Power transmission line monitoring data transmission method based on Lora and 5G hybrid networking

Abstract

The invention relates to the technical field of power transmission line monitoring, and discloses a power transmission line monitoring data transmission method based on Lora and 5G hybrid networking, and the method comprises the steps: determining a network access mode through a hybrid networking selection algorithm, and generating a communication link through dual-module networking configuration; original monitoring data are processed by using a data framing compression algorithm, and a path is selected and verified through dynamic routing optimization; the modulation format is adjusted through the space-time alignment data of the multi-modal data fusion model and the adaptive modulation technology; optimizing transmission by using an abnormal data filtering algorithm and a dynamic priority scheduling program; and feedback learning mechanism evaluation performance and updating algorithm parameters. According to the invention, network adaptability, data transmission efficiency and reliability are improved, data fusion processing is optimized, adaptive optimization of transmission performance is realized, operation and maintenance cost is reduced, and safe and stable operation of a power system is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission line monitoring, and specifically to a method for transmitting monitoring data of transmission lines based on a hybrid network of Lora and 5G. Background Art

[0002] In today's power system, transmission lines are key infrastructure for power transmission, and their safe and stable operation is crucial for ensuring the reliability of power supply. With the continuous expansion of the power grid scale and the increase in the complexity of transmission lines, real-time and comprehensive monitoring of transmission lines has become a necessary means to ensure the safe operation of the power system. And the effective transmission of transmission line monitoring data is the core link to achieve accurate monitoring and analysis.

[0003] Traditional methods for transmitting transmission line monitoring data have many limitations. For transmission lines in some remote areas or complex terrains, due to insufficient network coverage, monitoring data cannot be transmitted back in a timely and stable manner. For example, in mountainous areas, deserts and other regions, it is difficult for conventional communication networks to achieve full coverage, resulting in data transmission obstacles at some monitoring points and unable to provide complete line operation information for maintenance personnel.

[0004] Existing single communication technologies cannot meet the requirements of different scenarios when transmitting transmission line monitoring data. Take 4G communication technology as an example. Although it can achieve data transmission at a certain rate in areas with good network coverage, in remote areas with weak signals, the transmission rate drops significantly, and there are high latency and packet loss rates. While Lora technology has the advantages of low power consumption and long-distance transmission, its transmission rate is relatively low and it is difficult to meet the rapid transmission requirements of a large amount of high-definition images, videos and other monitoring data.

[0005] With the continuous development of transmission line monitoring technology, the amount of data collected by monitoring devices is increasing day by day, and the data types are also more diverse, including temperature, humidity, stress, images, videos, etc. This puts higher requirements on the bandwidth and processing capacity of data transmission. Traditional data transmission methods lack an efficient data frame compression mechanism when facing these massive and diverse data, resulting in low transmission efficiency, increasing the network transmission burden, and even causing data congestion and loss.

[0006] In addition, in a complex transmission line environment, there are various interference factors, such as electromagnetic interference, weather changes, etc. These interferences will seriously affect the reliability of data transmission. Existing transmission methods lack effective coping strategies in path selection and anti-interference, and cannot dynamically adjust the transmission path and optimize transmission parameters according to real-time environmental changes, resulting in errors easily occurring during data transmission and affecting the accuracy and integrity of monitoring data.

[0007] In terms of data fusion and processing, the data collected by different monitoring terminals often have differences in time and space. Traditional methods are difficult to effectively perform spatio-temporal alignment and fusion processing on these multi-source heterogeneous data, and cannot provide comprehensive and accurate information on the operating status of transmission lines for operation and maintenance personnel, thus affecting the timely discovery and accurate diagnosis of transmission line faults. Summary of the Invention

[0008] The purpose of the present invention is to provide a transmission method for monitoring data of transmission lines based on a hybrid network of Lora and 5G to solve the problems raised in the above-mentioned background technology.

[0009] To achieve the above purpose, the present invention provides the following technical solution: A transmission method for monitoring data of transmission lines based on a hybrid network of Lora and 5G, the method comprising: Step S1: Use a hybrid network selection algorithm to determine the network access mode of the transmission line monitoring terminal to obtain the target network access mode; and perform dual-module network configuration processing on the transmission line monitoring terminal through the Lora communication module and the 5G communication module to generate a hybrid network communication link; Step S2: Use a data frame compression algorithm to perform frame processing on the original monitoring data collected by the transmission line monitoring terminal to obtain frame monitoring data; and perform compression coding processing on the frame monitoring data based on dynamic network state parameters to generate compressed coding data; Step S3: Use a dynamic routing optimization algorithm to perform path selection processing on the hybrid network communication link to obtain the optimal transmission path; and perform redundancy check processing on the optimal transmission path based on an environmental interference evaluation model to generate post-check transmission data; Step S4: Perform spatio-temporal alignment processing on the post-check transmission data through a multi-modal data fusion model to obtain fusion monitoring data; and use adaptive modulation technology to perform dynamic adjustment processing on the modulation format of the fusion monitoring data to generate optimized modulation data; Step S5: Use an abnormal data filtering algorithm to perform noise interference detection processing on the optimized modulation data to obtain filtered transmission data; and perform transmission queue optimization processing on the filtered transmission data through a dynamic priority scheduling program to generate the final transmission data packet; Step S6: Use a feedback learning mechanism to perform real-time evaluation processing on the transmission performance of the final transmission data packet to obtain a transmission performance evaluation result; and update the parameter configurations of the hybrid network selection algorithm and the dynamic routing optimization algorithm according to the transmission performance evaluation result.

[0010] Preferably, step S1 includes the following steps: Step S11: Use network coverage intensity detection technology to measure the Lora signal intensity and 5G signal intensity in the area where the transmission line monitoring terminal is located to obtain a network coverage intensity data set; Step S12: Dynamically determine network switching based on a preset network switching threshold for the network coverage intensity dataset, and generate a target network access mode; Step S13: Perform dual-mode communication protocol adaptation processing on the transmission line monitoring terminal through the protocol conversion interface of the Lora communication module and the 5G communication module, and establish a hybrid networking communication link.

[0011] Preferably, step S2 includes the following steps: Step S21: According to the type and sampling rate of the transmission line monitoring data, use a variable frame strategy to frame the original monitoring data to obtain initial framed data; Step S22: Based on the dynamic parameters of the transmission bandwidth and network latency, use a compression coding algorithm to perform entropy coding processing on the initial framed data to generate compressed coding data; Among them, the function formula of the compression coding algorithm is as follows:

[0012] In the formula, C is the output value of the compressed coding data, D i is the original size of the i-th frame of data, α is the frame weight coefficient, B(t) is the real-time transmission bandwidth, β is the delay compensation factor, L(t) is the network latency parameter, and R(t) is the data redundancy.

[0013] Preferably, step S3 includes the following steps: Step S31: Use a path cost function to evaluate the link quality of the candidate paths of the hybrid networking communication link, and generate a path evaluation result; Step S32: Based on the path evaluation result and real-time environmental interference parameters, use a dynamic programming algorithm to select the optimal transmission path; Step S33: Perform redundancy check processing on the transmission data on the optimal transmission path through cyclic redundancy check technology to generate post-check transmission data.

[0014] Preferably, step S4 includes the following steps: Step S41: Use timestamp alignment technology to perform time synchronization processing on the post-check transmission data from different monitoring terminals to obtain time-aligned data; Step S42: Based on a spatial interpolation algorithm, perform spatial dimension fusion processing on the time-aligned data to generate spatio-temporal fusion data; Step S43: According to the channel quality index, use an adaptive modulation mapping table to dynamically adjust the modulation format of the spatio-temporal fusion data to generate optimized modulation data.

[0015] Preferably, step S5 includes the following steps: Step S51: Perform frequency-domain decomposition processing on the optimized modulation data using wavelet transform to extract noise interference features; Step S52: Perform abnormal data filtering processing on the noise interference features based on a preset noise threshold to generate filtered transmission data; Step S53: Dynamically allocate the transmission priority of the filtered transmission data through a weighted round-robin scheduling algorithm to generate a final transmission data packet.

[0016] Preferably, step S53 includes the following steps: Step S531: Generate dynamic priority parameters using a priority weight calculation model according to the urgency and data type of the data packet; Step S532: Reorder the transmission queue using a weighted round-robin algorithm based on the dynamic priority parameters and the transmission queue length to generate an optimized transmission queue.

[0017] Preferably, step S6 includes the following steps: Step S61: Quantitatively evaluate the transmission performance of the final transmission data packet through the packet loss rate, transmission delay, and bandwidth utilization rate to generate a set of transmission performance indicators; Step S62: Use the gradient descent algorithm to iteratively optimize the network switching threshold of the hybrid networking selection algorithm and update the network access determination rule; Step S63: Based on the set of transmission performance indicators and historical routing data, adaptively adjust the parameters of the path cost function of the dynamic routing optimization algorithm.

[0018] Preferably, step S62 includes the following steps: Step S621: Calculate the gradient direction of the network switching threshold through the set of transmission performance indicators to generate a gradient update amount; Step S622: Dynamically adjust the gradient update amount using a learning rate decay strategy to generate an optimized network switching threshold.

[0019] Preferably, the function formula of the multimodal data fusion model is as follows:

[0020] In the formula, F is the fused monitoring data, w j is the weight coefficient of the jth data source, S j (t) is the confidence of the jth data source at time t, λ is the time decay factor, Δt j is the data acquisition time difference; S k (t) is the confidence of the kth data source at time t.

[0021] Compared with the prior art, the beneficial effects of the present invention are: Through the hybrid networking selection algorithm, the present invention intelligently determines the network access mode according to the Lora signal strength and 5G signal strength in the area where the power transmission line monitoring terminal is located, and performs dual-module networking configuration. In areas with good signal coverage, 5G network is preferentially used to achieve high-speed data transmission; in remote or weak signal areas, it switches to the Lora network to ensure the continuity of data transmission. This method greatly improves the network adaptability, ensures stable data transmission in complex environments, reduces data interruption and loss, and provides a reliable network foundation for the real-time monitoring of power transmission lines.

[0022] Using the data frame compression algorithm, frame processing is carried out according to the type and sampling rate of the power transmission line monitoring data, and compression coding is combined with the dynamic parameters of the transmission bandwidth and network delay. For image data with a large amount of data, a larger frame size is adopted to reduce the frame overhead, and at the same time, the compression parameters are adjusted according to the real-time network state, effectively reducing the data transmission volume and improving the transmission efficiency. In practical applications, compared with traditional methods, it can greatly shorten the data transmission time, improve the timeliness of monitoring data, and enable operation and maintenance personnel to obtain the latest line operation information faster.

[0023] The dynamic routing optimization algorithm combines the path cost function and real-time environmental interference parameters to select the optimal transmission path, and performs redundancy check on the transmitted data through the cyclic redundancy check technology. When encountering harsh environments such as electromagnetic interference, it can timely avoid the interference area and select a reliable path to transmit data. At the same time, the check mechanism can effectively detect and correct errors in the transmission process, ensuring the accuracy and integrity of the data, and providing reliable data support for the fault diagnosis and analysis of power transmission lines.

[0024] The multi-modal data fusion model performs spatio-temporal alignment and fusion processing on data from different monitoring terminals, considering factors such as the weight, confidence, and time decay of data sources, and generates more accurate and comprehensive fusion monitoring data. This helps operation and maintenance personnel to more comprehensively understand the operation status of power transmission lines and improve the accuracy and timeliness of fault diagnosis. The adaptive modulation technology dynamically adjusts the modulation format according to the channel quality, further ensuring the reliable transmission of data under different channel conditions.

[0025] The feedback learning mechanism updates the parameters of the hybrid networking selection algorithm and the dynamic routing optimization algorithm by evaluating the transmission performance of the finally transmitted data packets in real time and using indicators such as packet loss rate, transmission delay, and bandwidth utilization. As the network environment and the state of the transmission line change, the algorithm can adaptively adjust, continuously optimize the transmission performance, continuously improve the quality and efficiency of data transmission, and ensure that the system is always in the best operating state. Efficient and stable data transmission reduces the costs of repeated monitoring and manual intervention caused by data loss or errors. At the same time, timely and accurate monitoring data helps to detect potential faults and hidden dangers of the transmission line in advance, provides a basis for preventive maintenance, reduces the incidence of line faults, improves the safety and reliability of the power system, ensures the stable supply of electricity, and has significant economic and social benefits. Brief Description of the Drawings

[0026] Figure 1 It is the working principle diagram of the transmission line monitoring data transmission method described in the present invention; Figure 2 It is the flowchart for establishing the hybrid networking communication link; Figure 3 It is the flowchart for data frame compression processing; Figure 4 It is the working flowchart for transmission performance evaluation and algorithm parameter update. Detailed Embodiment

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0028] Please refer to Figures 1-4 , the present invention provides a transmission line monitoring data transmission method based on Lora and 5G hybrid networking, and its overall implementation scheme is as follows: Step S1: Determine the network access mode of the transmission line monitoring terminal by means of the hybrid networking selection algorithm, and at the same time complete the dual-mode networking configuration using the Lora communication module and the 5G communication module to build a hybrid networking communication link and establish a basic network architecture for data transmission.

[0029] Step S2: Use the data frame compression algorithm to perform frame processing on the original monitoring data, and then perform compression encoding according to the dynamic network state parameters to reduce the data transmission volume and improve the transmission efficiency.

[0030] Step S3: Use the dynamic routing optimization algorithm to select the optimal transmission path in the hybrid networking communication link, and perform redundancy check based on the environmental interference evaluation model to ensure the accuracy of data transmission.

[0031] Step S4: Perform spatio-temporal alignment on the verified data through the multi-modal data fusion model, and then use the adaptive modulation technology to dynamically adjust the modulation format to adapt to different channel conditions.

[0032] Step S5: Apply the abnormal data filtering algorithm to detect and filter out noise interference, optimize the transmission queue through the dynamic priority scheduler, generate the final transmission data packet, and ensure the priority transmission of important data.

[0033] Step S6: Use the feedback learning mechanism to evaluate the transmission performance of the final transmission data packet in real time, and update the parameter configurations of the hybrid networking selection algorithm and the dynamic routing optimization algorithm according to the evaluation results to achieve the adaptive optimization of the transmission method.

[0034] The following further illustrates the implementation of the present invention in combination with Embodiments 1 to 5.

[0035] Embodiment 1. This embodiment elaborates in detail the specific implementation manner of Step S1, aiming to accurately determine the network access mode of the power transmission line monitoring terminal and successfully establish a hybrid networking communication link to ensure that data can be transmitted in a suitable network environment. The specific steps include: Step S11: Measure the network coverage intensity: Deploy high-precision signal intensity measurement devices in the area where the power transmission line monitoring terminal is located. These devices have the ability to monitor the Lora signal intensity and 5G signal intensity in real time. Through the built-in signal receiving antenna and signal processing chip, they collect and analyze various parameters of the signal, such as signal power, frequency, phase and other information. Measure once every certain time interval (for example, 5 seconds), and record the measured Lora signal intensity value and 5G signal intensity value to form a data set containing timestamps and corresponding signal intensities, that is, the network coverage intensity data set. For example, at a power transmission line monitoring point in a certain mountainous area, signal intensity data at multiple moments are recorded in this way, providing an accurate basis for subsequent network switching determination.

[0036] Step S12: Dynamic network switching determination: A series of network switching thresholds are preset, and these thresholds are set based on a large amount of experimental data and actual scenario tests. Compare and analyze the network coverage intensity dataset obtained in Step S11 with the preset thresholds. When the Lora signal intensity is greater than the 5G signal intensity and exceeds the preset Lora signal intensity switching threshold, it is determined that the target network access mode is the Lora network; conversely, if the 5G signal intensity is greater than the Lora signal intensity and exceeds the 5G signal intensity switching threshold, it is determined that the target network access mode is the 5G network. In this way, the best network access mode can be intelligently selected according to the real-time change of the signal intensity, ensuring the stability and efficiency of data transmission.

[0037] Step S13: Dual-mode communication protocol adaptation processing: Both the Lora communication module and the 5G communication module are equipped with dedicated protocol conversion interfaces. After determining the target network access mode, the dual-mode communication protocol of the power transmission line monitoring terminal is adapted through this interface. If the access mode is the Lora network, the protocol conversion interface will convert the internal data communication protocol of the monitoring terminal into a format that conforms to the Lora network communication specification, such as the LoRaWAN protocol format, to ensure that the data can be correctly transmitted in the Lora network; if it is the 5G network, it will be converted into a protocol adapted to the 5G network, such as the NR (New Radio) protocol, so as to establish a stable hybrid networking communication link.

[0038] Embodiment 2, this embodiment details Step S2, and its main function is to effectively frame and compress-encode the original monitoring data, reduce the data transmission volume, and improve the transmission efficiency. The specific steps include: Step S21: Framing processing with a variable framing strategy: The types of power transmission line monitoring data are diverse, including temperature, humidity, images, current, voltage, etc. The sampling rates of different types of data are also different. For example, the sampling rate of temperature data may be once per minute, while the sampling rate of image data may be several frames per second according to the monitoring requirements. According to the differences in data types and sampling rates, a variable framing strategy is adopted. For image data with a high sampling rate and a large amount of data, a larger framing size is used. For example, an image is divided into multiple larger frames according to a fixed pixel block size, which can reduce the number of frames and the additional overhead brought by framing processing; for data with a low sampling rate such as temperature and humidity, a smaller framing size is used to more flexibly process the data. In this way, the original monitoring data is framed to obtain the initial framed data.

[0039] Step S22: Entropy coding processing based on dynamic parameters: Transmission bandwidth and network latency are key dynamic parameters that affect data transmission, and they change in real time with the change of the network environment. Use a compression coding algorithm to perform entropy coding processing on the initial framed data. The function formula of this algorithm is:

[0040] Among them, C is the output value of the compressed encoded data, D i is the original size of the i-th frame of data, α is the frame division weight coefficient, B(t) is the real-time transmission bandwidth, β is the delay compensation factor, L(t) is the network delay parameter, and R(t) is the data redundancy. In practical applications, the real-time transmission bandwidth B(t) and the network delay parameter L(t) are obtained in real time through a network monitoring device, and the data redundancy R(t) is calculated according to the characteristics and transmission requirements of the data. The frame division weight coefficient α and the delay compensation factor β are reasonably set according to different application scenarios and data types. For example, when transmitting high-definition image monitoring data, since the data volume is large, in order to compress the data more effectively, the frame division weight coefficient α can be appropriately increased; if the network delay is high, in order to balance the relationship between data compression and transmission delay, the delay compensation factor β can be increased. Through this entropy encoding process based on dynamic parameters, compressed encoded data is generated.

[0041] Embodiment 3. In this embodiment, the specific implementation process of step S3 is described in detail. Its function is to select the optimal transmission path in the hybrid networking communication link and perform redundancy check on the transmitted data to ensure the reliability of data transmission. The specific steps include: Step S31: Link quality assessment: Define a path cost function, which comprehensively considers multiple factors to evaluate the link quality of the candidate paths of the hybrid networking communication link. The path cost function can be expressed as: where ω 1 、ω 2 、ω 3 are weight coefficients, which can be adjusted according to actual application requirements. Bandwidth represents the link bandwidth, Delay represents the link delay, and PacketLossRate represents the link packet loss rate. Use this path cost function to calculate each candidate path of the hybrid networking communication link to obtain the path cost of each path, thereby generating a path evaluation result. For example, at a certain moment, three candidate paths are evaluated. Path A has a larger bandwidth but a higher delay, path B has a smaller bandwidth but a lower delay, and path C has a higher packet loss rate. After calculating through the path cost function, the path costs of the three paths are obtained, and then their advantages and disadvantages are compared.

[0042] Step S32: Select the optimal transmission path: Based on the path evaluation results obtained in Step S31, combined with real-time environmental interference parameters, use the dynamic programming algorithm to select the optimal transmission path. The real-time environmental interference parameters include electromagnetic interference intensity, weather conditions (such as heavy rain, sandstorms, etc. that affect signal transmission), etc. If there is strong electromagnetic interference in a certain area, then the candidate paths passing through this area will be given a lower priority when being selected. The dynamic programming algorithm determines the optimal transmission path by continuously iterating and calculating, comprehensively considering the path cost and real-time environmental interference parameters. For example, when encountering strong electromagnetic interference, a path with a lower original path cost but passing through the interference area may no longer be the optimal choice, and the algorithm will select a path with a slightly higher path cost but less interference as the optimal transmission path.

[0043] Step S33: Redundancy check processing: Use the cyclic redundancy check (CRC) technology to perform redundancy check processing on the transmitted data on the optimal transmission path. At the sending end, according to a specific CRC algorithm, generate a check code for the data to be transmitted. For example, using the CRC - 16 algorithm, group the data according to the specified bit width, and then generate a 16-bit check code through specific polynomial operations, and append this check code to the data for transmission together. At the receiving end, recalculate the check code for the received data and compare it with the received check code. If the two are consistent, it means that the data has not been corrupted during transmission; if they are inconsistent, it indicates that the data has an error, and the receiving end can request the sending end to resend the data. Through this cyclic redundancy check technology, generate the transmitted data after verification, effectively improving the accuracy of data transmission.

[0044] Embodiment 4, this embodiment details Step S4, aiming to perform spatio-temporal alignment and fusion processing on the transmitted data after verification, improve the fusion effect through a multi-modal data fusion model, and dynamically adjust the modulation format according to the channel quality to improve the data transmission quality. The specific steps include: Step S41: Time synchronization processing: Use high-precision time synchronization devices to equip each monitoring terminal with a precise clock source to ensure that the timestamps added during data acquisition are highly accurate. At the receiving end, set up a time synchronization server, and each monitoring terminal regularly calibrates the time with the server. Use the network time protocol (NTP)-based time synchronization technology to transmit time information to each monitoring terminal through the network. At the same time, use the timestamp alignment algorithm to perform time synchronization processing on the transmitted data after verification from different monitoring terminals. The specific algorithm calculates the time deviation by comparing the timestamps of the data from different monitoring terminals. For the data with a deviation within a certain range, use the linear interpolation method for time calibration; for the data with a large deviation, trace back to the data source to re-obtain the data or mark it for subsequent processing, so as to ensure that the data from different monitoring terminals is precisely synchronized in time and obtain time-aligned data.

[0045] Step S42: Spatial dimension fusion processing: Use a multi-modal data fusion model for spatial dimension fusion. The functional formula of this model is:

[0046] where F is the fused monitoring data, w j is the weight coefficient of the j-th data source, S j (t) is the confidence level of the j-th data source at time t, λ is the time decay factor, and Δt j is the data acquisition time difference; S k (t) is the confidence level of the k-th data source at time t.

[0047] In the transmission line monitoring scenario, the data types collected by different monitoring terminals are diverse, such as temperature, humidity, stress, etc. Assign a weight coefficient W j， to each data source. This coefficient is determined based on factors such as the importance, stability of the data source, and its relevance to the operating state of the transmission line. For example, for stress data that directly reflects the safety status of the transmission line, a higher weight coefficient is assigned; for ambient temperature data, an appropriate weight is assigned according to the degree of its impact on the line. The confidence level S j (t) of the data source is comprehensively judged by analyzing the historical data of the data source, evaluating the accuracy of the monitoring equipment, and the real-time change of the data. The time decay factor λ is set according to the change frequency of the operating state of the transmission line. If the operating state of the line changes rapidly, the value of λ is appropriately increased to highlight the importance of recent data; if the change is slow, the value of λ is decreased. Through this multi-modal data fusion model, combined with the spatial position information of different monitoring terminals, the time-aligned data is processed for spatial dimension fusion to generate more accurate and comprehensive spatio-temporal fusion data.

[0048] Step S43: Dynamic adjustment of modulation format: Establish a real-time channel quality monitoring system to continuously obtain channel quality indicators such as channel signal-to-noise ratio and bit error rate. According to these channel quality indicators, use an adaptive modulation mapping table to dynamically adjust the modulation format of the spatio-temporal fusion data. When the channel signal-to-noise ratio is high and the bit error rate is low, a high-order modulation format such as 256QAM (Quadrature Amplitude Modulation) is selected to improve the data transmission rate; when the channel signal-to-noise ratio is low and the bit error rate is high, a low-order modulation format such as BPSK (Binary Phase Shift Keying) is selected to enhance the anti-interference ability of data transmission. At the same time, continuously monitor the change trend of the channel quality. If the channel quality shows a deteriorating trend, adjust the modulation format in advance to ensure the stability of data transmission. In this way, optimized modulation data is generated to ensure the efficient and reliable transmission of data under different channel conditions.

[0049] Example 5. This example elaborates in detail the specific implementation methods of steps S5 and S6. Its function is to detect and filter noise interference in the optimized modulation data, reasonably optimize the transmission queue, and update algorithm parameters according to the transmission performance evaluation results to improve the overall performance of data transmission. The specific steps include: Step S51: Noise interference feature extraction: Use wavelet transform to perform frequency-domain decomposition on the optimized modulation data. Wavelet transform can decompose the signal into different frequency sub-bands. By selecting an appropriate wavelet basis function, such as the Daubechies wavelet, perform multi-resolution analysis on the optimized modulation data. Analyze the energy distribution in the decomposed different frequency sub-bands. Usually, noise signals will exhibit higher energy in certain specific frequency sub-bands, while the energy distribution of useful signals has a certain regularity. Extract the features corresponding to these frequency sub-bands with extremely high energy as noise interference features. For example, in a certain section of power transmission line monitoring data, it is found that the energy in the high-frequency sub-bands is extremely concentrated after wavelet transform. The frequency components corresponding to these high-frequency sub-bands are very likely to be noise interference. Record these features for subsequent abnormal data filtering.

[0050] Step S52: Abnormal data filtering: Preset a noise threshold, which is determined based on a large amount of experimental data and practical application experience. Compare the noise interference features extracted in step S51 with the preset noise threshold. If the noise interference features exceed the noise threshold, determine that the data is abnormal data and filter it out from the optimized modulation data to generate filtered transmission data. In this way, effectively remove the noise interference in the data and improve the accuracy of the data.

[0051] Step S53: Dynamic allocation of transmission priority and optimization of transmission queue Step S531: Generate dynamic priority parameters: According to the urgency and data type of the data packet, use the priority weight calculation model to generate dynamic priority parameters. For data with a high degree of urgency, such as power transmission line fault alarm data, assign a higher priority weight; for general data, such as regular temperature monitoring data, assign a lower priority weight. The priority weight calculation model can be expressed as:

[0052] where ω 1 and ω 2 are weight coefficients, EmergencyLevel represents the urgency of the data packet, and DataTypeWeight represents the weight corresponding to the data type. For example, set the urgency of the fault alarm data to 10 and the urgency of the general temperature monitoring data to 1. The weights of different data types are also set according to their importance. Through this model, calculate the dynamic priority parameters of each data packet.

[0053] Step S532: Reorder the transmission queue: Based on the dynamic priority parameter and the length of the transmission queue, reorder the transmission queue using the weighted round-robin algorithm. The weighted round-robin algorithm assigns different polling weights to each data packet according to the dynamic priority parameter of the data packet. For data packets with higher priorities, their polling weights are larger, and the probability of being preferentially transmitted in the transmission queue is higher. When transmitting data each time, select data packets from the transmission queue for transmission according to the rules of the weighted round-robin algorithm to generate an optimized transmission queue, ensuring that important data can be transmitted preferentially.

[0054] Step S61: Quantitatively evaluate the transmission performance: Quantitatively evaluate the transmission performance of the finally transmitted data packets through the packet loss rate, transmission delay, and bandwidth utilization rate. The packet loss rate is the ratio of the number of lost data packets to the total number of sent data packets. Calculate the packet loss rate by counting the number of sent and received data packets. The transmission delay is the time it takes for a data packet to travel from the sender to the receiver. Calculate the transmission delay by recording the sending time and receiving time of the data packet. The bandwidth utilization rate is the ratio of the actually used bandwidth to the total bandwidth. Calculate the bandwidth utilization rate by monitoring the network traffic and the total bandwidth. Integrate these metrics to generate a transmission performance metric set.

[0055] Step S62: Update the parameters of the hybrid networking selection algorithm: Step S621: Calculate the gradient direction: Calculate the gradient direction of the network switching threshold through the transmission performance metric set to generate a gradient update amount. Specifically, use the relationship between the transmission performance metric set (such as the packet loss rate, transmission delay, etc.) and the network switching threshold, and calculate the gradient direction through mathematical methods such as taking derivatives. For example, when the packet loss rate is relatively high and is mainly caused by unreasonable network switching, calculate that the network switching threshold should be adjusted in a certain direction, and this adjustment direction is the gradient direction. Calculate the gradient update amount according to the gradient direction.

[0056] Step S622: Adjust the network switching threshold: Dynamically adjust the gradient update amount using the learning rate decay strategy to generate an optimized network switching threshold. The learning rate decay strategy means that as the number of algorithm iterations increases, the learning rate gradually decreases. In the initial stage, the learning rate is relatively large, which can accelerate the convergence speed of the algorithm; as the number of iterations increases, the learning rate gradually decreases to avoid the algorithm from oscillating when approaching the optimal solution. Adjust the gradient update amount through the learning rate decay strategy to obtain an optimized network switching threshold, update the network access determination rule, and make the hybrid networking selection algorithm more adaptable to the actual network environment.

[0057] Step S63: Parameter adjustment of the dynamic routing optimization algorithm: Based on the transmission performance index set and historical routing data, adaptively adjust the parameters of the path cost function of the dynamic routing optimization algorithm. Analyze the transmission performance index set and historical routing data to find the relationship between each parameter in the path cost function (such as the weight coefficients corresponding to factors such as link bandwidth and link delay) and the transmission performance. If it is found that although a certain path has a large bandwidth, the overall transmission performance is poor due to excessive delay, then the weight coefficient of the bandwidth in the path cost function can be appropriately reduced and the weight coefficient of the delay can be increased. In this way, adaptively adjust the parameters of the path cost function so that the dynamic routing optimization algorithm can select a better transmission path and improve the data transmission performance.

[0058] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0059] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for transmitting power line monitoring data based on Lora and 5G hybrid networking, characterized in that: The following steps are involved: Step S1: using a hybrid networking selection algorithm to determine the network access mode of the transmission line monitoring terminal to obtain a target network access mode; The dual-mode networking configuration of the power transmission line monitoring terminal is processed through the Lora communication module and the 5G communication module to generate a hybrid networking communication link; Step S2: using a data framing compression algorithm to perform framing processing on the original monitoring data collected by the transmission line monitoring terminal to obtain framed monitoring data; and performing compression encoding processing on the framed monitoring data based on dynamic network state parameters to generate compressed encoded data; Step S3: using a dynamic routing optimization algorithm to perform path selection processing on the hybrid networking communication link to obtain an optimal transmission path; and performing redundancy verification processing on the optimal transmission path based on an environmental interference assessment model to generate verified transmission data; Step S4: performing time-space alignment processing on the verified transmission data through a multimodal data fusion model to obtain fused monitoring data; and using adaptive modulation technology to dynamically adjust the modulation format of the fused monitoring data to generate optimized modulation data; Step S5: using an abnormal data filtering algorithm to perform noise interference detection processing on the optimized modulated data to obtain filtered transmission data; and performing transmission queue optimization processing on the filtered transmission data through a dynamic priority scheduling program to generate a final transmission data packet; Step S6: Use the feedback learning mechanism to perform real-time evaluation processing on the transmission performance of the final transmission data packet to obtain a transmission performance evaluation result; and update the parameter configuration of the hybrid networking selection algorithm and the dynamic routing optimization algorithm according to the transmission performance evaluation result.

2. The method for transmitting power line monitoring data according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: using the network coverage strength detection technology to measure the Lora signal strength and 5G signal strength in the area where the transmission line monitoring terminal is located, to obtain a network coverage strength data set; Step S12: Performing dynamic network switching determination on the network coverage strength data set based on a preset network switching threshold, and generating a target network access mode; Step S13: Perform dual-mode communication protocol adaptation processing on the power transmission line monitoring terminal through the protocol conversion interface of the Lora communication module and the 5G communication module to establish a hybrid networking communication link.

3. The method for transmitting power line monitoring data according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: according to the type and sampling rate of the transmission line monitoring data, the original monitoring data is framed using a variable framing strategy to obtain initial framed data; Step S22: Based on the dynamic parameters of transmission bandwidth and network delay, the initial frame data is entropy encoded using a compression encoding algorithm to generate compressed encoded data; Among them, the function formula of the compression coding algorithm is as follows: ； In the formula, C is the output value of the compressed coded data, D i is the original size of the i-th frame data, α is the frame weight coefficient, B(t) is the real-time transmission bandwidth, β is the delay compensation factor, L(t) is the network delay parameter, and R(t) is the data redundancy.

4. The method for transmitting power line monitoring data according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: using the path cost function to evaluate the link quality of the candidate paths of the hybrid networking communication link, and generating a path evaluation result; Step S32: Based on the path evaluation result and the real-time environmental interference parameter, the optimal transmission path is selected using a dynamic programming algorithm; Step S33: performing redundancy check processing on the transmission data on the optimal transmission path through cyclic redundancy check technology to generate verified transmission data.

5. The method for transmitting power line monitoring data according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: using the time stamp alignment technology to perform time synchronization processing on the verified transmission data from different monitoring terminals to obtain time-aligned data; Step S42: performing spatial dimension fusion processing on the time-aligned data based on a spatial interpolation algorithm to generate spatiotemporal fusion data; Step S43: According to the channel quality index, the modulation format of the spatiotemporal fusion data is dynamically adjusted using an adaptive modulation mapping table to generate optimized modulation data.

6. The method for transmitting power line monitoring data according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: using wavelet transform to perform frequency domain decomposition processing on the optimized modulation data to extract noise interference characteristics; Step S52: performing abnormal data filtering processing on the noise interference feature based on a preset noise threshold to generate filtered transmission data; Step S53: Dynamically assign the transmission priority of the filtered transmission data through a weighted round-robin scheduling algorithm to generate a final transmission data packet.

7. The method for transmitting power line monitoring data according to claim 6, characterized in that: Step S53 includes the following steps: Step S531: Generate dynamic priority parameters using a priority weight calculation model according to the urgency and data type of the data packet; Step S532: Based on the dynamic priority parameter and the transmission queue length, a weighted round-robin algorithm is used to reorder the transmission queue to generate an optimized transmission queue.

8. The method for transmitting power line monitoring data according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: quantitatively evaluating the transmission performance of the final transmission data packet through packet loss rate, transmission delay and bandwidth utilization, and generating a transmission performance indicator set; Step S62: using a gradient descent algorithm to iteratively optimize the network switching threshold of the hybrid networking selection algorithm, and updating the network access determination rule; Step S63: Based on the transmission performance indicator set and historical routing data, adaptively adjust the parameters of the path cost function of the dynamic routing optimization algorithm.

9. The method for transmitting power line monitoring data according to claim 8, characterized in that: Step S62 includes the following steps: Step S621: Calculate the gradient direction of the network switching threshold through the transmission performance indicator set to generate a gradient update amount; Step S622: dynamically adjust the gradient update amount using the learning rate decay strategy to generate an optimized network switching threshold.

10. The method for transmitting data for monitoring a power transmission line according to claim 1, characterized in that: The function formula of the multimodal data fusion model is as follows: ； Where F is the fusion monitoring data, w j is the weight coefficient of the jth data source, S j (t) is the confidence of the jth data source at time t, λ is the time decay factor, Δt j is the data collection time difference; S k (t) is the confidence of the kth data source at time t.

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