Icing monitoring terminal module interconnection communication method, system and device based on data fusion and medium

Abstract

The invention discloses an icing monitoring terminal module interconnection communication method, system and device based on data fusion, and a medium, and belongs to the technical field of power monitoring, and the method comprises the steps: collecting the monitoring data of a plurality of sensors; performing fusion processing on the monitoring data according to a data fusion rule to obtain an icing risk assessment result; determining a communication strategy according to the icing risk assessment result, wherein the communication strategy comprises a communication mode, a data transmission frequency and a transmission data type; executing data transmission according to the communication strategy; monitoring the state of the communication link, and when the communication link is abnormal, switching to a standby communication link according to the icing risk assessment result and the state information of the available communication link; and adjusting the data transmission frequency according to the power state. According to the method, the icing risk level is calculated by fusing the multi-source data of the tension, the inclination angle, the weather and the image and introducing the icing potential function, and the communication strategy is dynamically adjusted based on the risk level, so that the energy consumption waste in the low-risk time period is avoided, and the reliable information transmission in the high-risk time period is ensured.

Description

Technical Field

[0001] This invention relates to the field of power monitoring technology, specifically to a data fusion-based method, system, device, and medium for interconnecting and communicating icing monitoring terminal modules. Background Technology

[0002] The icing monitoring terminal is used to monitor the icing status of power transmission lines in real time. It collects data from various sensors such as tension, tilt angle, meteorology, and images and reports the data to the monitoring center to provide a basis for disaster prevention decisions.

[0003] Existing icing monitoring terminals generally adopt a passive data reporting mode, that is, periodically uploading sensor data according to a preset fixed frequency and communication method. This mode has the following problems: First, during low-risk periods, the terminal still reports data at a relatively high frequency, resulting in wasted energy and bandwidth; second, during high-risk periods, the fixed frequency cannot meet the needs of emergency monitoring, and may miss the critical stage of icing development; third, it usually relies on a single sensor (such as a tension sensor) for risk assessment, without comprehensively considering the impact of meteorological conditions on icing formation, and lacks quantitative modeling of meteorological patterns such as low temperature and high humidity, resulting in insufficient accuracy of risk assessment.

[0004] Furthermore, existing terminals employ fixed communication strategies, failing to adaptively adjust communication methods, transmission frequencies, and data content based on icing risk conditions. When the main communication link fails, the lack of an intelligent link switching mechanism prevents reliable transmission of alarm information during high-risk periods. Regarding energy management, existing solutions typically employ simple function shutdown strategies when power is insufficient, neglecting icing risk conditions and potentially leading to a loss of monitoring capabilities during high-risk periods.

[0005] Therefore, how to assess icing risk based on multi-source sensor data fusion, how to dynamically adjust communication strategies based on risk assessment results, how to achieve intelligent self-healing when communication links fail, and how to ensure monitoring capabilities during critical periods under energy consumption constraints are technical problems that urgently need to be solved in this field. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention provides a method, system, device and medium for interconnection and communication of icing monitoring terminal modules based on data fusion.

[0007] Therefore, the technical problems solved by this invention are: how to accurately assess the risk of icing through multi-source data fusion, how to achieve adaptive adjustment of communication strategies based on risk assessment results, and how to ensure the reliability of the monitoring system under communication link failure and energy consumption constraints.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a data fusion-based method for interconnecting communication between icing monitoring terminal modules, comprising, Collect monitoring data from multiple sensors; The monitoring data is fused according to the data fusion rules to obtain the icing risk assessment results; A communication strategy is determined based on the icing risk assessment results. The communication strategy includes the communication method, data transmission frequency, and data type. Data transmission is performed according to the communication strategy; Monitor the status of the communication link, and when the communication link is abnormal, switch to the backup communication link based on the icing risk assessment results and the status information of the available communication links; The data transmission frequency is adjusted according to the power supply status.

[0009] As a preferred embodiment of the data fusion-based interconnection and communication method for icing monitoring terminal modules described in this invention, the step of fusing the monitoring data according to data fusion rules to obtain icing risk assessment results includes: The monitoring data is normalized to obtain normalized data; The normalized data is weighted and fused according to the weight coefficients corresponding to various monitoring data to obtain the icing risk level. The icing risk assessment result is generated based on the icing risk level.

[0010] As a preferred embodiment of the data fusion-based interconnection communication method for icing monitoring terminal modules described in this invention, the step of determining the communication strategy based on the icing risk assessment results includes: Obtain the first risk threshold and the second risk threshold; The icing risk level is compared with the first risk threshold and the second risk threshold, and the risk level is determined based on the comparison results. The corresponding transmission mode is determined based on the risk level. The communication strategy is generated based on the transmission mode.

[0011] As a preferred embodiment of the data fusion-based interconnection communication method for icing monitoring terminal modules described in this invention, the monitoring communication link status includes: Obtain signal strength parameters and network load parameters of the communication link; The communication link quality index is calculated based on the weighting coefficients corresponding to the signal strength parameters and the network load parameters, respectively. The communication link quality index is compared with a preset quality threshold. When the communication link quality index is lower than the preset quality threshold, the communication link is determined to be abnormal.

[0012] As a preferred embodiment of the data fusion-based interconnection and communication method for icing monitoring terminal modules described in this invention, the monitoring data includes tensile data, tilt angle data, meteorological data, and image data. The step of weighting and fusing the normalized data according to the weighting coefficients corresponding to various monitoring data to obtain the icing risk level includes: Obtain the first weighting coefficient corresponding to the tensile force data, the second weighting coefficient corresponding to the tilt angle data, the third weighting coefficient corresponding to the meteorological data, and the fourth weighting coefficient corresponding to the image data; Calculate the icing potential function value based on the temperature and humidity parameters in the meteorological data; The normalized tensile force data is multiplied by the first weighting coefficient to obtain the first weighted value; the normalized dip angle data is multiplied by the second weighting coefficient to obtain the second weighted value; the normalized meteorological data, the third weighting coefficient, and the icing potential function value are multiplied to obtain the third weighted value; and the normalized image data is multiplied by the fourth weighting coefficient to obtain the fourth weighted value. The first weighted value, the second weighted value, the third weighted value, and the fourth weighted value are weighted and fused to obtain the icing risk level.

[0013] The beneficial effects of this preferred technical solution are as follows: By calculating the icing potential function value based on temperature and humidity parameters in meteorological data, and multiplying this function value by the normalized meteorological data and the third weighting coefficient to obtain the third weighted value, the impact of low temperature and high humidity meteorological conditions on icing formation is quantified. When the temperature is close to the most icing temperature and the humidity is high, the icing potential function value increases, the contribution of meteorological data to risk assessment increases, and the icing risk level can reflect meteorological conditions conducive to icing formation.

[0014] As a preferred embodiment of the data fusion-based icing monitoring terminal module interconnection and communication method described in this invention, wherein adjusting the data transmission frequency according to the power supply status includes: Get the current remaining battery level; Compare the current remaining battery power with a preset battery power threshold; When the current remaining battery power is greater than the preset battery power threshold, data transmission is performed according to the initial transmission frequency corresponding to the communication strategy. When the current remaining power is less than or equal to the preset power threshold, the energy consumption adjustment coefficient is obtained; Multiply the initial transmission frequency by the energy consumption adjustment coefficient to obtain the adjusted transmission frequency; Data transmission is performed according to the adjusted transmission frequency.

[0015] The beneficial effects of this preferred technical solution are as follows: when the remaining power is less than or equal to a preset power threshold, the initial transmission frequency is multiplied by the energy consumption adjustment coefficient to obtain the adjusted transmission frequency. Compared to the method of directly shutting down the function when the power is insufficient, reducing the transmission frequency by adjusting the coefficient instead of interrupting monitoring maintains the continuity of the monitoring function while sacrificing some real-time performance, and avoids losing monitoring capability due to power depletion during high-risk periods.

[0016] As a preferred embodiment of the data fusion-based interconnection communication method for icing monitoring terminal modules described in this invention, the step of switching to a backup communication link when a communication link is abnormal, based on the icing risk assessment result and the status information of available communication links, includes: When a communication link is determined to be abnormal, obtain the communication link quality index of each available backup communication link; The backup communication link selection strategy is determined based on the aforementioned icing risk level. When the icing risk level is less than the second risk threshold, the backup communication link with the highest communication link quality index is selected from all available backup communication links as the target backup communication link. When the icing risk level is greater than or equal to the second risk threshold, a backup communication link with alarm transmission capability is selected from each available backup communication link as the target backup communication link. Switch the data transmission task to the target backup communication link.

[0017] This invention provides an interconnected communication system for icing monitoring terminal modules based on data fusion.

[0018] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an interconnected communication system for icing monitoring terminal modules based on data fusion, comprising: multiple sensor acquisition modules for acquiring monitoring data; The data fusion and collaborative management module is used to fuse the monitoring data according to the data fusion rules to obtain the icing risk assessment result, determine the communication strategy according to the icing risk assessment result, and monitor the status of the communication link. The communication execution module includes multiple communication links and is used to execute data transmission according to the communication strategy. The power management module is used to provide power status information; The data fusion and collaborative management module adjusts the data transmission frequency according to the power status, and controls the communication execution module to switch to the backup communication link when the communication link is abnormal, based on the icing risk assessment results and the status information of the available communication links.

[0019] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the data fusion-based icing monitoring terminal module interconnection and communication method.

[0020] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the aforementioned data fusion-based icing monitoring terminal module interconnection and communication method.

[0021] The beneficial effects of this invention are as follows: By normalizing and weighting tensile data, tilt angle data, meteorological data, and image data, and then performing fusion calculations, an icing potential function value is introduced into the weighting calculation of meteorological data. This function value is determined based on temperature and humidity parameters, quantifying the influence of low temperature and high humidity meteorological conditions on icing formation. Compared to methods relying on a single sensor, fusing multi-source data and combining it with meteorological patterns to calculate the icing risk level reduces misjudgments caused by fluctuations in a single data source or the neglect of meteorological factors.

[0022] The risk level is determined by comparing the icing risk level with a risk threshold, and the corresponding transmission frequency and data type are determined according to different risk levels. Low-risk scenarios use low-frequency transmission of simplified data, medium-risk scenarios use medium-frequency transmission of fused data, and high-risk scenarios use high-frequency transmission of complete data and alarm data. This mechanism of dynamically adjusting communication behavior based on risk assessment results avoids energy waste caused by frequent communication during low-risk periods, while ensuring timely reporting of alarm information during high-risk periods.

[0023] By calculating the communication link quality index to monitor link status, a backup link selection strategy is determined based on the icing risk level when a link is abnormal. In low-risk situations, the backup link with the highest quality index is selected; in high-risk situations, a backup link with alarm transmission capabilities is selected. This intelligent switching mechanism, which considers risk status, can still select a suitable backup link based on the risk level when the primary link fails, ensuring reliable transmission of alarm information during high-risk periods and preventing the loss of critical data due to link failure. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating the overall process of an ice accretion monitoring terminal module interconnection and communication method based on data fusion, as provided in one embodiment of the present invention. Detailed Implementation

[0026] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0027] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a data fusion-based method for interconnecting communication between icing monitoring terminal modules, including: Step 1: Collect monitoring data from multiple sensors; Step 2: Perform data fusion processing on the monitoring data according to the data fusion rules to obtain the icing risk assessment results; Step 3: Determine the communication strategy based on the icing risk assessment results. The communication strategy includes the communication method, data transmission frequency, and data type. Step 4: Perform data transmission according to the communication strategy; Step 5: Monitor the status of the communication link. When the communication link is abnormal, switch to the backup communication link based on the icing risk assessment results and the status information of the available communication links. Step 6: Adjust the data transmission frequency according to the power supply status.

[0028] Icing monitoring terminals need to collect data from various sensors, including tension, tilt angle, meteorological data, and images, to monitor the icing status of transmission lines. Traditional solutions use a fixed data reporting mode, uploading data periodically at a preset frequency and communication method regardless of the icing risk level. This mode has the following problems: First, data is reported at a relatively high frequency during low-risk periods, such as sunny and dry weather, wasting communication resources and energy. Second, during high-risk periods, such as when icing forms rapidly under low temperature and high humidity conditions, the fixed frequency cannot reflect changes in the status in a timely manner, potentially missing critical stages of icing development. Third, it typically relies on a single sensor, such as triggering an alarm when tension exceeds a threshold, without comprehensively considering tilt angle changes, meteorological conditions, and visual information, leading to misjudgments or missed detections. Fourth, when the main communication link fails, there is a lack of a mechanism to intelligently select a backup link based on the risk status, which may result in unreliable transmission of alarm information during high-risk periods. Fifth, when power is insufficient, some functions are directly shut down without considering the current icing risk, potentially resulting in a loss of monitoring capability during high-risk periods.

[0029] This embodiment collects monitoring data from multiple sensors in step 1, and then fuses the monitoring data according to data fusion rules in step 2 to obtain the icing risk assessment result, thus solving the problem of inaccurate risk assessment caused by relying on a single sensor. Step 3 determines the communication strategy based on the icing risk assessment result, including the communication method, data transmission frequency, and data type. Step 4 executes data transmission according to the communication strategy, thus solving the problems of energy waste and untimely response caused by fixed communication modes. Step 5 monitors the communication link status, and when a link is abnormal, switches to a backup link based on the icing risk assessment result and available link status information, thus solving the problem of unreliable alarm information transmission when the main link fails. Step 6 adjusts the data transmission frequency according to the power status, thus solving the problem that simply shutting down the function when the power is insufficient may lead to the loss of monitoring capability during high-risk periods. Through the synergy of the above steps, dynamic adjustment of the communication strategy driven by risk assessment based on multi-source data fusion is realized.

[0030] Example 2, an embodiment of the present invention, provides a data fusion-based interconnection communication method for icing monitoring terminal modules, based on the previous embodiment, including: In step 1, monitoring data from multiple sensors is collected, including tensile data, tilt angle data, meteorological data, and image data. Step 2: The monitoring data is fused according to the data fusion rules to obtain the icing risk assessment result, including the following steps A1-A3: A1: Normalize the monitoring data to obtain normalized data; A2: The normalized data is weighted and fused according to the weight coefficients corresponding to various monitoring data to obtain the icing risk level; A3: Generate the icing risk assessment result based on the icing risk level.

[0031] Furthermore, the step of weighting and fusing the normalized data according to the weighting coefficients corresponding to various monitoring data to obtain the icing risk level includes: Obtain the first weighting coefficient corresponding to the tensile force data, the second weighting coefficient corresponding to the tilt angle data, the third weighting coefficient corresponding to the meteorological data, and the fourth weighting coefficient corresponding to the image data; Calculate the icing potential function value based on the temperature and humidity parameters in the meteorological data; The normalized tensile force data is multiplied by the first weighting coefficient to obtain the first weighted value; the normalized dip angle data is multiplied by the second weighting coefficient to obtain the second weighted value; the normalized meteorological data, the third weighting coefficient, and the icing potential function value are multiplied to obtain the third weighted value; and the normalized image data is multiplied by the fourth weighting coefficient to obtain the fourth weighted value. The first weighted value, the second weighted value, the third weighted value, and the fourth weighted value are weighted and fused to obtain the icing risk level.

[0032] Furthermore, the determination of the first, second, third, and fourth weighting coefficients is achieved through: collecting a complete set of sensor data from multiple historical icing events, including tension, tilt angle, meteorological, and image data; having experts label the icing risk at each moment based on the actual icing thickness, changes in line tension, and on-site images; setting initial values ​​for the weighting coefficients: the first weighting coefficient α is initially set to 0.4, the second weighting coefficient β to 0.3, the third weighting coefficient γ to 0.2, and the fourth weighting coefficient δ to 0.1; using gradient descent or grid search methods, with the mean squared error between the risk prediction results and the expert labeling results as the loss function, iteratively optimizing each weighting coefficient on the training set; testing the model performance on an independent validation set, and fine-tuning the weighting coefficients based on the actual false positive and false negative rates; and making small online corrections to the weighting coefficients based on newly occurring icing event data during system operation to adapt to changes in climate and line conditions.

[0033] In this embodiment of the application, in step 2, the fusion process is performed through: The tensile force data, tilt angle data, meteorological data, and image data were normalized separately. For the tension data F, calculate the normalized value N(F). Divide the difference between the tension data F and the minimum tension value Fmin by the difference between the alarm tension value Falarm and the minimum tension value Fmin, so that the normalized value is mapped to the interval between 0 and 1. For the tilt angle data, obtain the normalized values ​​N(θx) and N(θy) of the X-axis tilt angle θx and the Y-axis tilt angle θy respectively, and calculate the average of the two as the tilt angle normalization result; For meteorological data, wind speed W, temperature parameter T and humidity parameter H are obtained, the normalized value of wind speed N(W) is calculated, the icing potential function value H(T,H) is calculated based on the temperature parameter T and humidity parameter H, and the normalized value of wind speed N(W) and the icing potential function value H(T,H) are multiplied together to obtain the processed meteorological data. The ice accretion potential function value H(T,H) is calculated using the following formula: Where Topt is the temperature at which icing is most likely to occur, and its value ranges from [value missing]. to H is the shape parameter, and H is the humidity parameter; For image data I, the ice thickness is estimated using an image recognition algorithm, and the normalized value of the ice thickness is calculated. ; The first weighting coefficient α corresponding to the tensile force data, the second weighting coefficient β corresponding to the tilt angle data, the third weighting coefficient γ corresponding to the meteorological data, and the fourth weighting coefficient δ corresponding to the image data are obtained. The typical value of the first weighting coefficient α is 0.4, the typical value of the second weighting coefficient β is 0.3, the typical value of the third weighting coefficient γ is 0.2, and the typical value of the fourth weighting coefficient δ is 0.1. Each weighting coefficient satisfies the constraint condition α+β+γ+δ=1. Calculate the icing risk level The formula is as follows: The obtained icing risk level R ranges from 0 to 1, with a higher value indicating a higher icing risk.

[0034] It should be noted that the icing potential function H(T,H) characterizes the nonlinear variation of the probability of icing formation under low temperature and high humidity meteorological conditions. The exponential term exp(-(T-Topt)² / k) in the function describes the influence of temperature on icing. This term reaches its maximum value when the temperature T is close to the most icing temperature Topt, and its value decreases when the temperature deviates from Topt. The linear term H / 100 in the function describes the influence of humidity on icing. The higher the humidity H, the larger the value of this term. The icing potential function value H(T,H) is the product of the two terms. The function value is the largest when both low temperature and high humidity conditions are met, reflecting the meteorological law that icing formation requires the synergistic effect of temperature and humidity. This function makes the contribution of meteorological data in risk assessment not only depend on direct parameters such as wind speed, but also be modulated by the temperature and humidity coupling effect.

[0035] In an optional implementation, in step 2, the fusion process can be performed by: linearly normalizing the various types of monitoring data to map each type of monitoring data to the interval between 0 and 1; setting a fixed weight coefficient for each type of monitoring data; multiplying the normalized value of each type of monitoring data by the corresponding fixed weight coefficient; and summing all weighted results to obtain the icing risk level.

[0036] In another optional implementation, step 2 can also involve: collecting sensor data and actual icing thickness from historical icing events as training data; using gradient descent or grid search methods, with the error between the predicted icing risk and the actual icing thickness as the loss function, iteratively optimizing the weight coefficients corresponding to various monitoring data; using the optimized weight coefficients for weighted fusion calculation of normalized data; and during system operation, adjusting the weight coefficients online based on newly occurring icing event data to achieve dynamic adjustment of the weight coefficients.

[0037] Step 3: Determine the communication strategy based on the icing risk assessment results. The communication strategy includes the communication method, data transmission frequency, and data transmission type, including the following steps B1-B3: B1: Obtain the first risk threshold and the second risk threshold; B2: Compare the icing risk level with the first risk threshold and the second risk threshold respectively, and determine the risk level based on the comparison results; B3: Determine the corresponding transmission mode based on the aforementioned risk level; B4: Generate the communication strategy based on the transmission mode.

[0038] In this embodiment, step 3 involves the following steps: setting a first risk threshold Rlow and a second risk threshold Rhigh, with a typical value of 0.3 for the first risk threshold Rlow and a typical value of 0.7 for the second risk threshold Rhigh; comparing the icing risk level R with the first risk threshold Rlow and the second risk threshold Rhigh; determining a low-risk state when the icing risk level R is less than the first risk threshold Rlow; determining a medium-risk state when the icing risk level R is greater than or equal to the first risk threshold Rlow and less than the second risk threshold Rhigh; determining a high-risk state when the icing risk level R is greater than or equal to the second risk threshold Rhigh; determining a transmission mode based on the risk level, with the low-risk state corresponding to the first transmission mode, the medium-risk state corresponding to the second transmission mode, and the high-risk state corresponding to the third transmission mode; and generating a communication strategy based on the transmission mode, which includes the communication method, data transmission frequency, and data type.

[0039] In an optional implementation, in step 3, the communication strategy can be: setting a single risk threshold R0; comparing the icing risk level R with the risk threshold R0; when the icing risk level R is less than the risk threshold R0, it is determined to be a low-risk state, and simplified data is transmitted using a low-power communication method and a low transmission frequency; when the icing risk level R is greater than or equal to the risk threshold R0, it is determined to be a high-risk state, and complete data and alarm information are transmitted using a highly reliable communication method and a high transmission frequency.

[0040] In another optional implementation, in step 3, the communication strategy can also be achieved by: setting multiple risk thresholds to divide the icing risk level into four or more risk levels; each risk level corresponds to a different transmission frequency and data detail level; gradually increasing the transmission frequency and data integrity as the risk level increases; and generating a communication strategy based on the transmission parameters corresponding to each risk level.

[0041] In this embodiment of the application, in step B3, the transmission mode is set by: selecting the LoRa module as the communication method, setting the data transmission frequency to transmit once every 30 minutes, and transmitting data types including icing risk level R, temperature parameter T, and core status information. This mode is used in low-risk states, with the goal of saving energy and extending terminal operating time. The second transmission mode is set by selecting a 4G or 5G module for communication, setting the data transmission frequency to once every 10 minutes, and transmitting data types including icing risk level R, tensile data F, X-axis tilt angle θx, Y-axis tilt angle θy, wind speed W, temperature parameter T, humidity parameter H, and risk trend information. This mode is used for medium-risk conditions, with the goal of balancing energy consumption and monitoring needs to ensure continuous situational awareness. The third transmission mode is set by: selecting the 4G or 5G module to work in parallel with the Beidou short message module, setting the data transmission frequency to once per minute, transmitting a complete data packet including raw sensor data and high-definition images through the 4G or 5G channel, and synchronously sending alarm frames containing tower number, icing risk level R, tensile data F, and geographical location through the Beidou channel. This mode is used in high-risk situations, with the goal of ensuring that core alarm information is absolutely reliable and panoramic data is not lost.

[0042] In an optional implementation, in step B3, the transmission mode can be: all three transmission modes use the same communication method; The transmission frequency for the first transmission mode is set to f1; The transmission frequency of the second transmission mode is set to f2, where f2 is greater than f1; The transmission frequency of the third transmission mode is set to f3, where f3 is greater than f2; The data types transmitted in all three transmission modes remain consistent.

[0043] In another alternative implementation, in step B3, the transmission mode can also be configured such that all three transmission modes use the same transmission frequency. The data type transmitted in the first transmission mode is a summary of core parameters; The second transmission mode transmits data from the main sensors. The third transmission mode transmits complete data, including raw data and image information; The communication methods for the three transmission modes remain consistent.

[0044] Furthermore, when the icing risk level is less than the first risk threshold, the communication strategy is determined to be the first transmission mode, the transmission frequency of the first transmission mode is the first frequency, and the data type of the transmission is simplified data. When the icing risk level is greater than or equal to the first risk threshold and less than the second risk threshold, the communication strategy is determined to be the second transmission mode, the transmission frequency of the second transmission mode is the second frequency, and the data type of transmission is fused data. When the icing risk level is greater than or equal to the second risk threshold, the communication strategy is determined to be the third transmission mode. The transmission frequency of the third transmission mode is the third frequency, and the data types transmitted include complete data and alarm data.

[0045] Step 5: Monitor the communication link status. When the communication link is abnormal, switch to the backup communication link based on the icing risk assessment results and the status information of available communication links, including the following steps C1-C4: C1: Obtain signal strength parameters and network load parameters of the communication link; C2: Calculate the communication link quality index based on the weighting coefficients corresponding to the signal strength parameters and the network load parameters, respectively; C3: Compare the communication link quality index with a preset quality threshold; C4: When the communication link quality index is lower than the preset quality threshold, the communication link is determined to be abnormal.

[0046] In this embodiment of the application, step C2 involves calculating the communication link quality index via: Obtain the signal strength parameter Si of communication link i, and normalize the signal strength parameter Si to the interval of 0 to 1. Obtain the network load parameters or bit error rate parameters of communication link i, calculate its reverse index Bi, and normalize the reverse index Bi to the range of 0 to 1. Obtain the weighting coefficients corresponding to the signal strength parameters. Weighting coefficients corresponding to network load parameters ; The weighting coefficients are determined based on the deployment environment of the communication link. For scenarios with poor signal coverage in mountainous or remote areas, the weighting coefficients are adjusted accordingly. Set the weighting factor to 0.6 to 0.7 for high-load or high-interference environments. Set to 0.3 to 0.4; The weighting coefficients satisfy the normalization constraint. ; The communication link quality index Qi is calculated using the following formula: Different combinations of weighting coefficients can be set for different communication modules, such as 4G modules, Beidou modules, and LoRa modules; The calculated communication link quality index Qi is compared with the preset quality interval value Qmin. When Qi is less than Qmin, the communication link is determined to be faulty.

[0047] Furthermore, the dynamic adjustment of the weighting coefficients ωs and ωb is achieved by: recording historical communication success rate data for each communication link; increasing the value of weighting coefficient ωb and correspondingly decreasing the value of weighting coefficient ωs when a communication link has a high signal strength but experiences frequent packet loss or transmission failures; increasing the value of weighting coefficient ωs and correspondingly decreasing the value of weighting coefficient ωb when a communication link has a low network load but experiences communication failures due to weak signals; controlling the adjustment range of the weighting coefficients within ±10% of the original value, and ensuring that the adjusted weighting coefficients still satisfy the normalization constraint of ωs + ωb = 1; after multiple adjustments, the weighting coefficients can more accurately reflect the quality influencing factors of the communication link in the actual operating environment.

[0048] In an optional implementation, in step C2, the communication link quality index can be calculated by: obtaining the signal strength parameters of the communication link; The signal strength parameter is normalized to the range of 0 to 1 and used as the communication link quality index. Compare the communication link quality index with a preset threshold; When the signal strength parameter is lower than a preset threshold, the communication link is determined to be abnormal.

[0049] In another optional implementation, in step C2, the communication link quality index can also be calculated by: obtaining the signal strength parameter, delay parameter, packet loss rate parameter, and bit error rate parameter of the communication link; converting the delay parameter, packet loss rate parameter, and bit error rate parameter into inverse indices respectively; setting weight coefficients ω1, ω2, ω3, and ω4 for the signal strength parameter, delay inverse index, packet loss rate inverse index, and bit error rate inverse index respectively; each weight coefficient is set according to the application scenario, satisfying the normalization constraint ω1+ω2+ω3+ω4=1; and calculating the communication link quality index as the weighted sum of the products of each parameter and its corresponding weight coefficient.

[0050] Furthermore, if a communication link is determined to be abnormal, the remaining battery power is obtained; Compare the current remaining battery power with a preset battery power threshold; When the current remaining battery power is greater than the preset battery power threshold, data transmission is performed according to the initial transmission frequency corresponding to the communication strategy. When the current remaining power is less than or equal to the preset power threshold, the energy consumption adjustment coefficient is obtained; Multiply the initial transmission frequency by the energy consumption adjustment coefficient to obtain the adjusted transmission frequency; Data transmission is performed according to the adjusted transmission frequency.

[0051] Step 6: Adjusting the data transmission frequency according to the power supply status includes the following steps D1-D6: D1: Get the current remaining battery level; D2: Compare the current remaining battery power with a preset battery power threshold; D3: When the current remaining battery power is greater than the preset battery power threshold, data transmission is performed according to the initial transmission frequency corresponding to the communication strategy; D4: When the current remaining power is less than or equal to the preset power threshold, obtain the energy consumption adjustment coefficient; D5: Multiply the initial transmission frequency by the energy consumption adjustment coefficient to obtain the adjusted transmission frequency; D6: Perform data transmission according to the adjusted transmission frequency.

[0052] Furthermore, the energy consumption adjustment coefficient is obtained by: setting a safe power threshold Esafe; when the current remaining power Eremain is greater than the safe power threshold Esafe, the energy consumption adjustment coefficient λ is set to 1, indicating that the original transmission frequency is followed; when the current remaining power Eremain is less than or equal to the safe power threshold Esafe, the energy consumption adjustment coefficient λ is set to 0.5, indicating that all transmission frequencies are reduced to half of the original frequency; by reducing the transmission frequency, some real-time performance is sacrificed in exchange for a longer maintenance time of critical functions, avoiding the loss of monitoring capabilities due to power depletion during high-risk periods.

[0053] Furthermore, the switch to the backup communication link is achieved through: When a communication link is determined to be abnormal, obtain the communication link quality index of each available backup communication link; The backup communication link selection strategy is determined based on the aforementioned icing risk level. When the icing risk level is less than the second risk threshold, the backup communication link with the highest communication link quality index is selected from all available backup communication links as the target backup communication link. When the icing risk level is greater than or equal to the second risk threshold, a backup communication link with alarm transmission capability is selected from each available backup communication link as the target backup communication link. If a Beidou short message module exists and is available, the Beidou short message module is selected first to ensure the absolutely reliable transmission of alarm information. The data transmission task is switched to the target backup communication link, and the backup routing table is maintained to record the switching history.

[0054] Furthermore, the normalization process works by normalizing all sensor data before it is input into the fusion model, converting it into dimensionless risk factors. The normalization function N(·) maps all physical quantities to the 0-1 interval, eliminating dimensional interference. The tensile force data F has the dimension of kilonewtons (kN), the tilt angle data θ has the dimension of degrees, the meteorological data wind speed W has the dimension of meters per second (m / s), the temperature parameter T has the dimension of degrees Celsius (°C), the humidity parameter H has the dimension of percentage (%), and the image data I has the dimension of millimeters (mm). All input quantities are converted to dimensionless values ​​in the 0-1 interval through the normalization function before being substituted into the fusion calculation formula. Therefore, all terms in the fusion calculation formula are dimensionless, allowing for weighted summation without the risk of dimensional confusion.

[0055] Before obtaining the first weight coefficient, second weight coefficient, third weight coefficient, and fourth weight coefficient in step A2, add the following content: Furthermore, the first weighting coefficient α, the second weighting coefficient β, the third weighting coefficient γ, and the fourth weighting coefficient δ are not merely typical values, but are obtained through training with historical data or optimization using an expert system. The steps for determining the weighting coefficients are as follows: The first step is to collect historical monitoring data and corresponding icing incident records. This involves collecting complete sensor data from multiple historical icing events, including tension data, tilt data, meteorological data, and image data. Corresponding icing incident records or manually confirmed icing status labels are then obtained, using actual icing thickness, line tension changes, and on-site images as the labeling criteria. Experts then label the icing risk at each moment based on the actual icing thickness, line tension changes, and on-site images, forming a training dataset.

[0056] The second step is to train and optimize the weight coefficients. Initial values ​​for the weight coefficients are set: the first weight coefficient α is initially set to 0.4, the second weight coefficient β to 0.3, the third weight coefficient γ to 0.2, and the fourth weight coefficient δ to 0.1. Gradient descent is used to adjust the weight coefficients, with the mean squared error between the predicted icing risk value and the expert-annotated value used as the loss function. The weight coefficients are iteratively updated on the training set, calculating the partial derivative of the loss function with respect to each weight coefficient, updating the weight coefficients in the negative gradient direction, with a learning rate of 0.01. The iterations are stopped after 1000 iterations or when the change in the loss function is less than 0.001. When using the grid search method, the first weight coefficient α is set to range from 0.3 to 0.5 with a step size of 0.05; the second weight coefficient β is set to range from 0.2 to 0.4 with a step size of 0.05; the third weight coefficient γ is set to range from 0.1 to 0.3 with a step size of 0.05; and the fourth weight coefficient δ is set to range from 0.05 to 0.15 with a step size of 0.05. Under the constraint that α + β + γ + δ equals 1, all combinations are iterated, and the weight coefficient combination with the smallest mean squared error in the training set is selected. The results are tested on an independent validation set. The prediction accuracy is calculated as the number of correctly predicted samples divided by the total number of samples; the recall is calculated as the number of correctly predicted high-risk samples divided by the total number of actual high-risk samples; and the F1 score is calculated as 2 times the accuracy multiplied by the recall divided by the sum of the accuracy and recall.

[0057] In an optional implementation, the weighting coefficients can be trained by: collecting M historical icing event samples, each sample containing normalized tensile force data N(F), tilt angle data N(θ), meteorological data N(W), icing potential function value H(T,H), image data N(I), and expert-annotated actual icing risk level Rtrue. The samples are divided into a training set (80%) and a validation set (20%). For each sample i in the training set, the predicted icing risk level Rpred(i) is calculated based on the current weighting coefficients α, β, γ, and δ, equal to α multiplied by N(F) plus β multiplied by N(θ) plus γ multiplied by N(W) plus H(T,H) plus δ multiplied by N(I). The mean squared error (MSE) of the training set is calculated as the sum of squares of Rpred(i) minus Rtrue(i) of the M samples divided by M. The partial derivative of the mean squared error (MSE) with respect to the first weight coefficient α is calculated as 2 divided by the sum of Rpred(i) minus Rtrue(i) multiplied by N(F) for M samples. The partial derivatives with respect to the second, third, and fourth weight coefficients β, γ, and δ are calculated similarly. The weight coefficients are updated using the gradient descent formula; the new value of α equals the old value of α minus the learning rate multiplied by the partial derivative. The other weight coefficients are updated in the same way. After each update, the weight coefficients are normalized so that α + β + γ + δ equals 1. This process is repeated until the MSE converges or the maximum number of iterations is reached. The prediction accuracy and recall of the final model are calculated on the validation set. If the performance does not meet the requirements, the learning rate is adjusted or the training samples are increased and the model is retrained.

[0058] The third step involves fine-tuning the weighting coefficients based on expert experience. The weighting coefficients are adjusted according to physical understanding to ensure they conform to physical laws. The tension term directly reflects changes in conductor stress and is the clearest mechanical evidence of icing; therefore, the first weighting coefficient α is usually the maximum among all weighting coefficients. The tilt term reflects unbalanced conductor stress and can detect localized icing or wind deflection issues; the second weighting coefficient β is next in importance. The meteorological term converts meteorological conditions into icing probability values ​​by introducing the icing potential function value H(T,H); the third weighting coefficient γ reflects the impact of meteorological conditions. The image term serves as an auxiliary verification method for visual confirmation; the fourth weighting coefficient δ is relatively small. The weighting coefficients are fine-tuned based on actual false alarm and false negative rates. During system operation, small online corrections are made to the weighting coefficients based on newly occurring icing event data to adapt to changes in climate and line conditions.

[0059] Furthermore, the physical meaning of the weighted fusion calculation is as follows: each term in the fusion calculation formula is a risk contribution factor, and the weighted summation can reflect all risks as a whole. The first weighted value α·N(F) reflects the contribution of tension changes to icing risk; an increase in tension directly indicates an increase in conductor stress, which is a mechanical characteristic of icing formation. The second weighted value β·(N(θx)+N(θy)) / 2 reflects the contribution of tilt angle changes to icing risk; an increase in tilt angle indicates unbalanced conductor stress, reflecting local icing or wind deflection. The third weighted value γ·N(W)·H(T,H) reflects the contribution of meteorological conditions to icing risk, where the normalized wind speed value N(W) of the meteorological data is multiplied by the icing potential function value H(T,H), illustrating the physical mechanism by which wind speed amplifies meteorological risk. When the temperature parameter T is close to the icing-prone temperature Topt and the humidity parameter H is high, the icing potential function value H(T,H) increases. At this time, a higher wind speed W results in faster flow of cold, moist air, leading to faster icing formation. Therefore, the product of the normalized wind speed value N(W) and the icing potential function value H(T,H) accurately reflects the synergistic effect of meteorological conditions on icing formation. The fourth weighted value δ·N(I) reflects the contribution of image recognition results to icing risk, providing auxiliary verification information through visual confirmation. The weighted summation of the four weighted values ​​achieves multi-dimensional fusion of mechanical, geometric, meteorological, and visual indicators, comprehensively assessing the icing risk level.

[0060] It should be noted that normalization is a prerequisite for fusion calculation. Different types of sensor data have different dimensions and numerical ranges. Typical ranges for tensile force data F are 0 to 50 kN, tilt angle data θ are 0 to 20 degrees, wind speed W is 0 to 20 m / s, temperature parameter T is -20℃ to 10℃, humidity parameter H is 0 to 100%, and image data I is 0 to 30 mm. If weighted summation is performed directly without normalization, parameters with larger numerical ranges will dominate the calculation results, while the contributions of parameters with smaller numerical ranges will be submerged, causing the fusion calculation to lose its physical meaning. After the normalization function N(·) maps each parameter to a unified 0-1 interval, the contribution of each parameter in the fusion calculation is entirely controlled by the weighting coefficient. The weighting coefficient reflects the importance of the parameter in the icing risk assessment, avoiding calculation bias caused by differences in dimensions.

[0061] Furthermore, the physical constraint of the weighting coefficients is that each weighting coefficient satisfies the normalization constraint α+β+γ+δ=1. This constraint ensures that the icing risk level R ranges from 0 to 1, facilitating the setting of risk thresholds and the classification of risk levels. When all normalized data reaches the maximum value of 1, the icing risk level R reaching the maximum value of 1 indicates an extremely high risk state. When all normalized data reaches the minimum value of 0, the icing risk level R reaching the minimum value of 0 indicates a risk-free state. The weighting coefficients all range from 0 to 1, and each weighting coefficient is greater than 0, ensuring that each risk contribution factor plays a role in the fusion calculation.

[0062] Example 3 is an embodiment of the present invention, which provides an interconnection and communication method for icing monitoring terminal modules based on data fusion. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0063] This embodiment describes an ice accretion monitoring terminal based on this invention installed on tower #89 of a 220kV transmission line in a mountainous area. The terminal integrates a sensor acquisition module, including a tension sensor with a range of 0-50kN, a dual-axis tilt sensor, a micro-weather station for collecting wind speed, temperature, and humidity parameters, and a high-definition PTZ camera for acquiring image data. The communication execution module includes a 4G communication unit as the primary high-speed channel, a BeiDou short message communication unit as a backup channel, and a LoRa communication unit as a low-power local area network channel. The core modules are a data fusion and collaborative management module, a main control decision module, and a power management module including a solar panel and a battery.

[0064] The initial system configuration is as follows: the communication strategy decision-maker presets the LoRa transmission frequency (flow) to once every 30 minutes, the 4G transmission frequency (fnormal) to once every 10 minutes, and the high-risk transmission frequency (fhigh) to once per minute. The data fusion engine of the data fusion and collaborative management module is configured with weighting coefficients: the first weighting coefficient α is 0.4, the second weighting coefficient β is 0.3, the third weighting coefficient γ is 0.2, and the fourth weighting coefficient δ is 0.1. Risk thresholds are set as follows: the first risk threshold Rlow is 0.3, and the second risk threshold Rhigh is 0.7. The module status monitoring unit continuously monitors the 4G signal strength S4g, the BeiDou signal strength Sbd, and the remaining power supply Eremain.

[0065] In the system architecture, the data fusion and collaborative management module plays a core role, with all raw data from the sensor acquisition modules being fed into it. The data fusion engine calculates the icing risk level R, and the communication strategy decision-maker generates instructions based on this result. The data fusion and collaborative management module directly commands the communication execution module to achieve intelligent automation of the communication strategy. The module status monitoring unit feeds back the communication link status and power status to the communication strategy decision-maker, forming a closed-loop decision-making process. The main control decision-making module interacts with the data fusion and collaborative management module, responsible for recording and edge computing, but does not directly control the communication details.

[0066] The first stage is intelligent hibernation under low-risk normal conditions. The time is a sunny afternoon. The sensor acquisition module collects monitoring data. The tension data F is 8.5kN, which is the conductor's own weight and there is no icing. After normalization, N(F) is approximately 0.17. The X-axis tilt angle θx is 2 degrees and the Y-axis tilt angle θy is 1 degree. After normalization, the mean N(θ) is approximately 0.1. The meteorological data includes wind speed W of 2m / s, temperature parameter T of 5℃, and humidity parameter H of 45%. The icing potential function value is calculated according to the formula H(T,H)=exp(-(T-Topt)² / k)×(H / 100), where the most icing temperature Topt is taken as -3℃ and the shape parameter k is taken as 10. The calculated value is H(T,H)=exp(-((5-(-3))² / 10))×0.45, which is approximately 0.01. The normalized wind speed value N(W) is approximately 0.1, and the meteorological term has a very small effect. Image data I is 0 mm with no icing, and the normalized value N(I) is 0.

[0067] The data fusion engine performs fusion processing according to the formula R=α·N(F)+β·(N(θx)+N(θy)) / 2+γ·N(W)·H(T,H)+δ·N(I). The calculated R=0.4×0.17+0.3×0.1+0.2×(0.1×0.01)+0.1×0=0.068+0.03+0.0002=0.0982. The communication strategy decision-maker determines that R is approximately 0.098, which is less than the first risk threshold Rlow of 0.3, indicating the system is in a low-risk state. Based on the icing risk assessment results, the communication strategy is determined to be the first transmission mode. The communication strategy decision-maker instructs the LoRa communication unit to start, sending simplified data packets to the mountain repeater every 30 minutes at the first transmission frequency flow. The data type includes tower number 89, icing risk level R of 0.10, and a timestamp. Simultaneously, it instructs the 4G communication unit and the Beidou short message communication unit to enter deep sleep mode. During this stage, the terminal power consumption is extremely low and the communication cost is almost zero, enabling silent monitoring and saving energy and operating costs.

[0068] The second phase involves risk escalation and adaptive strategy switching. The timeframe is 24 hours after the cold air intrusion and the weather turning cloudy. The sensor acquisition module collects monitoring data. The tensile force F is 11.2 kN, slowly increasing, with a normalized value N(F) of approximately 0.32. The X-axis tilt angle θx is 3.5 degrees, and the Y-axis tilt angle θy is 2.5 degrees, with a normalized value N(θ) of approximately 0.22. Meteorological data includes a wind speed W of 8 m / s, a temperature parameter T of -3℃, and a humidity parameter H of 92%. The icing potential function value H(T,H) is calculated as exp(-(-3-(-3))² / 10)×0.92=0.92, and the normalized wind speed value N(W) is approximately 0.4. Image data I shows thin ice at 2 mm, with a normalized value N(I) of approximately 0.2.

[0069] The data fusion engine reapplies the fusion calculation formula: R = 0.4 × 0.32 + 0.3 × 0.22 + 0.2 × (0.4 × 0.92) + 0.1 × 0.2 = 0.128 + 0.066 + 0.0736 + 0.02 = 0.2876. The communication strategy decision-maker determines that R is approximately 0.29, still slightly lower than the first risk threshold Rlow of 0.3. After 15 minutes, the monitoring data is updated: the tensile force F is 12.0 kN, and the temperature parameter T is -4℃. The recalculated R is 0.31. At this point, R is greater than the first risk threshold Rlow and less than the second risk threshold Rhigh. The communication strategy decision-maker determines that the system has entered a medium-risk state.

[0070] Based on the icing risk assessment results, the communication strategy is determined to be the second transmission mode. The communication strategy decision-maker immediately issues a command to wake up the 4G communication unit and shut down the LoRa communication unit. At the second transmission frequency fnormal (once every 10 minutes), a fused data packet is sent to the monitoring center via the 4G communication unit. The transmitted data includes tower number 89, icing risk level R of 0.31, tensile force F of 12.0 kN, X-axis tilt angle θx of 3.5 degrees, Y-axis tilt angle θy of 2.5 degrees, temperature parameter T of -4℃, humidity parameter H of 92%, and a timestamp. The system automatically enhances the monitoring level, providing maintenance personnel with continuous and comprehensive on-site situational awareness, and seamlessly switching from energy-saving mode to normal monitoring mode.

[0071] The third stage involves emergency alarms and multi-channel concurrent disaster recovery. The timeframe is 6 hours after the blizzard arrives. The sensor acquisition module collects monitoring data. The tensile force F is 38.5 kN, rapidly increasing to near the alarm value, with a normalized value N(F) of approximately 0.96. The X-axis tilt angle θx is 12 degrees, and the Y-axis tilt angle θy is 8 degrees, with a normalized value N(θ) of approximately 0.75. Meteorological data shows wind speed W at 15 m / s, temperature T at -5℃, humidity H at 98%, and an icing potential function H(T,H) of approximately 0.98, with a normalized wind speed value N(W) of approximately 0.75. Image data I shows 15 mm of heavy icing, with a normalized value N(I) of approximately 1.0.

[0072] The data fusion engine calculates R = 0.4 × 0.96 + 0.3 × 0.75 + 0.2 × (0.75 × 0.98) + 0.1 × 1.0 = 0.384 + 0.225 + 0.147 + 0.1 = 0.856. The communication strategy decision-maker determines that R = 0.856 is greater than the second risk threshold Rhigh = 0.7, and the system enters a high-risk alarm state. Based on the icing risk assessment results, the communication strategy is determined to be the third transmission mode. The communication strategy decision-maker immediately triggers the highest level response and concurrently starts the 4G communication unit and the Beidou short message communication unit. Transmission is performed at the third transmission frequency fhigh, i.e., once per minute.

[0073] The 4G communication unit uploads a complete data packet, transmitting data including all raw sensor data, the calculation process for the icing risk level R value, and high-resolution on-site images captured and compressed by the image module. The BeiDou short message communication unit simultaneously sends alarm data; this alarm frame is simplified to include core information such as the alarm identifier ALARM, tower number 89, icing risk level R of 0.86, tensile force F of 38.5 kN, and GPS location. Even in adverse environmental conditions, at least one channel can transmit critical alarm information.

[0074] During the data perception and fusion process, the sensor acquisition module sends data to the data fusion engine, which uses the fusion calculation formula to calculate a high-risk level R of 0.86. The communication strategy decision-maker determines the high risk based on the R value and immediately executes a concurrent transmission scheme. The communication execution module simultaneously activates the 4G communication unit and the BeiDou short message communication unit, transmitting complete data packets and core alarm frames separately for double protection.

[0075] The fourth stage reflects communication self-healing and energy consumption constraints. Continuing from the third stage, a blizzard causes power outages or severe signal transmission disruptions to 4G base stations. The module status monitoring unit monitors the communication link status, acquiring signal strength parameters and network load parameters. Based on the weighting coefficients corresponding to the signal strength parameter S4g and network load parameters, the communication link quality index is calculated using the formula Q4g=ωs·S4g+ωb·B4g. This index is compared to a preset quality threshold Qmin. If Q4g rapidly drops below the threshold Qmin, the communication link is deemed abnormal.

[0076] When a communication link is determined to be abnormal, the system switches to a backup communication link based on the icing risk assessment results and the status information of available communication links. The communication strategy decision-maker immediately determines that the 4G communication unit has failed and obtains the communication link quality index of each available backup communication link. A backup communication link selection strategy is determined based on the icing risk level. When the icing risk level is greater than or equal to the second risk threshold, a backup communication link with alarm transmission capability is selected as the target backup communication link from among the available backup communication links. The communication strategy decision-maker switches all data transmission tasks handled by the 4G communication unit to the BeiDou short message communication unit, which then needs to send core alarm frames and simplified fused data packets.

[0077] The continuous blizzard interrupted solar charging, causing the remaining power (Eremain) provided by the power management module to drop below a preset power threshold. The system adjusts the data transmission frequency based on the power status to obtain the current remaining power (Eremain). This remaining power is compared to the preset power threshold; if the remaining power is less than or equal to the threshold, the system automatically activates energy consumption constraints. An energy consumption adjustment coefficient λ is set to 0.5. The initial transmission frequency is multiplied by this coefficient to obtain the adjusted transmission frequency. All transmission frequencies, including the BeiDou short message communication unit's transmission frequency, are multiplied by the energy consumption adjustment coefficient λ to 0.5, changing the frequency from once per minute to once every two minutes. Data transmission is executed according to the adjusted frequency to ensure uninterrupted core communication, extend system lifetime, and achieve intelligent trade-offs.

[0078] To verify the advantages of the present invention over traditional solutions, a comparative experiment was conducted. Traditional solutions employ a fixed data reporting mode, periodically uploading all sensor data every 5 minutes at a preset frequency. The communication method is fixed to a 4G communication unit, lacking a risk assessment mechanism, automatic switching functionality in case of main communication link failure, and direct shutdown of some sensor functions when battery power is low.

[0079] During low-risk periods with clear weather and an icing risk level R of 0.10, the traditional method uploads data via 4G communication every 5 minutes, resulting in 288 transmissions in 24 hours, consuming high power. The proposed solution uploads simplified data via LoRa communication every 30 minutes, resulting in 48 transmissions in 24 hours, consuming low power. This invention reduces the number of transmissions by 83.3% and power consumption by approximately 70%.

[0080] When the icing risk level R is 0.31 during a medium-risk period, the traditional solution still uploads data at a fixed frequency of once every 5 minutes, with a fixed response time of 5 minutes. The solution of this invention automatically switches to uploading fused data via the 4G communication unit every 10 minutes, with a response time of 10 minutes, but including risk assessment results. Although the transmission frequency is reduced, the solution of this invention provides the icing risk level R value and trend information through data fusion, giving maintenance personnel more valuable situational awareness information.

[0081] During high-risk periods with an icing risk level R of 0.86, traditional solutions still upload data at a fixed frequency of once every 5 minutes using a single 4G communication channel. When the 4G base station fails, data transmission is interrupted, resulting in a 100% alarm message loss rate. The solution of this invention concurrently starts the 4G communication unit and the BeiDou short message communication unit at a frequency of once per minute, enabling dual-channel concurrent transmission. When the 4G communication link is abnormal, it automatically switches to the BeiDou short message communication unit, achieving a 0% alarm message loss rate and a 5-fold improvement in response speed.

[0082] When the remaining battery power is insufficient and falls below a preset threshold, traditional solutions directly shut down some sensor functions. If this occurs during a high-risk period, monitoring capability is lost, resulting in an interruption of monitoring continuity. The present invention reduces the transmission frequency to half its original frequency by setting an energy consumption adjustment coefficient λ to 0.5, adjusting the frequency from once per minute to once every two minutes. This sacrifices some real-time performance but maintains the continuity of monitoring functionality, thus ensuring continued monitoring continuity.

[0083] In the event of a 4G communication link failure, traditional solutions lack an automatic switching mechanism, resulting in data transmission interruption and fault recovery relying on manual intervention, with an average recovery time of 2 to 4 hours. The present invention's module status monitoring unit calculates the communication link quality index Q4g in real time. When Q4g falls below a preset quality threshold Qmin, the link is deemed abnormal. Based on the icing risk level R being greater than the second risk threshold Rhigh, a BeiDou short message communication unit with alarm transmission capability is automatically selected as the target backup communication link. The automatic switching time is less than 1 minute, and fault recovery is completed automatically without manual intervention.

[0084] Regarding the accuracy of icing risk assessment, traditional methods rely on a single tensile sensor to determine risk, issuing an alarm when the tensile data F exceeds a threshold. This approach does not consider meteorological conditions and tilt angle changes, leading to false alarms and missed alarms. The present invention integrates tensile data F, tilt angle data θ, meteorological data including wind speed W, temperature parameter T, humidity parameter H, and image data I. Through weighted fusion calculation, the icing risk level R is obtained. The icing potential function value H(T,H) is introduced into the meteorological data weighting calculation to quantify the impact of low temperature and high humidity meteorological conditions on icing formation, improving the accuracy of risk assessment by approximately 40%.

[0085] This embodiment integrates four key aspects—perception, decision-making, execution, and disaster recovery—into a cohesive whole. The icing monitoring terminal transforms from a simple data logger into a field intelligent agent capable of sensing risks, making intelligent decisions, executing accurately, and possessing strong self-healing capabilities. Through three mathematical models—a risk fusion calculation formula, an icing potential function, and a communication link quality index—it achieves dynamic adjustment of communication strategies driven by risk assessment based on multi-source data fusion, significantly improving the intelligence level and reliability of transmission line safety assurance. This invention reduces power consumption by approximately 70% during low-risk periods, increases response speed by 5 times during high-risk periods, reduces alarm information loss rate from 100% to 0% during communication link failures, improves risk assessment accuracy by approximately 40%, and shortens automatic fault recovery time from 2-4 hours to less than 1 minute.

[0086] Example 4 is an embodiment of the present invention. This embodiment provides an interconnection and communication system for icing monitoring terminal modules based on data fusion, including: multiple sensor acquisition modules for acquiring monitoring data; The data fusion and collaborative management module is used to fuse the monitoring data according to the data fusion rules to obtain the icing risk assessment result, determine the communication strategy according to the icing risk assessment result, and monitor the status of the communication link. The communication execution module includes multiple communication links and is used to execute data transmission according to the communication strategy. The power management module is used to provide power status information; The data fusion and collaborative management module adjusts the data transmission frequency according to the power status, and controls the communication execution module to switch to the backup communication link when the communication link is abnormal, based on the icing risk assessment results and the status information of the available communication links.

[0087] It should be noted that the core innovation of this embodiment lies in the introduction of a data fusion and collaborative management module as the intelligent processing unit of the icing monitoring terminal, realizing the transformation from passive data reporting to proactive intelligent decision-making.

[0088] The plurality of sensor acquisition modules include: Tension sensors are used to collect tension data of power transmission lines; A dual-axis tilt sensor is used to acquire tilt angle data along the X and Y axes; Micro weather stations are used to collect meteorological data such as wind speed, temperature, and humidity; The image acquisition unit is used to acquire image data of icing on power transmission lines.

[0089] The data fusion and collaborative management module includes: The data fusion engine receives raw data from multiple sensor acquisition modules, normalizes tensile data, tilt data, meteorological data and image data, performs weighted fusion calculations based on the weight coefficients corresponding to various monitoring data, and obtains the icing risk level by combining the icing potential function value, thus generating an icing risk assessment result. The communication strategy decision-maker is used to determine the risk level based on the comparison between the icing risk assessment results and the preset risk threshold, determine the corresponding transmission mode based on the risk level, generate a communication strategy including communication method, data transmission frequency and data type, and directly issue control commands to the communication execution module. The module status monitoring unit is used to monitor the signal strength parameters and network load parameters of each communication link, calculate the communication link quality index, determine whether the communication link is abnormal, monitor the current remaining power of the system, and provide status information for self-healing switching and energy consumption adjustment.

[0090] The communication execution module includes: 4G or 5G communication units are used to transmit converged and complete data in medium- and high-risk situations, and have high bandwidth characteristics. The Beidou short message communication unit is used to transmit alarm frames containing tower number, icing risk level, tension data and geographical location under high-risk conditions, and has high reliability characteristics. LoRa communication units are used to transmit simplified data in a low-risk environment and feature low power consumption.

[0091] The power management module is used for: Power the entire system; Real-time monitoring of remaining battery power; Synchronize the current remaining battery status to the data fusion and collaborative management module.

[0092] The working mechanism of the data fusion and collaborative management module includes: When the icing risk level is less than the first risk threshold, the communication strategy decision-maker controls the communication execution module to select the LoRa communication unit and transmit simplified data at a transmission frequency of once every 30 minutes, with the goal of saving power and extending the terminal's operating time. When the icing risk level is greater than or equal to the first risk threshold and less than the second risk threshold, the communication strategy decision-maker controls the communication execution module to select a 4G or 5G communication unit to transmit fused data at a transmission frequency of once every 10 minutes, with the goal of balancing energy consumption and monitoring requirements. When the icing risk level is greater than or equal to the second risk threshold, the communication strategy decision-maker controls the communication execution module to simultaneously activate the 4G or 5G communication unit and the Beidou short message communication unit, transmitting complete data and alarm information at a transmission frequency of once per minute. The goal is to ensure that the core alarm information is absolutely reliable and that the panoramic data is not lost. When the module status monitoring unit determines that the communication link is abnormal, the communication strategy decision-maker determines the backup communication link selection strategy based on the icing risk level and the communication link quality index of each available backup communication link, controls the communication execution module to switch the data transmission task to the target backup communication link, and maintains the backup routing table to record the switching history. When the current remaining power provided by the power management module is less than or equal to the preset power threshold, the data fusion and collaborative management module obtains the energy consumption adjustment coefficient, multiplies the initial transmission frequency by the energy consumption adjustment coefficient to obtain the adjusted transmission frequency, and controls the communication execution module to perform data transmission according to the adjusted transmission frequency.

[0093] This embodiment also provides an electronic device applicable to the interconnection and communication method of icing monitoring terminal modules based on data fusion, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the interconnection and communication method of icing monitoring terminal modules based on data fusion as proposed in the above embodiment.

[0094] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the data fusion-based interconnection and communication method for icing monitoring terminal modules as proposed in the above embodiments.

[0095] The storage medium proposed in this embodiment and the method for interconnecting and communicating icing monitoring terminal modules based on data fusion proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0096] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A data fusion-based interconnection and communication method for icing monitoring terminal modules, characterized in that: include, Collect monitoring data from multiple sensors; The monitoring data is fused according to the data fusion rules to obtain the icing risk assessment results; A communication strategy is determined based on the icing risk assessment results. The communication strategy includes the communication method, data transmission frequency, and data type. Data transmission is performed according to the communication strategy; Monitor the status of the communication link, and when the communication link is abnormal, switch to the backup communication link based on the icing risk assessment results and the status information of the available communication links; The data transmission frequency is adjusted according to the power supply status.

2. The interconnection and communication method for icing monitoring terminal modules based on data fusion as described in claim 1, characterized in that: The step of fusing the monitoring data according to the data fusion rules to obtain the icing risk assessment result includes: The monitoring data is normalized to obtain normalized data; The normalized data is weighted and fused according to the weight coefficients corresponding to various monitoring data to obtain the icing risk level. The icing risk assessment result is generated based on the icing risk level.

3. The interconnection and communication method for icing monitoring terminal modules based on data fusion as described in claim 2, characterized in that: The step of determining the communication strategy based on the icing risk assessment results includes: Obtain the first risk threshold and the second risk threshold; The icing risk level is compared with the first risk threshold and the second risk threshold, and the risk level is determined based on the comparison results. The corresponding transmission mode is determined based on the risk level. The communication strategy is generated based on the transmission mode.

4. The interconnection and communication method for icing monitoring terminal modules based on data fusion as described in claim 3, characterized in that: The monitoring of communication link status includes: Obtain signal strength parameters and network load parameters of the communication link; The communication link quality index is calculated based on the weighting coefficients corresponding to the signal strength parameters and the network load parameters, respectively. The communication link quality index is compared with a preset quality threshold. When the communication link quality index is lower than the preset quality threshold, the communication link is determined to be abnormal.

5. The interconnection and communication method for icing monitoring terminal modules based on data fusion as described in claim 4, characterized in that: The monitoring data includes tensile force data, tilt angle data, meteorological data, and image data; The step of weighting and fusing the normalized data according to the weighting coefficients corresponding to various monitoring data to obtain the icing risk level includes: Obtain the first weighting coefficient corresponding to the tensile force data, the second weighting coefficient corresponding to the tilt angle data, the third weighting coefficient corresponding to the meteorological data, and the fourth weighting coefficient corresponding to the image data; Calculate the icing potential function value based on the temperature and humidity parameters in the meteorological data; The normalized tensile force data is multiplied by the first weighting coefficient to obtain the first weighted value; the normalized dip angle data is multiplied by the second weighting coefficient to obtain the second weighted value; the normalized meteorological data, the third weighting coefficient, and the icing potential function value are multiplied to obtain the third weighted value; and the normalized image data is multiplied by the fourth weighting coefficient to obtain the fourth weighted value. The first weighted value, the second weighted value, the third weighted value, and the fourth weighted value are weighted and fused to obtain the icing risk level.

6. The interconnection and communication method for icing monitoring terminal modules based on data fusion as described in claim 5, characterized in that: The step of adjusting the data transmission frequency according to the power state includes: Get the current remaining battery level; Compare the current remaining battery power with a preset battery power threshold; When the current remaining battery power is greater than the preset battery power threshold, data transmission is performed according to the initial transmission frequency corresponding to the communication strategy. When the current remaining power is less than or equal to the preset power threshold, the energy consumption adjustment coefficient is obtained; Multiply the initial transmission frequency by the energy consumption adjustment coefficient to obtain the adjusted transmission frequency; Data transmission is performed according to the adjusted transmission frequency.

7. The interconnection and communication method for icing monitoring terminal modules based on data fusion as described in claim 6, characterized in that: When a communication link fails, switching to a backup communication link based on the icing risk assessment results and the status information of available communication links includes: When a communication link is determined to be abnormal, obtain the communication link quality index of each available backup communication link; The backup communication link selection strategy is determined based on the aforementioned icing risk level. When the icing risk level is less than the second risk threshold, the backup communication link with the highest communication link quality index is selected from all available backup communication links as the target backup communication link. When the icing risk level is greater than or equal to the second risk threshold, a backup communication link with alarm transmission capability is selected from each available backup communication link as the target backup communication link. Switch the data transmission task to the target backup communication link.

8. A data fusion-based interconnection and communication system for icing monitoring terminal modules, employing the data fusion-based interconnection and communication method for icing monitoring terminal modules as described in any one of claims 1 to 7, characterized in that, include: Multiple sensor acquisition modules are used to collect monitoring data; The data fusion and collaborative management module is used to fuse the monitoring data according to the data fusion rules to obtain the icing risk assessment result, determine the communication strategy according to the icing risk assessment result, and monitor the status of the communication link. The communication execution module includes multiple communication links and is used to execute data transmission according to the communication strategy. The power management module is used to provide power status information; The data fusion and collaborative management module adjusts the data transmission frequency according to the power status, and controls the communication execution module to switch to the backup communication link when the communication link is abnormal, based on the icing risk assessment results and the status information of the available communication links.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the data fusion-based icing monitoring terminal module interconnection communication method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the data fusion-based icing monitoring terminal module interconnection communication method as described in any one of claims 1 to 7.

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