is used to verify and optimize continuous idle time periods.

[0020] As a third aspect of the present invention, the present invention provides an electronic device comprising: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for calculating the cost of tracking satellites from the ground station.

[0021] As a fourth aspect of the present invention, the present invention provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the method for calculating the cost of tracking satellites by a ground station.

[0022] The advantages of this invention are: This solution utilizes multi-dimensional feature extraction, rapid threshold judgment, and multi-source data verification to achieve fast and accurate idle time period detection, providing crucial technical support for resource scheduling between ground stations and satellites. The algorithm boasts advantages such as rapid perception, high-precision judgment, and intelligent processing, effectively reducing resource waste and improving resource utilization, demonstrating significant practical value and promising application prospects. Attached Figure Description

[0023] The above and other objects, features, and advantages of this application will become more apparent from the detailed description of exemplary embodiments with reference to the accompanying drawings. The drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0024] Figure 1 The diagram illustrates the structure of the computing system during the idle period of a third-party satellite ground station.

[0025] Figure 2 The diagram illustrates the steps involved in calculating idle time periods for third-party satellite ground stations.

[0026] Figure 3 A schematic diagram of the electronic device is shown.

[0027] Figure 4 A schematic diagram of the structure of a computer medium is shown. Detailed Implementation

[0028] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0029] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0030] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0031] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0032] It should be understood that although the terms first, second, third, etc., may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Therefore, the first component discussed below may be referred to as the second component without departing from the teachings of this application. As used herein, the term "and / or" includes all combinations of any one and more of the associated listed items.

[0033] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily essential for implementing this application, and therefore cannot be used to limit the scope of protection of this application.

[0034] According to a first specific embodiment of the present invention, the present invention provides a system for calculating the idle time periods of a third-party satellite ground station, such as... Figure 1As shown, it includes: a data acquisition module, a feature extraction module, an idle time period calculation module, a continuous idle time period determination module, a verification and optimization module, and an output module.

[0035] The data acquisition module is used to acquire operational data from third-party satellite ground stations; The feature extraction module is used to extract feature parameters from the operational data of third-party satellite ground stations; The idle time period calculation module is used to calculate the idleness score based on the feature parameters, and to determine whether it is an idle time period or a non-idle time period based on the idleness score; The continuous idle period determination module is used to set the size of the time window and scan the preset continuous time period using a sliding time window mechanism. When the time window of the continuously predetermined data is an idle period, the third-party satellite ground station is determined to be an continuous idle period.

[0036] The verification and optimization module is used to verify and optimize continuous idle periods.

[0037] The output module is used to output information about the verified and optimized continuous idle periods.

[0038] According to a second embodiment of the present invention, the present invention provides a method for calculating idle time periods of a third-party satellite ground station, employing the calculation system for idle time periods of a third-party satellite ground station as described in the first embodiment, such as... Figure 2 As shown, it includes the following steps: S1: Obtain operational data from a third-party satellite ground station.

[0039] The operational data from third-party satellite ground stations includes: communication mission data, equipment status data, resource usage data, and timestamp data.

[0040] Communication mission data refers to communication mission information between the ground station and the satellite, including mission type, mission status, and mission duration.

[0041] Equipment status data includes the operating status and configuration information of ground station equipment, including antenna status, receiver status, and transmitter status.

[0042] Resource usage data refers to the usage of ground station resources, which include antennas, channels, and processing units.

[0043] Timestamp data includes information about the time when the data was collected.

[0044] After acquiring the operational data, the operational data from the third-party satellite ground station is preprocessed. Preprocessing includes data cleaning, data normalization, and time alignment. Data cleaning removes outliers and noise. Data normalization unifies data with different dimensions to the same scale. Time alignment synchronizes multi-source data according to timestamps.

[0045] S2: Extract feature parameters from operational data of third-party satellite ground stations.

[0046] The characteristic parameters include communication task characteristics, device status characteristics, resource usage characteristics, and time characteristics.

[0047] Communication task characteristics include: task data, task density, task duration, and task type distribution. Task data refers to the total number of communication tasks within a preset time period. Task density is the number of tasks per unit time. Task duration includes the average task duration and the longest task duration. Task type distribution includes the distribution of different task types, such as data transmission, telemetry, and control.

[0048] Task data = the number of communication task records within the statistical time period, i.e., the total number of tasks N.

[0049] Task density = total number of tasks N / length of statistical time period T, in units of tasks / hour or tasks / minute; for example, if the statistical time period is 1 hour and the total number of tasks is 6, then the task density = 6 tasks / hour.

[0050] Average task duration = Σ(duration of each task) / total number of tasks N; longest task duration = MAX(duration of each task).

[0051] Task type distribution = number of tasks of each type / total number of tasks N × 100%, or expressed as a vector of the proportion of each type.

[0052] Equipment status characteristics include: equipment utilization rate, equipment idle rate, equipment status changes, and equipment load. Equipment utilization rate refers to the usage rate of each device, including antennas, receivers, and transmitters. Equipment idle rate refers to the percentage of idle time for each device. Equipment status changes include the frequency and pattern of these changes. Equipment load includes the load level and load change trends.

[0053] Raw data for equipment utilization: working time and stopping time of each device (antenna, receiver, transmitter); Extraction method: Equipment utilization rate = (total working time of equipment / total duration of statistical period) × 100%.

[0054] Raw data for equipment idle rate: working time and stopping time of each device; Extraction method: Equipment idle rate = (total idle time of equipment / total duration of statistical period) × 100%.

[0055] Raw data on equipment status changes: Equipment status sequence (working → idle → working → idle...); Extraction method: Status change frequency = number of status transitions / statistical time period.

[0056] Raw data on equipment load: Time series data such as receiver / transmitter power values ​​and data traffic; Extraction methods: Load level: Calculate the average, peak, and minimum load values; Average load = Σ(load value at each time moment) / number of data points; Peak load = MAX(load value at each time moment); Load change trend: Calculate the load change trend through time series analysis (such as linear regression, moving average); Load change trend = (average load of the most recent N times - average load of the previous N times moment) / time interval.

[0057] Resource occupancy characteristics include: resource occupancy rate, resource idle rate, and resource allocation. Resource occupancy rate is the percentage of time each type of resource is occupied, including antennas, channels, and processing units. Resource idle rate is the percentage of time each type of resource is idle. Resource allocation includes the allocation pattern and allocation efficiency.

[0058] Raw data on resource occupancy: Occupancy records of various resources (antenna, channel, processing unit) (occupancy start time, end time); Extraction method: Resource occupancy rate = (total resource occupancy time / total duration of the statistical period) × 100%.

[0059] Raw data for resource idle rate: occupancy records of various resources; Extraction method: Resource idle rate = (total idle time of resources / total duration of the statistical period) × 100%.

[0060] Time characteristics include: time period characteristics, periodic characteristics, and duration characteristics. Time period characteristics include weekdays / non-working days and working hours / non-working hours. Periodic characteristics include the presence of periodic idle patterns. Duration characteristics are the duration of consecutive idle periods.

[0061] Raw data for time period characteristics: timestamps, date information; Extraction methods: Weekday / Non-working day: Determine whether it is a weekday from the date information (Monday to Friday are weekdays, Saturday and Sunday are non-working days); or determine it based on the holiday calendar; Working hours / Non-working hours: Determine whether it is within the defined working time period based on the timestamp (e.g., 8:00-18:00 is working hours); or derive the actual working time pattern based on historical data statistics.

[0062] Raw data for duration characteristics: device state sequence, resource occupancy record; Extraction method: continuous idle duration = idle state end time - idle state start time.

[0063] For example: Antenna idle rate: 40% (9.6 hours out of 24 hours are idle); Receiver idle rate: 35% (8.4 hours out of 24 hours are idle); Transmitter idle rate: 45% (10.8 hours out of 24 hours are idle); Equipment status changes: an average of 2 times per hour; Equipment load status: average load is 55%, peak load is 85%.

[0064] S3: Calculate the idleness score based on the feature parameters, and determine whether it is an idle period or a non-idle period based on the idleness score.

[0065] Features are statistics, patterns, and regularities extracted from raw data, while idleness is a quantitative assessment of the "degree of idleness." The conversion from features to idleness is a process of feature fusion and normalization.

[0066] The idleness score is calculated based on the feature parameters using the following formula: Idleness score = w1 × Communication task idleness + w2 × Device status idleness + w3 × Resource usage idleness + w4 × Time characteristic idleness; Among them, w1, w2, w3, and w4 are weighting coefficients.

[0067] Calculation of communication task idle time: Communication task idleness = f(task data, task density, task duration, task type distribution); Detailed calculation steps: Step 1: Feature normalization (the larger the value, the busier the worker and the lower the idle level; the smaller the value, the idle the worker and the higher the idle level): Task data normalization: task_count_norm=1-min(total number of tasks / baseline number of tasks, 1); when the number of tasks is 0, the idle degree is 1, and when the number of tasks reaches or exceeds the baseline, it is 0.

[0068] Task density normalization: density_norm=1-min(task density / baseline density, 1); take 1 when density is 0, and take 0 when density reaches or exceeds the baseline.

[0069] Average task duration normalization: duration_norm=1-min(average task duration / baseline duration, 1); the shorter the duration, the higher the idle rate.

[0070] Task type distribution normalization: You can choose the idle degree corresponding to "no task or low percentage", such as 1 when there is no task; or map it inversely according to the percentage of the main occupation type (such as control, telemetry, data transmission).

[0071] Step 2: Feature Fusion Communication task idleness = δ1×task_count_norm+δ2×density_norm+δ3×duration_norm+δ4×type_dist_norm (task type distribution normalization); where δ1, δ2, δ3, and δ4 are weight coefficients, and δ1+δ2+δ3+δ4=1; usually, task data and task density have larger weights (δ1, δ2), because "no tasks or low density" directly represents idleness.

[0072] Step 3: Special case: When the total number of tasks is 0, the communication task idle degree can be directly set to 1.0.

[0073] Calculation of equipment idle status: Equipment status idleness = f(equipment utilization rate, equipment idle rate, equipment status change, equipment load status); Detailed calculation steps: Step 1: Feature Normalization: Equipment utilization normalization: utilization_norm=1-(equipment utilization / 100); the higher the utilization, the lower the idle rate, so subtract the normalization value from 1.

[0074] Device idle rate normalization: idle_rate_norm = device idle rate / 100; the higher the idle rate, the higher the idleness.

[0075] Device state change normalization: change_freq_norm=1 / (1+state change frequency / base frequency); the lower the state change frequency, the higher the idle degree (state stability).

[0076] Equipment load normalization: load_norm = 1 - (average load / maximum load); the lower the load, the higher the idle rate.

[0077] Step 2: Feature Fusion Equipment idle rate = α1 × utilization_norm + α2 × idle_rate_norm + α3 × change_freq_norm + α4 × load_norm; where: α1, α2, α3, α4 are weighting coefficients, and α1 + α2 + α3 + α4 = 1; typically: α2 (equipment idle rate weight) > α1 (equipment utilization weight) > α4 (load weight) > α3 (state change weight).

[0078] Step 3: Multi-device integration: If there are multiple devices (antenna, receiver, transmitter), integration is required: Device idle time = (antenna idle time + receiver idle time + transmitter idle time) / 3; or use a weighted average: Device idle time = w_antenna1 × antenna idle time + w_receiver × receiver idle time + w_transmitter × transmitter idle time. Where w_antenna1 represents the antenna's weight, the proportion of antenna idle time in "Device idle time"; w_receiver represents the receiver's weight, the proportion of receiver idle time in "Device idle time"; w_transmitter represents the transmitter's weight, the proportion of transmitter idle time in "Device idle time".

[0079] Resource idleness calculation: Resource idleness = f(resource occupancy rate, resource idleness rate, resource allocation status); Detailed calculation steps: Step 1: Feature Normalization: Resource occupancy normalization: occupancy_norm = 1 - (resource occupancy / 100); the higher the occupancy, the lower the idle rate.

[0080] Resource idle rate normalization: idle_rate_norm = resource idle rate / 100; the higher the idle rate, the higher the idleness.

[0081] Resource allocation efficiency normalization: allocation_efficiency_norm=1-(allocation efficiency / 100); the higher the allocation efficiency, the more fully the resources are utilized and the lower the idle rate; or: low allocation efficiency indicates that the resources are not fully utilized after allocation and there may be idle space.

[0082] Step 2: Feature Fusion Resource occupancy / idleness = β1 × occupancy_norm + β2 × idle_rate_norm + β3 × allocation_efficiency_norm; where β1, β2, and β3 are weighting coefficients, and β1 + β2 + β3 = 1; typically: β2 (resource idleness rate weight) > β1 (resource occupancy rate weight) > β3 (allocation efficiency weight).

[0083] Step 3: Multi-resource integration: If there are multiple resources (antenna, channel, processing unit), they need to be integrated: Resource occupancy idle time = (antenna resource idle time + channel resource idle time + processing unit idle time) / 3; or use a weighted average.

[0084] Resource occupancy idle time = w_antenna2 × antenna resource idle time + w_channel × channel resource idle time + w_processor × processing unit idle time. Where w_antenna represents the antenna resource weight, the proportion of antenna resource idle time in the overall result; w_channel represents the channel resource weight, the proportion of channel resource idle time in the overall result; and w_processor represents the processing unit resource weight, the proportion of processing unit idle time in the overall result.

[0085] Calculation of idle time characteristics: Time-related idleness = f(time period characteristics, periodicity characteristics, duration characteristics).

[0086] Detailed calculation steps: Step 1: Time Period Characteristics - Idleness: If it's a non-working day: time_period_score=0.8; the idle time is higher on non-working days. ELSE IF Non-working time: time_period_score=0.6; Non-working time idleness is moderate; ELSE: time_period_score=0.3; low idle time during working hours.

[0087] Step 2: Periodic Feature Idleness: If a periodic idle mode exists: If the current time is within a periodic idle period: periodic_score = periodic intensity × 0.9; ELSE: periodic_score = (1 - periodic intensity) × 0.5; ELSE: periodic_score=0.5; no periodic pattern, take the neutral value.

[0088] Step 3: Duration feature: Idleness If the current period is a continuous idle period: duration_score = min(continuous idle duration / baseline duration, 1.0); the longer the idle duration, the higher the idle level. ELSE: duration_score=0.3; Not in idle period, low idle level.

[0089] Step 4: Feature Fusion The idle time feature is calculated as follows: idle time = γ1 × time_period_score + γ2 × periodic_score + γ3 × duration_score; where γ1, γ2, and γ3 are weight coefficients, and γ1 + γ2 + γ3 = 1; typically, γ3 (duration weight) > γ2 (periodic weight) > γ1 (time period weight).

[0090] The idle or non-idle periods are determined based on the idleness score, including: Set an idle time threshold. When the idle time score is lower than the idle time threshold, the time period is determined to be an idle time period; otherwise, the time period is determined to be a non-idle time period.

[0091] For example: Set the idle threshold T_idle = 0.3, and calculate the idleness score: Communication task idleness = 0.75 (no communication tasks during this time period); Device status idleness = 0.90 (antenna, receiver, and transmitter are all in idle or standby state); Resource utilization idleness = 0.85 (resource utilization rate is less than 20%). Time-based idleness = 0.80 (nighttime period); Idle time score = 0.25×0.75+0.35×0.90+0.25×0.85+0.15×0.80=0.8375; Since the idleness score 0.8375 > T_idle (0.3) and the device status clearly shows that it is idle, this time period is determined to be an idle period.

[0092] S4: Set the time window size and use a sliding time window mechanism to scan the preset continuous time period. When a predetermined number of consecutive time windows are all idle periods, it is determined that the third-party satellite ground station is in a continuous idle period.

[0093] The time window size can be 5 minutes, 10 minutes, or 30 minutes.

[0094] Specifically, the window slides with a step size S (e.g., 1 minute), and the idleness score is calculated for each window. When the idleness scores of multiple consecutive windows are all lower than the idleness judgment threshold, it is determined to be a continuous idle period.

[0095] The predetermined quantity is a threshold parameter used to control the criteria for determining consecutive idle periods.

[0096] S5: Verify and optimize continuous idle periods.

[0097] S51: Historical data verification.

[0098] Query the idle status of the same time period in historical data; if the historical data shows that the time period is usually idle, increase the confidence of the current judgment; if the historical data shows that the time period is usually not idle, decrease the confidence of the current judgment or re-judge.

[0099] S52: Cross-validation of multi-source data.

[0100] Cross-validation is performed by combining multiple data sources (such as communication task data, device status data, resource usage data, etc.); when multiple data sources show an idle status, the accuracy of the judgment is improved; when there are contradictions between data sources, further analysis or manual intervention is required.

[0101] The cross-validation method is as follows: S521: Data Acquisition: Acquire communication task data, acquire device status data, and acquire resource usage data.

[0102] S522: Time Alignment: Aligns all data sources to the same time window; handles time delays and asynchrony issues.

[0103] S523: Independent judgment: Idle status is determined based on task data; idle status is determined based on device status data; idle status is determined based on resource usage data.

[0104] S524: Cross-validation: Compare the judgment results from different data sources; calculate the degree of consistency; detect contradictions.

[0105] S525: Conflict handling: IF Completely consistent: Use the judgment result with high confidence; ELSE IF Partially consistent: Perform weighted fusion or further analysis; ELSE: Trigger detailed analysis or manual intervention.

[0106] S526: Final Judgment: Output the final idle state judgment; output the confidence level; output the conflict resolution record.

[0107] Why do contradictions occur? Although the preceding steps (S1, S2, S3) have already used communication task data, device status data, resource usage data, etc., contradictions may still arise during the calculation process. The main reasons include: 1. Different data perspectives: Communication task data: From a task scheduling perspective, it may display "No task" (idle); Device status data: From the perspective of device operation, it may show "Device is working" (not idle); Resource usage data: From the perspective of resource allocation, it may show "Resources are occupied" (not idle); For example, the task scheduling system may show no tasks, but the equipment may be performing non-task activities such as maintenance or self-test, and resources may be reserved or locked.

[0108] 2. Time synchronization issues: The collection times of different data sources may not be completely synchronized; The timestamps of the data source may be inaccurate; For example, at time T1, the task data shows that the task has ended (idle), but the device status data is only updated to the stopped state at time T2 (T2>T1), which leads to inconsistent judgments within the time window.

[0109] 3. Data integrity issues: Some data sources may be missing some data; Data collection may fail or be delayed; For example, the device status data may be normal, but the communication task data may not be updated in time due to network problems, leading to inconsistent judgments.

[0110] 4. Data quality issues: Sensor malfunction caused data anomalies; Data transmission errors caused data distortion; For example, the equipment may be idle, but a sensor malfunction causes the equipment status data to be incorrectly displayed as "working".

[0111] 5. Differences in state definition: Different data sources may have different definitions of "idle"; Threshold settings may be inconsistent; For example, task data considers "no tasks" to be idle, but resource data considers "resource utilization rate < 5%" to be idle.

[0112] 6. Intermediate and Transitional States: The system may be in the middle of a state transition; The states captured by different data sources may be at different stages of transformation; For example: the task has ended but the equipment has not completely stopped; resources have been released but the equipment is still running.

[0113] 7. Multi-task concurrent scenarios: Multiple tasks may share resources; The completion of a task does not mean that resources are immediately released; For example, if task A finishes, but task B is still using the same resource, the task data will show as idle, but the resource data will show as occupied.

[0114] S53: Dynamic threshold adjustment.

[0115] Based on historical data and real-time feedback, the idle judgment threshold T_idle is dynamically adjusted; the detection speed is optimized while ensuring detection accuracy; and the system adapts to the characteristics and changing patterns of different ground stations.

[0116] Historical data sources: historical data of S1-S3 (historical operation data, historical features, historical judgment results), historical judgment results of S51, historical cross-validation results of S52, and other historical data (historical working modes, historical threshold adjustment records, etc.).

[0117] Real-time feedback sources: S52 real-time cross-validation results (main source), S51 real-time judgment results, and other real-time feedback (user feedback, actual scheduling results, etc.).

[0118] Dynamically adjusting the idle judgment threshold T_idle requires recalculating the idle judgment threshold T_idle in steps S3 and S52, and saving the idle judgment threshold T_idle for the next round of optimization.

[0119] The idle threshold T_idle is a dynamic threshold.

[0120] T_idle(t) = f(historical data(t), real-time feedback(t), ground station characteristics, time(t)); in: T_idle(t): The dynamic threshold at time t; Historical data (t): Historical data up to time t; Real-time feedback(t): Real-time feedback at time t; Ground station characteristics: The inherent characteristics of a ground station; Time t: Current time (considering time factors such as periodicity and seasonality).

[0121] Methods to ensure detection accuracy: First, a precision monitoring mechanism.

[0122] Accuracy monitoring indicators: 1. Accuracy of judgment: Accuracy = (Number of correct judgments / Total number of judgments) × 100%.

[0123] 2. Precision: Accuracy = (True idleness correctly identified / All identified as idle) × 100%.

[0124] 3. Recall: Recall rate = (True idle units correctly identified / All true idle units) × 100%.

[0125] 4. F1 score: F1 = 2 × (precision × recall) / (precision + recall).

[0126] 5. Confidence: Confidence assessment from S52 cross-validation.

[0127] Second, the accuracy guarantee strategy.

[0128] Accuracy guarantee strategy: Strategy 1: Precision threshold constraint: If the current precision is less than the minimum precision requirement: Reduction in precision is not permitted; Sacrificing accuracy for speed is not allowed.

[0129] Strategy 2: Dynamic Precision Monitoring Real-time monitoring and judgment accuracy; When accuracy drops, adjust the strategy immediately; Accuracy must not be allowed to continue to decline.

[0130] Strategy 3: Accuracy Verification Mechanism Verify the accuracy after each judgment; Regularly assess overall accuracy; Any accuracy issues discovered should be corrected immediately.

[0131] Strategy 4: Precision Feedback Closed Loop Collect user feedback; Compare with actual scheduling results; Adjust thresholds and strategies based on feedback.

[0132] Third, the application of precision constraints in threshold adjustment.

[0133] Precision constraints for threshold adjustment: T_idle(t+1)= T_idle(t)+ΔT(t); Constraints: 1. Precision Constraints: Accuracy(T_idle(t+1))≥Accuracy_min; Among them, Accuracy_min is the minimum accuracy requirement (e.g., 90%).

[0134] 2. Precision variation constraints: |Accuracy(T_idle(t+1))-Accuracy(T_idle(t))|≤ΔAccuracy_max; to avoid drastic fluctuations in accuracy.

[0135] 3. Precision trend constraint: IF Accuracy(T_idle(t)) < Accuracy(T_idle(t-1)): Limit the range of threshold adjustment; Prioritize ensuring that accuracy does not decrease.

[0136] In step S5, S51 (Idle Time Detection): Function: Idle time period judgment based on features, providing S53 with: historical judgment results, real-time judgment results, and judgment accuracy statistics.

[0137] S52 (Multi-source cross-validation): Function: Cross-validation of multi-source data improves the accuracy of judgment; Provided for S53: Real-time feedback: current confidence level of the judgment, data source consistency, and contradiction detection results; Historical data: historical cross-validation results, historical confidence data.

[0138] S53 (Dynamic Threshold Adjustment): Function: Dynamically adjust the idle threshold T_idle based on historical data and real-time feedback; It is not limited to using data from S51 and S52.

[0139] S6: Output idle time period information.

[0140] The identified idle time period information will be output and updated in real time: S61: List of Idle Time Periods: Output information such as start time, end time, duration, confidence level, and available resource type for the idle period.

[0141] S62: Real-time update mechanism: The list of idle periods is updated in real time when a new idle period is detected or the status of an idle period changes.

[0142] S63: Data storage: Idle time information is stored in a database for subsequent resource scheduling queries and analysis.

[0143] S64: Resource Scheduling Interface Idle time information is provided to the resource scheduling system for resource scheduling decisions between ground stations and satellites.

[0144] Compared to existing technologies, the fast perception algorithm in this solution achieves accurate differentiation between idle and low-load conditions through the following technical means: 1. Multi-dimensional comprehensive judgment mechanism: Simultaneously monitor multiple metrics: signal power, data traffic, number of connections, task queue length, resource utilization, etc. Establish a multi-dimensional status assessment model to comprehensively judge the status of ground stations.

[0145] 2. Dynamic threshold adaptive mechanism: The judgment threshold is dynamically adjusted based on factors such as the ground station's historical operating mode, current mission characteristics, and satellite orbit period. Different threshold standards are used for different time periods and different task types; It avoids misjudgments caused by fixed thresholds and can accurately distinguish between idle and low-load states based on the actual situation.

[0146] 3. State persistence and stability analysis: It not only determines the current state, but also analyzes the duration and stability of the state; Idle state: No tasks for an extended period and no tasks expected; Low load state: There are a few tasks, but the load remains stable at a low level; This is reflected in the fact that, through time-based analysis, it is possible to distinguish between "temporarily idle" and "continuously idle" states, as well as "low-load but stable" states.

[0147] 4. Task context awareness: Consider factors such as the nature, priority, and expected duration of the current task; Distinguish between "no task" (idle) and "tasks on but low load" (low load); This means that by combining mission context information, it is possible to more accurately determine whether the ground station is truly idle or operating under low load.

[0148] This solution utilizes multi-dimensional feature extraction, rapid threshold judgment, and multi-source data verification to achieve fast and accurate idle time period detection, providing crucial technical support for resource scheduling between ground stations and satellites. The algorithm boasts advantages such as rapid perception, high-precision judgment, and intelligent processing, effectively reducing resource waste and improving resource utilization, demonstrating significant practical value and application prospects. Specifically, its advantages include the following: 1. Rapid perception capability: Through multi-dimensional feature fusion and rapid threshold judgment, it can complete the detection of idle periods in a short time, realize rapid perception, meet the needs of real-time resource scheduling, and improve resource scheduling efficiency.

[0149] 2. High-precision judgment: By adopting a multi-source data cross-validation and historical data verification mechanism, it can accurately distinguish between "idle" and "low load" states, improve judgment accuracy, reduce misjudgments, and provide accurate information support for resource scheduling.

[0150] 3. Intelligent processing: Based on feature extraction and machine learning, it can automatically learn and adapt to the characteristics of different ground stations, realize intelligent idle time perception, reduce manual intervention, and improve the level of automation.

[0151] 4. Real-time updates: Through the real-time update mechanism, changes in the status of ground stations can be reflected in a timely manner, providing the latest idle time information for resource scheduling and supporting dynamic resource scheduling.

[0152] 5. Reduce resource waste: By accurately identifying idle periods, a basis is provided for resource scheduling between ground stations and satellites, enabling idle ground station resources to be allocated to satellite missions in a timely manner, effectively reducing resource waste and improving resource utilization.

[0153] 6. High scalability: The algorithm adopts a modular design, which can adjust feature weights and judgment thresholds according to different application scenarios, and has good scalability and adaptability.

[0154] According to a third specific embodiment of the present invention, the present invention provides an electronic device, such as... Figure 3 As shown, Figure 3 This is a block diagram illustrating an electronic device according to an exemplary embodiment.

[0155] The following reference Figure 3 To describe an electronic device 300 according to this embodiment of the present application. Figure 3 The electronic device 300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0156] like Figure 3 As shown, the electronic device 300 is presented in the form of a general-purpose computing device. The components of the electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one storage unit 320, a bus 330 connecting different system components (including storage unit 320 and processing unit 310), a display unit 340, etc.

[0157] The storage unit stores program code that can be executed by the processing unit 310, causing the processing unit 310 to perform the steps described in this specification according to various exemplary embodiments of this application. For example, the processing unit 310 can perform the steps shown in the second specific embodiment.

[0158] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 3201 and / or a cache storage unit 3202, and may further include a read-only memory unit (ROM) 3203.

[0159] The storage unit 320 may also include a program / utility 3204 having a set (at least one) program module 3205, such program module 3205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0160] Bus 330 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0161] Electronic device 300 can also communicate with one or more external devices 300' (e.g., keyboard, pointing device, Bluetooth device, etc.), enabling users to communicate with devices that interact with electronic device 300, and / or any device (e.g., router, modem, etc.) that allows electronic device 300 to communicate with one or more other computing devices. This communication can be performed via input / output (I / O) interface 350. Furthermore, electronic device 300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 360. Network adapter 360 can communicate with other modules of electronic device 300 via bus 330. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0162] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware.

[0163] Therefore, according to a fourth specific embodiment of the present invention, the present invention provides a computer-readable medium. For example... Figure 4As shown, the technical solution according to the embodiments of the present invention can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard drive, etc.) or on a network, and includes several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the above-described method according to the embodiments of the present invention.

[0164] The software product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0165] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0166] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0167] The aforementioned computer-readable medium carries one or more programs, which, when executed by a device, cause the computer-readable medium to perform the functions of the second specific embodiment.

[0168] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0169] Through the description of the above embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions of the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of the present invention.

[0170] Exemplary embodiments of the present invention have been specifically shown and described above. It should be understood that the present invention is not limited to the detailed structures, arrangements, or implementations described herein; rather, the present invention is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. A method for calculating idle time periods of a third-party satellite ground station, characterized in that, Includes the following steps: S1: Obtain operational data from a third-party satellite ground station; S2: Extract feature parameters from operational data of third-party satellite ground stations; S3: Calculate the idleness score based on the feature parameters, and determine whether it is an idle period or a non-idle period based on the idleness score; S4: Set the time window size and use a sliding time window mechanism to scan the preset continuous time period. When a predetermined number of consecutive time windows are all idle periods, the third-party satellite ground station is determined to be an idle period.

2. The method for calculating idle time periods of a third-party satellite ground station according to claim 1, characterized in that, Also includes: S5: Verify and optimize continuous idle periods.

3. The method for calculating idle time periods of a third-party satellite ground station according to claim 1, characterized in that, In step S1, the operational data of the third-party satellite ground station includes: communication task data, equipment status data, resource usage data, and timestamp data; The communication mission data refers to the communication mission information between the ground station and the satellite, including mission type, mission status, and mission duration. The equipment status data refers to the operating status and configuration information of the ground station equipment, including antenna status, receiver status, and transmission status. The resource occupancy data refers to the occupancy status of ground station resources, which include antennas, channels, and processing units. The timestamp data includes the time information of data collection.

4. The method for calculating idle time periods of a third-party satellite ground station according to claim 1, characterized in that, Step S1 also includes: preprocessing the operational data of the third-party satellite ground station, the preprocessing including: data cleaning, data normalization and time alignment.

5. The method for calculating the idle time period of a third-party satellite ground station according to claim 1, characterized in that, In step S2, the feature parameters include communication task features, device status features, resource usage features, and time features: The characteristics of the communication task include: task data, task density, task duration, and task type distribution; The equipment status characteristics include: equipment utilization rate, equipment idle rate, equipment status changes, and equipment load status; The resource occupancy characteristics include: resource occupancy rate, resource idle rate, and resource allocation status; The time characteristics include: time period characteristics, periodic characteristics, and duration characteristics.

6. The method for calculating the idle time period of a third-party satellite ground station according to claim 1, characterized in that, In step S3, the idleness score is calculated based on the feature parameters using the following formula: Idleness score = w1 × Communication task idleness + w2 × Device status idleness + w3 × Resource usage idleness + w4 × Time characteristic idleness; Among them, w1, w2, w3, and w4 are weighting coefficients.

7. The method for calculating idle time periods of a third-party satellite ground station according to claim 1, characterized in that, In step S3, determining whether a period is idle or non-idle based on the idleness score includes: Set an idle period threshold. When the idle score is lower than the idle period threshold, the time period is determined to be an idle period; otherwise, the time period is determined to be a non-idle period.

8. A calculation system for idle time periods of a third-party satellite ground station, characterized in that, include: The module includes a data acquisition module, a feature extraction module, an idle period calculation module, and a continuous idle period determination module. The data acquisition module is used to acquire operational data from third-party satellite ground stations; The feature extraction module is used to extract feature parameters from the operational data of a third-party satellite ground station; The idle time period calculation module is used to calculate the idleness score based on the feature parameters, and to determine whether it is an idle time period or a non-idle time period based on the idleness score; The continuous idle period determination module is used to set the time window size and scan the preset continuous time period using a sliding time window mechanism. When a predetermined number of consecutive time windows are all idle periods, the third-party satellite ground station is determined to be in a continuous idle period.

9. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for calculating the idle time period of a third-party satellite ground station as described in any one of claims 1-7.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for calculating the idle time period of a third-party satellite ground station as described in any one of claims 1-7.

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