Transmission line monitoring method and system based on phased array large beam satellite communication

Abstract

The present invention discloses a power transmission line monitoring method and system based on phased array large-beam satellite communication, which specifically relates to the technical field of data transmission monitoring, and is used to solve the existing problem that it is impossible to timely warn of hidden dangers in the communication quality of phased array large-beam satellite communication; by combining the fault probability and transmission fault frequency data, the risk level of the transmission line area is determined, so that more centralized monitoring and maintenance can be implemented on the transmission lines with higher risks; through a comprehensive analysis of the probability of failure in the transmission line area, beam stability and signal uniformity, the data transmission risk of the transmission line can be comprehensively evaluated, and a warning can be issued in time; it brings higher monitoring efficiency and safety to the operation of the transmission line, significantly improves the stability and reliability of the power grid, and effectively reduces the operating cost and potential failure risks.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission monitoring, and more specifically, to a method and system for monitoring a power transmission line based on phased array large beam satellite communication. Background Art

[0002] Large-beam satellite communications based on phased arrays can monitor power transmission lines in remote or difficult-to-reach areas. Phased array large-beam satellite communications use phased array technology with multiple antenna units to dynamically adjust and control the direction of the transmitted or received signal beam. This technology can change the phase relationship between antenna units by electronic means, so that it can accurately point to a specific direction without physically moving the antenna, improving the directional transmission efficiency of the signal and reducing interference. Large beam means that the signal generated by the antenna has a wide coverage range and can cover a large area.

[0003] In the actual monitoring of transmission lines, if there is no timely warning of hidden dangers in the communication quality of phased array large-beam satellite communications, the monitoring center may not be able to receive timely notification of technical failures or abnormal conditions, which may cause small problems to evolve into larger failures and even cause widespread power outages.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method and system for monitoring a power transmission line based on phased array large-beam satellite communication to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The transmission line monitoring method based on phased array large beam satellite communication includes the following steps:

[0008] S1: Collect the operation data of the transmission line based on the sensor, and predict the probability of failure in the transmission line area through the operation data of the transmission line;

[0009] S2: Obtain the recent data transmission failure frequency in the transmission line area;

[0010] S3: Determine the risk of the transmission line area based on the recent data transmission failure frequency and the probability of failure in the transmission line area; the risk of the transmission line area includes high and low;

[0011] S4: When the risk in the transmission line area is high, the stability of the beam operation within the predetermined direction range is evaluated by real-time monitoring of the beam pointing deviation; the uniformity of the signal in the transmission line area is evaluated by monitoring the signal attenuation at the edge of the beam;

[0012] S5: Comprehensively analyze the predicted results of the probability of failure in the transmission line area, the stability of the beam operation within the predetermined direction range, and the uniformity of the signal in the transmission line area, evaluate the risk level of data transmission of the operation data in the transmission line area, and issue an early warning on the data transmission availability in the transmission line area.

[0013] In a preferred embodiment, in S1, specifically:

[0014] Deploy sensors along transmission lines to collect operational data;

[0015] Use logistic regression model to build a fault prediction model: Where P(y=1|x) is the fault occurrence probability index generated by the fault prediction model; β0, β1, ..., β n are the model parameters; x1,...,x n are the features extracted from the running data; y is the target variable of the model; n is the number of features in the model; and e is the base of the natural logarithm.

[0016] In a preferred embodiment, in S2, specifically:

[0017] Define data transmission failure; extract event records that meet the definition of data transmission failure from the database;

[0018] The total number of data transmission failures within a preset time period is obtained, and the ratio of the total number of data transmission failures within the preset time period to the length of the preset time period is marked as the data transmission failure frequency.

[0019] In a preferred embodiment, in S3, specifically:

[0020] Set a fault probability threshold; compare the fault probability index with the fault probability threshold:

[0021] When the fault occurrence probability index is greater than the fault occurrence probability threshold, it is determined that a large fault prediction probability signal is generated; when the fault occurrence probability index is less than or equal to the fault occurrence probability threshold, it is determined that a small fault prediction probability signal is generated;

[0022] Set a data transmission failure frequency threshold; compare the data transmission failure frequency with the data transmission failure frequency threshold:

[0023] When the data transmission fault frequency is greater than the data transmission fault frequency threshold, it is determined that a large transmission fault frequency signal is generated; when the data transmission fault frequency is less than or equal to the data transmission fault frequency threshold, it is determined that a small transmission fault frequency signal is generated;

[0024] When a small fault prediction probability signal is generated and a small transmission fault frequency signal is generated, the risk of the transmission line area is determined to be low, otherwise, the risk of the transmission line area is determined to be high.

[0025] In a preferred embodiment, in S4, the stability of the beam operating within a predetermined direction range is evaluated by real-time monitoring of the beam's directional deviation; specifically:

[0026] Define the target beam direction of the satellite antenna and record the target beam direction of the satellite antenna as θ target ;

[0027] Collect beam pointing data in real time; record the beam direction obtained in each measurement as θ t , where t represents the time point of measurement;

[0028] Assign a weight ω to each measured beam direction t ;

[0029] Calculate the deviation between the beam direction and the target direction |θ t -θ target |;

[0030] Calculate the beam deviation stability index, which is expressed as: Where WOSI is the beam offset stability index and m is the number of measurements of the beam direction.

[0031] In a preferred embodiment, the signal uniformity in the transmission line area is evaluated by monitoring the signal attenuation at the edge of the beam, specifically:

[0032] Divide the transmission line area into multiple sub-areas;

[0033] The signal strength is measured in each sub-area; the signal strength measurement result of each sub-area is recorded as S u , where u represents the sub-region index;

[0034] The signal strength of the collected sub-areas is normalized; the normalized signal strength calculation formula is: Among them, p u is the normalized signal strength of the u-th sub-region, S u is the signal strength of the u-th sub-area, N is the number of sub-areas into which the transmission line area is divided, and j is the summation index;

[0035] According to the normalized signal strength, the regional signal information entropy is calculated using the information entropy formula: Among them, Hj is the regional signal information entropy.

[0036] In a preferred embodiment, in S5, specifically:

[0037] The fault probability index, beam offset stability index and regional signal information entropy are normalized, and the normalized fault probability index, beam offset stability index and regional signal information entropy are assigned preset proportional coefficients respectively, and the line data transmission risk warning coefficient is calculated;

[0038] Set the line data transmission risk warning threshold; compare the line data transmission risk warning coefficient with the line data transmission risk warning threshold, and issue a warning on the data transmission availability in the transmission line area:

[0039] When the line data transmission risk warning coefficient is greater than the line data transmission risk warning threshold, it is determined that a transmission available stability difference signal is generated, and early warning measures are taken according to the generated transmission available stability difference signal;

[0040] When the line data transmission risk warning coefficient is less than or equal to the line data transmission risk warning threshold, it is determined that a transmission available stable normal signal is generated.

[0041] On the other hand, the present invention provides a power transmission line monitoring system based on phased array large beam satellite communication, including an operation fault prediction module, a transmission fault analysis module, a regional risk judgment module, a beam stability assessment module, a regional uniformity assessment module and a transmission risk warning module;

[0042] The operation fault prediction module collects the operation data of the transmission line based on the sensor, and predicts the probability of failure in the transmission line area through the operation data of the transmission line;

[0043] The transmission fault analysis module obtains the recent data transmission fault frequency in the transmission line area;

[0044] The regional risk judgment module determines the risk of the transmission line area according to the recent data transmission failure frequency and the probability of failure in the transmission line area; the risk of the transmission line area includes high and low;

[0045] When the risk in the transmission line area is high, the beam stability assessment module evaluates the stability of the beam within the predetermined direction range by monitoring the beam pointing deviation in real time;

[0046] The regional uniformity assessment module assesses the uniformity of the signal within the transmission line area by monitoring the signal attenuation at the edge of the beam;

[0047] The transmission risk warning module conducts a comprehensive analysis of the predicted results of the probability of failure in the transmission line area, the stability of the beam operation within the predetermined direction range, and the uniformity of the signal in the transmission line area, evaluates the risk level of data transmission of the operating data in the transmission line area, and issues a warning on the data transmission availability in the transmission line area.

[0048] The technical effects and advantages of the power transmission line monitoring method and system based on phased array large beam satellite communication of the present invention are as follows:

[0049] 1. By collecting operating data and predicting the probability of failure in the transmission line area, potential failures can be identified early and appropriate measures can be taken before failures occur, thereby reducing the risk of power outages and equipment damage. The system evaluates the frequency of data transmission failures and provides an assessment of the frequency of data transmission failures on the transmission line. By combining the failure probability and transmission failure frequency data, the risk level of the transmission line area can be accurately determined, so that more centralized monitoring and maintenance can be implemented on transmission lines with higher risks. In particular, on high-risk transmission lines, communication quality is ensured by monitoring beam pointing deviation and signal attenuation.

[0050] 2. Through comprehensive analysis of the probability of failure in the transmission line area, beam stability and signal uniformity, the data transmission risk of the transmission line can be fully evaluated and early warning can be issued in a timely manner. This comprehensive analysis method not only enhances the accuracy of the early warning, but also enables the early warning system to provide sufficient response time before the problem develops to a dangerous level, thereby greatly reducing transmission failures and related economic losses caused by data transmission problems. Through advanced phased array large beam satellite communication technology and precise data analysis, higher monitoring efficiency and safety are brought to the operation of transmission lines, significantly improving the stability and reliability of the power grid, and effectively reducing operating costs and potential failure risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of a power transmission line monitoring method based on phased array large beam satellite communication of the present invention;

[0052] Figure 2 The present invention is a schematic structural diagram of a power transmission line monitoring system based on phased array large beam satellite communication. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Example 1

[0055] For a better understanding, the monitoring of transmission lines in remote or difficult-to-reach areas based on phased array large beam satellite communications is as follows:

[0056] Sensors installed on transmission lines first collect key operating data, such as current, voltage, temperature, etc. These operating data are transmitted to nearby base stations and sent to satellites equipped with phased array antennas through ground equipment. Phased array antennas can dynamically adjust the beam direction, efficiently cover a wide geographical area, and transmit data to a remote monitoring center. At the monitoring center, the received information is processed and analyzed through advanced data analysis technology to predict potential failures and risks, and realize real-time monitoring and management of remote transmission lines. This approach greatly improves the reliability and timeliness of monitoring, ensuring the stable operation of the power grid, especially in areas that are difficult to cover with traditional ground monitoring.

[0057] Figure 1 The present invention provides a transmission line monitoring method based on phased array large beam satellite communication, which comprises the following steps:

[0058] S1: Collect the operation data of the transmission line based on the sensor, and predict the probability of failure in the transmission line area through the operation data of the transmission line.

[0059] S2: Obtain the recent data transmission failure frequency in the transmission line area.

[0060] S3: Determine the risk of the transmission line area based on the recent data transmission failure frequency in the transmission line area and the probability of failure in the transmission line area; the risk of the transmission line area includes high and low.

[0061] S4: When the risk in the transmission line area is high, the stability of the beam operation within the predetermined direction range is evaluated by real-time monitoring of the beam pointing deviation; the uniformity of the signal in the transmission line area is evaluated by monitoring the signal attenuation at the edge of the beam.

[0062] S5: Comprehensively analyze the predicted results of the probability of failure in the transmission line area, the stability of the beam operation within the predetermined direction range, and the uniformity of the signal in the transmission line area, evaluate the risk level of data transmission of the operation data in the transmission line area, and issue an early warning on the data transmission availability in the transmission line area.

[0063] In S1, the operation data of the transmission line is collected based on the sensor, and the probability of failure in the transmission line area is predicted through the operation data of the transmission line. Specifically:

[0064] Deploy various sensors along the transmission lines to collect necessary operation data. Including but not limited to:

[0065] Temperature Sensors: Monitor the temperature of wires and other equipment.

[0066] Current and Voltage Sensors: Record the current and voltage levels of transmission lines in real time.

[0067] Vibration sensors: Monitor the physical stability of transmission lines, such as vibrations caused by wind or other external factors.

[0068] Insulation monitoring equipment: detect insulation performance and prevent insulation failure.

[0069] Among them, the collection of operational data should be carried out at a high frequency to ensure sufficient data granularity for subsequent analysis.

[0070] The collected data needs to be preprocessed to clean and standardize to ensure the accuracy of the analysis. The steps include:

[0071] Data cleaning: Remove obvious erroneous data and outliers.

[0072] Data normalization: Converting all data to the same scale and range to facilitate comparison and analysis.

[0073] Based on physical knowledge and historical failure data, relevant features are developed, which can effectively predict potential failures. For example:

[0074] Load Coefficient of Variation: Calculates the ratio of the standard deviation of current or voltage to the mean value within a certain time window.

[0075] Temperature-current correlation characteristics: Analyze the correlation between temperature changes and current increases.

[0076] The existing technology for calculating the load variation coefficient and the temperature-current correlation characteristics is relatively mature and will not be described in detail here.

[0077] Use statistical and machine learning methods to build a fault prediction model. Use a logistic regression model, whose basic form is: Where P(y=1|x) is the fault occurrence probability index generated by the fault prediction model; β0, β1, ..., β n is the model parameter (representing the influence weight of each feature on the fault prediction probability); x1,...,x n are features extracted from operational data.

[0078] Among them, y is the target variable (or dependent variable) of the model. In this case, it is a binary variable indicating whether a transmission line failure will occur. Usually, y=1 indicates that a failure has occurred, while y=0 indicates that no failure has occurred.

[0079] x represents the independent variables (or explanatory variables, features) of the model, which are various indicators extracted from the data to predict the possibility of failure. These features are based on the transmission line operation data collected from sensors, such as load variation coefficient and temperature-current correlation characteristics.

[0080] n is the number of features in the model. e is the base of the natural logarithm, which is approximately equal to 2.71828.

[0081] In the application of transmission line monitoring, feature selection and extraction are key steps. These features directly affect the performance of the model. The following are common features and how to obtain them:

[0082] Temperature change rate: From the data collected by the temperature sensor, the maximum, minimum and average temperature over a certain period of time are calculated to further obtain the temperature change rate. This helps detect possible failures due to reasons such as overheating.

[0083] Current load fluctuation: Use the data monitored by the current sensor to calculate the standard deviation or coefficient of variation (CV = standard deviation / mean value) to evaluate the stability and abnormal fluctuation of the current.

[0084] Historical failure rate: Combine historical maintenance and failure record data to calculate the historical failure rate for a specific area or transmission line. This can be used as an important indicator to predict future failures.

[0085] Environmental influencing factors: Considering the impact of the external environment on the transmission line, data such as wind speed and humidity can be extracted from meteorological station data or environmental monitoring sensors to analyze their correlation with the occurrence of faults.

[0086] Insulation performance indicators: Data obtained from insulation monitoring equipment, such as insulation resistance values, can be used to evaluate the insulation condition of transmission lines. Poor insulation performance is a common cause of failures.

[0087] After appropriate mathematical processing and statistical analysis, these features are input into the logistic regression model, which predicts whether the transmission line is likely to fail by learning the relationship between these data. Using this data-driven approach for fault prediction can significantly improve the accuracy and efficiency of the prediction.

[0088] The fault probability index indicates the possibility of a transmission line failure; the larger the fault probability index, the greater the probability of a failure in the transmission line area.

[0089] The transmission line area is the area of ​​the transmission line corresponding to the range covered by the phased array large beam satellite.

[0090] In S2, the recent data transmission failure frequency in the transmission line area is obtained, specifically:

[0091] In order to obtain information about data transmission failures, communication status monitoring modules need to be integrated into the system, which can record any data transmission interruption or loss in real time.

[0092] This is achieved by setting up detection points on the data transmission link, which can identify and record events such as packet loss, transmission delay or data corruption. Each detection point generates a log about the data transmission status, which is then used to analyze and calculate the frequency of data transmission failures.

[0093] All collected data, including fault logs, are stored in a database system. The database should not only support efficient data writing operations, but also complex query and data analysis operations. The data should be organized in a way that facilitates quick retrieval of fault events and related operating data within a specific time period.

[0094] The calculation of data transmission failure frequency relies on a detailed analysis of the stored failure logs. The specific calculation steps include:

[0095] Definition of data transmission failure: Data packet loss exceeding a certain percentage, continuous communication interruption exceeding a preset time, etc. are defined as data transmission failure.

[0096] Event records that meet the definition of data transmission failure are extracted from the database.

[0097] The total number of data transmission failures within a preset time period is obtained, and the ratio of the total number of data transmission failures within the preset time period to the length of the preset time period is marked as the data transmission failure frequency.

[0098] Among them, the preset time period is the recent time, and its range is set according to actual monitoring needs.

[0099] The greater the frequency of data transmission failures, the greater the risk of transmission of the operating data of the transmission line. By accurately monitoring and analyzing data transmission failures, problems that may lead to larger-scale power outages can be effectively identified and prevented, thereby ensuring the stability and security of power supply. This process not only improves the technical management level of the power grid, but also enhances the ability to respond to emergencies.

[0100] In S3, the risk of the transmission line area is determined based on the recent data transmission failure frequency in the transmission line area and the probability of failure in the transmission line area; specifically:

[0101] The fault probability threshold is set. The fault probability threshold is set by professional and technical personnel in this field based on the size of the fault probability index and other actual conditions such as the safety requirement standards for the impact of the fault probability on the risk of the transmission line area in practice, and will not be repeated here.

[0102] Compare the failure probability index to the failure probability threshold:

[0103] When the fault probability index is greater than the fault probability threshold, a large fault prediction probability signal is generated, and the probability of a fault in the transmission line area is high. The probability of a fault in the transmission line area is higher than the normal level, suggesting an increased risk state.

[0104] Based on the generated large signal of fault prediction probability, the following actions can be taken:

[0105] Enhanced monitoring: Increase the frequency of monitoring in this area, using more detailed data collection and analysis to more precisely diagnose problems.

[0106] Preventive maintenance: Schedule maintenance work in advance based on specific areas where the model indicates a high probability of failure, such as replacing components that are suspected to be about to fail, and clearing or reinforcing external factors that may affect line stability (such as tree branches, ice and snow, etc.).

[0107] Prepare for emergency response: Develop emergency response plans so that you can respond quickly when a failure occurs, reducing power outage time and potential losses.

[0108] When the fault probability index is less than or equal to the fault probability threshold, it is determined that a small fault prediction probability signal is generated. At this time, the probability of a fault in the transmission line area is small; this indicates that the fault probability in the transmission line area is within a normal or acceptable range.

[0109] The data transmission failure frequency threshold is set. The data transmission failure frequency threshold is set by professional and technical personnel in this field based on the size of the data transmission failure frequency, as well as other actual conditions such as the safety requirement standards for the impact of the data transmission failure frequency on the risk of the transmission line area, and will not be repeated here.

[0110] Compare the data transmission failure frequency to the data transmission failure frequency threshold:

[0111] When the data transmission failure frequency is greater than the data transmission failure frequency threshold, it is determined that a large transmission failure frequency signal is generated. This indicates that the frequency of data transmission failures in the operation data of the transmission line is high in the near future. This indicates that data transmission failures in the transmission line have occurred frequently in the near future, which may be due to equipment aging, environmental interference or system configuration problems.

[0112] Based on the generated transmission fault frequency large signal, measures that can be taken may include:

[0113] Technical inspection and optimization: Check communication equipment and configurations to find technical or physical obstacles that may cause data transmission problems and make necessary replacements or configuration adjustments.

[0114] Enhance communication paths: It may be necessary to add redundant paths to improve the reliability of data transmission, such as using multiple communication technologies to ensure the transmission of important data.

[0115] System upgrade: Upgrade or replace aging communication equipment to meet higher performance requirements and improve data transmission quality.

[0116] When the data transmission failure frequency is less than or equal to the data transmission failure frequency threshold, it is determined that a small transmission failure frequency signal is generated. This indicates that the frequency of data transmission failures for the operation data of the transmission line in the near future is small and relatively normal; this indicates that the data transmission frequency is in a normal or low-risk state.

[0117] When a small fault prediction probability signal is generated and a small transmission fault frequency signal is generated, the risk of the transmission line area is determined to be low, otherwise, the risk of the transmission line area is determined to be high.

[0118] When the probability of fault prediction is low and the frequency of data transmission failure is also low, it can be reasonably inferred that the entire transmission line area is in good operating condition and the reliability of the system is high. Therefore, it is appropriate to judge the overall risk level as low. In this case, the expected operation and maintenance pressure is small, the probability of failure is low, and the data credibility of the monitoring system is high, so any potential problems can be discovered and handled in time. On the contrary, if any signal fails to meet the low risk standard (i.e., the probability of fault prediction is high or the frequency of data transmission failure is high), it indicates that there are certain unstable factors or the data of the monitoring system is incomplete or inaccurate, which may mask potential problems or mislead operation and maintenance decisions.

[0119] High-risk transmission line areas mean more frequent maintenance and troubleshooting needs, so it is particularly important to ensure that communication links in these areas are stable and reliable. Phased array large-beam satellite communications can cover a wide range of areas, especially those that are geographically remote or difficult to reach. Through this communication method, monitoring data and alarm information can be transmitted in real time, ensuring that the monitoring system can operate effectively even under extreme or complex environmental conditions. In high-risk transmission line areas, any failure of the monitoring system may result in an inability to respond to potential faults or accidents in a timely manner, thereby increasing the risk of system losses or more serious safety accidents. Therefore, it is particularly important to monitor the potential hidden dangers of phased array large-beam satellite communications. As the risk of transmission line areas increases, it becomes more important to maintain the availability of phased array large-beam satellite communications and monitor their potential operational hazards. This can not only improve the overall safety and reliability of the power grid, but also optimize the allocation of resources and the efficiency of emergency response.

[0120] In S4, when the risk in the transmission line area is high, the stability of the beam operation within the predetermined direction range is evaluated by real-time monitoring of the beam pointing deviation; specifically:

[0121] Define the target beam direction of the satellite antenna and record the target beam direction of the satellite antenna as θ target ,θ target Usually based on the direction of the area with best coverage of the transmission line.

[0122] The phased array antenna system collects the beam pointing data in real time. The beam direction obtained in each measurement is recorded as θ t , where t represents the time point of measurement.

[0123] Assign a weight ω to each measured beam direction t ,The weights can be assigned based on the importance of the ,measurement time, weather conditions, and other factors that may affect the beam pointing ,accuracy.

[0124] For each measured beam direction, calculate the deviation between the beam direction and the target direction |θ t -θ target |.

[0125] To reduce the impact of large deviations, an exponential function is used To adjust the impact of each deviation so that larger deviations contribute less to the result.

[0126] Calculate the beam deviation stability index, which is expressed as: Where WOSI is the beam offset stability index and m is the number of measurements of the beam direction.

[0127] The beam deviation stability index is used to evaluate the stability of the beam in a predetermined range of directions:

[0128] The higher the beam deviation stability index is, the smaller the beam deviation is and the closer it is to the target direction. This means that the system is very stable and the beam control is precise, which is more conducive to the transmission of the operation data of the transmission line.

[0129] The lower the beam deviation stability index is, the greater or more frequent the beam deviation is, indicating possible system problems or environmental interference, which is more unfavorable for the transmission of operation data of the transmission line.

[0130] When the beam drift stability index is low, further analysis is needed on the potential causes of the drift, which may include equipment aging, software configuration issues, external environmental interference (such as the impact of climate change), or physical obstacles.

[0131] Adjust phased array parameters: Based on the analysis results, adjust the phase control parameters of the phased array, optimize the beamforming algorithm, or update the control software to improve the beam pointing accuracy.

[0132] Hardware Inspection and Maintenance: Check the physical condition of the phased array and its components and perform repairs or replace damaged parts if necessary.

[0133] By introducing a weighting mechanism and an exponential decay function, the beam offset stability index not only simply measures the average deviation, but can also more effectively handle occasional large deviations, providing a smoother and more robust stability assessment. Allowing real-time monitoring and evaluation of beam stability enables operators to promptly detect and correct beam offset problems, preventing transmission monitoring data loss or errors that may be caused by communication failures. By accurately monitoring beam stability, satellite communication resources can be more effectively configured, ensuring that data transmission at critical moments is not interfered with, and enhancing the reliability and efficiency of the entire transmission line monitoring system.

[0134] By monitoring the signal attenuation at the edge of the beam, the uniformity of the signal in the transmission line area is evaluated, specifically:

[0135] In phased array large beam satellite communications, the beam is composed of signals transmitted by multiple satellites, which form a "beam" covering a specific area. The signal attenuation at the edge of the beam refers to how the signal strength decreases with distance or other factors in the edge area covered by the beam. Analyze the collected signal data to determine whether the signal is evenly distributed throughout the area covered by the satellite beam, without significant signal gaps or overly concentrated areas.

[0136] Information entropy is a basic concept in information theory, which is used to quantify the uncertainty or randomness of information. In the field of signal processing, information entropy can be used to evaluate the uniformity of signal distribution in a region. In theory, the higher the information entropy, the more uniform the signal distribution in the region. On the contrary, if the signal strength in some areas is much higher than that in other areas, the information entropy will be relatively low, indicating that the signal distribution is uneven.

[0137] Divide the transmission line area into multiple sub-areas to ensure that each sub-area can receive satellite signals independently. This step is to be able to measure and compare the signal strength in each sub-area in detail.

[0138] In each sub-area, the signal strength is measured using a phased array antenna system. Data is collected over a certain period of time to enable reliable statistical analysis. The signal strength measurement result of each sub-area is recorded as S u , where u represents the sub-region index.

[0139] In order to make an effective comparison, the signal intensity of the collected sub-areas is normalized. The normalized signal intensity calculation formula is: Among them, p u is the normalized signal strength of the u-th sub-region, S u is the signal strength of the u-th sub-area, N is the number of sub-areas divided by the transmission line area, j is the summation index, and j is used to represent different sub-areas in the summation process. Refers to the signal strength S of all sub-areas j Here j iterates from 1 to N, and each S j represents the signal strength of the jth sub-region.

[0140] Among them, j is used to traverse each sub-area to calculate the total signal strength when normalizing the signal strength. This process makes the signal strength S of each sub-area u can be represented by its relative proportion p of the signal strength in the entire transmission line area u Indicated.

[0141] According to the normalized signal strength, the regional signal information entropy is calculated using the information entropy formula: Among them, Hj is the regional signal information entropy, which is used to quantify the uniformity of signal distribution.

[0142] When the regional signal information entropy is large, it means that the signal is relatively evenly distributed in the transmission line area. In theory, the maximum information entropy occurs when the signal strength in all sub-areas is exactly equal, that is, the normalized signal strength in each sub-area is the same. In the case of completely uniform signal distribution, the signal strength of each sub-area contributes equally to the overall information entropy, and the signal strength of no sub-area deviates significantly from the average level. This distribution reduces the predictability and uncertainty of the overall system.

[0143] When the regional signal information entropy is small, it indicates that the signal is unevenly distributed in the transmission line area. In extreme cases, if all signal strength is concentrated in one or a few sub-areas, while the signal strength in other sub-areas is extremely low or close to zero, the information entropy can be close to zero. In this case, the distribution of the signal is highly uneven, resulting in significantly higher signal strength in some sub-areas than in other areas. This highly concentrated signal distribution increases the predictability of the system (because the signal is mainly concentrated in a few areas), but it is usually not a desirable state because it indicates that the signal coverage has obvious dead spots or weak areas.

[0144] In S5, the prediction results of the probability of failure in the transmission line area, the stability of the beam operation within the predetermined direction range, and the uniformity of the signal in the transmission line area are comprehensively analyzed to evaluate the data transmission risk level of the operation data in the transmission line area and issue an early warning for the data transmission availability in the transmission line area, specifically:

[0145] A high predicted probability of transmission line failure means that there may be physical or electrical faults in the transmission line, which may indirectly affect the operation of data acquisition and transmission equipment. For example, if a transmission line fails, it may cause unstable power supply, which in turn affects the power supply of monitoring equipment and the normal operation of communication equipment, thereby increasing the risk of data transmission interruption or data loss.

[0146] The beam offset stability index is an indicator to measure the stability of the beam direction. In an ideal communication system, the beam should be stably pointed to the predetermined target area to ensure the efficiency and accuracy of data transmission. When the beam offset stability index is low, the following adverse effects may occur:

[0147] Communication interruption: If the beam is not pointed correctly or deviates, it may cause communication interruption, especially in the case of long transmission distance or weak signal.

[0148] Signal quality degradation: Even if beam deviation does not lead to a complete loss of communication, it may cause the signal strength to become unstable in the target area, affecting the quality of data transmission.

[0149] Energy inefficiency: Beam deviation may require the system to make more frequent adjustments and corrections, consuming more energy and reducing the energy efficiency of the entire system.

[0150] Increased maintenance costs: Frequent beam adjustments and corrections not only increase energy consumption, but can also increase maintenance costs, especially when manual adjustments to antennas or system settings are required.

[0151] The result of regional signal information entropy has a direct impact on the communication quality. In phased array large beam satellite communications, ideally, the signal should be evenly distributed in the transmission line area to ensure that the entire transmission line area can receive high-quality data. The calculation result of regional signal information entropy can reflect the uniformity of signal distribution:

[0152] When the calculated regional signal information entropy is low, it indicates that the signal is unevenly distributed in the transmission line area, and the signal strength in some sub-areas is much stronger than in other sub-areas. This uneven distribution may lead to the following problems:

[0153] Data loss: Sub-areas with weak signals may not be able to effectively receive the operation data of the transmission lines, resulting in data loss.

[0154] Increased Errors: Monitoring systems rely on data collected from the entire transmission line area for analysis and decision making. Uneven signal distribution may increase errors in data processing and affect the accuracy of fault detection and prediction.

[0155] Degraded system reliability: Uneven signal distribution increases the vulnerability of the system in certain sub-areas, which may affect the overall reliability of the system at critical moments.

[0156] The fault probability index, beam offset stability index and regional signal information entropy are normalized, and the normalized fault probability index, beam offset stability index and regional signal information entropy are assigned preset proportional coefficients respectively, and the line data transmission risk warning coefficient is calculated.

[0157] Among them, the specific implementation method of calculating the line data transmission risk warning coefficient is not specifically limited here. It can be realized by assigning the normalized fault probability index, beam offset stability index and regional signal information entropy to preset proportional coefficients respectively to calculate the line data transmission risk warning coefficient. In order to realize the technical solution of the present invention, the present invention provides a specific calculation method of the line data transmission risk warning coefficient, and its expression is: Among them, Xc yxis the line data transmission risk warning coefficient, δ1, δ2, and δ3 are the preset proportional coefficients of the fault probability index, beam offset stability index, and regional signal information entropy, respectively, and δ1, δ2, and δ3 are all greater than 0.

[0158] The setting of the preset proportionality coefficient is determined based on the relative importance of each indicator to the risk of data transmission on the transmission line. In practice, these preset proportionality coefficients are usually set by professional and technical personnel through historical data analysis, professional and technical personnel in this field, and the operating environment and specific requirements of the system. For example, if the beam offset has a greater impact on the communication quality in a certain area, the beam offset stability index may be given a higher weight. On the contrary, if the equipment failure rate in a certain area is high, the failure probability index will be given a larger proportionality coefficient. In this way, the preset proportionality coefficient can flexibly reflect the actual influence of different factors in a specific application environment, so that the line data transmission risk warning coefficient can more accurately reflect the actual risk situation.

[0159] From the expression of the line data transmission risk warning coefficient, it can be seen that the larger the line data transmission risk warning coefficient is, the higher the data transmission risk level of the transmission line area operation data is.

[0160] The line data transmission risk warning threshold is set. The line data transmission risk warning threshold is set by professional and technical personnel in this field based on the size of the line data transmission risk warning coefficient and other actual conditions such as the safety requirements for data transmission of phased array large-beam satellites monitoring transmission lines. It will not be repeated here.

[0161] Compare the line data transmission risk warning coefficient with the line data transmission risk warning threshold to issue a warning on the data transmission availability in the transmission line area:

[0162] When the line data transmission risk warning coefficient is greater than the line data transmission risk warning threshold, a transmission available stability difference signal is generated, indicating that the current data transmission risk is higher than the acceptable level, which may be due to signal attenuation, beam deviation, equipment failure or other environmental interference that leads to unstable data transmission. In this case, the integrity and accuracy of the data may be threatened, and immediate action is required to prevent data loss or erroneous transmission.

[0163] Based on the generated transmission availability stability difference signal, the early warning measures include:

[0164] Immediately implement more intensive monitoring of the affected areas, including increasing the frequency of data collection and using more sophisticated analytical tools to determine the root cause of the problem.

[0165] Make adjustments to the phased array system, such as recalibrating the beam pointing of the antenna array, to ensure that the beam is properly aligned with the target area and reduce deviation.

[0166] Enable backup communication links or add a data retransmission mechanism to ensure that critical data can be transmitted reliably.

[0167] Perform rapid repairs on detected device failures or configuration errors, which may include replacing damaged components or updating software settings.

[0168] When the line data transmission risk warning coefficient is less than or equal to the line data transmission risk warning threshold, it is determined that a transmission available stable normal signal is generated, which means that the current data transmission risk is at or below the preset safety level, and the data transmission is considered stable and reliable. In this case, the system is in good operating condition and no emergency intervention is required.

[0169] By comparing the line data transmission risk warning coefficient with the preset threshold and generating the corresponding warning signal accordingly, this process greatly improves the response efficiency and safety of the transmission line monitoring system. Specifically, it enables the monitoring system to detect and take preventive measures in time before potential risks develop to dangerous levels, thereby effectively preventing data loss or transmission errors. In addition, the system is able to dynamically adjust its monitoring strategy and resource allocation based on real-time risk assessment, optimize operation and maintenance costs, and improve the overall stability and reliability of the system. This not only reduces the operational risks that may be caused by inaccurate monitoring data, but also enhances the operating efficiency and safety management level of the transmission network.

[0170] Example 2

[0171] The difference between Example 2 of the present invention and Example 1 is that this example introduces a power transmission line monitoring system based on phased array large beam satellite communication.

[0172] Figure 2 A structural schematic diagram of a power transmission line monitoring system based on phased array large-beam satellite communication of the present invention is given. The power transmission line monitoring system based on phased array large-beam satellite communication includes an operation fault prediction module, a transmission fault analysis module, a regional risk judgment module, a beam stability assessment module, a regional uniformity assessment module and a transmission risk warning module.

[0173] The operation fault prediction module collects the operation data of the transmission line based on the sensors, and predicts the probability of failure in the transmission line area through the operation data of the transmission line.

[0174] The transmission fault analysis module obtains the recent data transmission fault frequency in the transmission line area.

[0175] The regional risk judgment module determines the risk of the transmission line area based on the recent data transmission failure frequency and the probability of failure in the transmission line area; the risk of the transmission line area includes high and low.

[0176] When the risk in the transmission line area is high, the beam stability assessment module evaluates the stability of the beam operating within a predetermined direction range by monitoring the pointing deviation of the beam in real time.

[0177] The regional uniformity assessment module evaluates the uniformity of the signal within the transmission line area by monitoring the signal attenuation at the edge of the beam.

[0178] The transmission risk warning module conducts a comprehensive analysis of the predicted results of the probability of failure in the transmission line area, the stability of the beam operation within the predetermined direction range, and the uniformity of the signal in the transmission line area, evaluates the risk level of data transmission of the operating data in the transmission line area, and issues a warning on the data transmission availability in the transmission line area.

[0179] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0180] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0181] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0182] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0183] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0184] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0185] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0186] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0187] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0188] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A power transmission line monitoring method based on phased array large beam satellite communication, characterized in that: The steps include: S1: Collect the operation data of the transmission line based on the sensor, and predict the probability of failure in the transmission line area through the operation data of the transmission line; S2: Obtain the recent data transmission failure frequency in the transmission line area; S3: Determine the risk of the transmission line area based on the recent data transmission failure frequency and the probability of failure in the transmission line area; the risk of the transmission line area includes high and low; S4: When the risk in the transmission line area is high, the stability of the beam operation within the predetermined direction range is evaluated by real-time monitoring of the beam pointing deviation; the uniformity of the signal in the transmission line area is evaluated by monitoring the signal attenuation at the edge of the beam; S5: Comprehensively analyze the predicted results of the probability of failure in the transmission line area, the stability of the beam operation within the predetermined direction range, and the uniformity of the signal in the transmission line area, evaluate the risk level of data transmission of the operation data in the transmission line area, and issue an early warning on the data transmission availability in the transmission line area.

2. The method for monitoring power transmission lines based on phased array large beam satellite communication according to claim 1, characterized in that: In S1, specifically: Deploy sensors along transmission lines to collect operational data; Use logistic regression model to build a fault prediction model: Where P(y=1 / x) is the fault occurrence probability index generated by the fault prediction model; β0, β1, ..., β n are the model parameters; x1,...,x n are the features extracted from the running data; y is the target variable of the model; n is the number of features in the model; and e is the base of the natural logarithm.

3. The method for monitoring power transmission lines based on phased array large beam satellite communication according to claim 2, characterized in that: In S2, specifically: Define data transmission failure; extract event records that meet the definition of data transmission failure from the database; The total number of data transmission failures within a preset time period is obtained, and the ratio of the total number of data transmission failures within the preset time period to the length of the preset time period is marked as the data transmission failure frequency.

4. The method for monitoring power transmission lines based on phased array large beam satellite communication according to claim 3, characterized in that: In S3, specifically: Set a fault probability threshold; compare the fault probability index with the fault probability threshold: When the fault occurrence probability index is greater than the fault occurrence probability threshold, it is determined that a large fault prediction probability signal is generated; When the fault occurrence probability index is less than or equal to the fault occurrence probability threshold, it is determined that a small fault prediction probability signal is generated; Set a data transmission failure frequency threshold; compare the data transmission failure frequency with the data transmission failure frequency threshold: When the data transmission fault frequency is greater than the data transmission fault frequency threshold, it is determined that a large transmission fault frequency signal is generated; when the data transmission fault frequency is less than or equal to the data transmission fault frequency threshold, it is determined that a small transmission fault frequency signal is generated; When a small fault prediction probability signal is generated and a small transmission fault frequency signal is generated, the risk of the transmission line area is determined to be low, otherwise, the risk of the transmission line area is determined to be high.

5. The method for monitoring power transmission lines based on phased array large beam satellite communication according to claim 4, characterized in that: In S4, the stability of the beam operating within the predetermined direction range is evaluated by monitoring the beam pointing deviation in real time; Specifically: Define the target beam direction of the satellite antenna and record the target beam direction of the satellite antenna as θ target ; Collect beam pointing data in real time; The beam direction obtained in each measurement is recorded as θ t , where t represents the time point of measurement; Assign a weight ω to each measured beam direction t ; Calculate the deviation between the beam direction and the target direction |θ t -θ target |; Calculate the beam deviation stability index, which is expressed as: Where WOSI is the beam offset stability index and m is the number of measurements of the beam direction.

6. The method for monitoring power transmission lines based on phased array large beam satellite communication according to claim 5, characterized in that: By monitoring the signal attenuation at the edge of the beam, the uniformity of the signal in the transmission line area is evaluated, specifically: Divide the transmission line area into multiple sub-areas; The signal strength is measured in each sub-area; the signal strength measurement result of each sub-area is recorded as S u , where u represents the sub-region index; The signal strength of the collected sub-areas is normalized; the normalized signal strength calculation formula is: Among them, p u is the normalized signal strength of the u-th sub-region, S u is the signal strength of the u-th sub-area, N is the number of sub-areas into which the transmission line area is divided, and j is the summation index; According to the normalized signal strength, the regional signal information entropy is calculated using the information entropy formula: Among them, Hj is the regional signal information entropy.

7. The method for monitoring power transmission lines based on phased array large beam satellite communication according to claim 6, characterized in that: In S5, specifically: The fault probability index, beam offset stability index and regional signal information entropy are normalized, and the normalized fault probability index, beam offset stability index and regional signal information entropy are assigned preset proportional coefficients respectively, and the line data transmission risk warning coefficient is calculated; Set the line data transmission risk warning threshold; Compare the line data transmission risk warning coefficient with the line data transmission risk warning threshold to issue a warning on the data transmission availability in the transmission line area: When the line data transmission risk warning coefficient is greater than the line data transmission risk warning threshold, it is determined that a transmission available stability difference signal is generated, and early warning measures are taken according to the generated transmission available stability difference signal; When the line data transmission risk warning coefficient is less than or equal to the line data transmission risk warning threshold, it is determined that a transmission available stable normal signal is generated.

8. A power transmission line monitoring system based on phased array large beam satellite communication, used to implement the power transmission line monitoring method based on phased array large beam satellite communication according to any one of claims 1 to 7, characterized in that: It includes operation fault prediction module, transmission fault analysis module, regional risk judgment module, beam stability assessment module, regional uniformity assessment module and transmission risk warning module; The operation fault prediction module collects the operation data of the transmission line based on the sensor, and predicts the probability of failure in the transmission line area through the operation data of the transmission line; The transmission fault analysis module obtains the recent data transmission fault frequency in the transmission line area; The regional risk judgment module determines the risk of the transmission line area according to the recent data transmission failure frequency and the probability of failure in the transmission line area; the risk of the transmission line area includes high and low; When the risk in the transmission line area is high, the beam stability assessment module evaluates the stability of the beam within the predetermined direction range by monitoring the beam pointing deviation in real time; The regional uniformity assessment module assesses the uniformity of the signal within the transmission line area by monitoring the signal attenuation at the edge of the beam; The transmission risk warning module conducts a comprehensive analysis of the predicted results of the probability of failure in the transmission line area, the stability of the beam operation within the predetermined direction range, and the uniformity of the signal in the transmission line area, evaluates the risk level of data transmission of the operating data in the transmission line area, and issues a warning on the data transmission availability in the transmission line area.

Images