An unmanned aerial vehicle inspection-based transmission line icing monitoring and intelligent deicing integrated system and method

Abstract

The application discloses a kind of transmission line icing monitoring and intelligent deicing integrated system and method based on unmanned aerial vehicle inspection, including unmanned aerial vehicle inspection module, icing monitoring module, intelligent deicing module and control and decision module.Icing monitoring module uses capacitive ice layer sensor, quickly, synchronous inversion icing thickness and density;Control and decision module assesses risk according to monitoring data, calls strategy library to generate optimal deicing scheme;Intelligent deicing module includes clamping mechanism, can be fixed to line before operation, to execute safe and controllable deicing operation in mechanical ice removal and high-frequency micro-vibration collaborative mode;Corresponding method realizes closed-loop process from inspection, fast sensing, intelligent decision to safe deicing.The application solves the problem of response delay and operation safety risk caused by the separation of monitoring and deicing system in the prior art, and realizes fast, safe and intelligent integrated disposal.

Description

Technical Field

[0001] This invention belongs to the field of power transmission line operation and maintenance technology, specifically relating to an integrated system and method for monitoring and intelligent de-icing of power transmission lines based on drone inspection. Background Technology

[0002] In existing technologies, integrated UAV de-icing systems for power transmission lines, designed to improve response speed, integrate icing status perception and de-icing execution functions to achieve rapid closed-loop handling after discovering hazards during inspections. However, in practical implementation, inherent coordination barriers between the monitoring unit and the de-icing unit at the underlying technical principles often lead to difficulties in simultaneously pursuing rapid response and ensuring the safety and controllability of the operation process. This has become a prominent technical bottleneck restricting further improvement in its efficiency.

[0003] Specifically, existing technologies use ultrasonic vibration as the de-icing method. The de-icing execution end relies on the transmission of vibration energy to break the ice layer, while the state sensing end needs to use a contact-type ultrasonic thickness measurement method. To achieve rapid linkage between "monitoring and de-icing," the system needs to complete thickness measurement and trigger the removal operation in real time during inspection. However, to ensure accuracy, ultrasonic thickness measurement requires contact pretreatment procedures such as ice surface grinding and coupling agent application before measurement. These additional operations objectively delay the response chain from sensing to decision-making. The ultrasonic vibration de-icing method used to meet the needs of rapid removal has the characteristics of strong nonlinearity and difficulty in precise quantification in the energy transmission process to the conductor-ice composite system. When applied to transmission lines, it generates continuous and difficult-to-assess broadband mechanical stress. This may not only damage key components such as conductors or insulators due to energy accumulation effects, but also make the entire de-icing operation lack sufficient safety margin and controllability. Therefore, existing technologies still suffer from the problem of delayed response and operational safety risks due to the disconnect between monitoring and de-icing systems, which is a shortcoming of existing technologies.

[0004] In view of this, the present invention provides an integrated system and method for monitoring and intelligently de-icing icing of power transmission lines based on unmanned aerial vehicle (UAV) inspection. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the existing technology, which suffers from delayed response and operational safety risks due to the disconnect between monitoring and de-icing systems. This invention provides an integrated system and method for monitoring and intelligent de-icing icing on power transmission lines based on unmanned aerial vehicle (UAV) inspections, thereby solving the aforementioned technical problems.

[0006] To achieve the above-mentioned objectives, this invention eliminates the cumbersome pretreatment process of grinding and applying coupling agent required for traditional ultrasonic thickness measurement, and uses a capacitive ice layer sensor to achieve rapid inversion without pretreatment; at the same time, it eliminates the traditional UAV hovering de-icing mode and introduces a clamping mechanism and a collaborative de-icing mechanism. Firstly, this application provides an integrated system for monitoring and intelligently de-icing icing on power transmission lines based on unmanned aerial vehicle (UAV) inspection, comprising: The drone inspection module includes an airborne sensing device that performs flight inspection missions along power transmission lines, collecting images, temperature data, and 3D point cloud data of the lines. The icing monitoring module includes a capacitive ice sensor that is placed close to or in contact with the line during the inspection process. By detecting the change in the equivalent capacitance value between the electrodes in real time, the thickness and density of the ice are calculated. The control and decision-making module cross-validates image, temperature, and 3D point cloud data, as well as ice thickness and density, to obtain ice status data. Based on the ice status data, it calls the ice risk assessment model to determine the risk. When the risk is determined to be high, it generates the optimal de-icing plan based on the de-icing strategy library. The intelligent de-icing module is a detachable working mechanism mounted on the drone inspection module. After receiving the de-icing command, it is transported by the drone inspection module to the target location and performs fixed-point de-icing operations.

[0007] By adopting the above technical solution, the measurement error of a single sensor is effectively eliminated through the fusion of multi-source heterogeneous data, and the accuracy of icing status judgment is significantly improved. Through hierarchical control architecture and intelligent decision-making algorithm, a rapid closed-loop response from icing detection to precise de-icing is realized.

[0008] Specifically, the UAV inspection module utilizes visible light, infrared thermal imaging, and lidar to construct a three-dimensional observation system encompassing vision, thermal field, and geometric structure; the icing monitoring module acquires multiple sets of differentiated capacitance values ​​through a cylindrical array multi-electrode sensor, and combines this with a pre-stored mapping model to invert icing thickness and density; the control and decision-making module, as the central hub, corrects density deviations and verifies thickness dimensions through data fusion algorithms, and calculates safety factors based on wind loads to achieve accurate risk assessment; the intelligent de-icing module achieves a rigid connection between the drone body and the conductor through a clamping mechanism, and, in conjunction with ice-cutting and vibration-assisted units, completes safe and efficient operations under the guidance of the optimal de-icing scheme.

[0009] Preferably, the capacitive ice sensor in the icing monitoring module adopts a cylindrical array multi-electrode structure. By detecting the change in the equivalent capacitance value between electrodes at different spatial positions, multiple sets of measurement values ​​with differentiated responses to ice thickness and density are obtained. By adopting the above technical solution, and utilizing the differentiated sensitivity of electrodes at different spatial locations to changes in ice thickness and density, a multi-input equation system is constructed, which solves the problem that a single capacitance value cannot simultaneously calculate the two variables of thickness and density.

[0010] Preferably, the icing monitoring module further includes a data processing unit, which pre-stores a capacitance-thickness-density relationship mapping model, inputs multiple sets of measurement values ​​into the capacitance-thickness-density relationship mapping model, and outputs the thickness and density of the icing. The capacitance-thickness-density relationship mapping model is established based on icing sample data of different thicknesses and densities and their corresponding capacitance measurements, and constructs a mapping relationship between the input capacitance sequence and the output thickness and density estimates. By adopting the above technical solution, the mapping model established through experimental calibration or numerical simulation can quickly and accurately convert capacitance signals into physical thickness and density values, thereby improving the real-time performance and accuracy of monitoring.

[0011] Preferably, the intelligent de-icing module includes an ice-shaving mechanism, a clamping mechanism, and a vibration-assisted de-icing unit; The ice-cutting mechanism includes a rotating ice-cutting wheel with multiple ice-cutting cones for mechanically cutting the ice. The clamping mechanism temporarily fixes the intelligent de-icing module to the power transmission line before the de-icing operation; The vibration-assisted de-icing unit includes a high-frequency micro-vibration device, the vibration frequency of which is adjustable in coordination with the rotational speed of the ice-cutting wheel, and is used to apply auxiliary vibration during cutting. By adopting the above technical solution, the clamping mechanism solves the problem of unstable hovering of the UAV and achieves rigid fixation of the working mechanism; the combined operation of ice cutting and vibration reduces cutting resistance, improves de-icing efficiency, and avoids damage to the wire by excessive single mechanical force.

[0012] Preferably, the control and decision-making module includes a ground station unit and an airborne control unit; The ground station unit receives data collected by the UAV inspection module and the icing monitoring module, performs icing risk assessment, calls the de-icing strategy library to generate the optimal de-icing plan, and decomposes the plan into control commands. The airborne control unit receives control commands from the ground station unit and drives the UAV inspection module, icing monitoring module, and intelligent de-icing module to work together to execute the optimal de-icing solution. By adopting the above technical solution and through a hierarchical control architecture, the high computing power requirements of the decision-making layer and the real-time response requirements of the execution layer are separated, thus ensuring the control stability of the system in complex electromagnetic environments.

[0013] Preferably, the control and decision module performs cross-validation on imagery, temperature, and 3D point cloud data, as well as ice thickness and density, to obtain icing status data, including: Use image data to define icing zones; Temperature data is used to correct the ice density retrieved by the icing monitoring module. The geometric dimensions of the ice accretion are quantified using 3D point cloud data, and the ice thickness obtained by the ice accretion monitoring module is verified. Based on imagery, temperature, and 3D point cloud data, as well as cross-validation of icing thickness and density, icing status data including thickness, density, and type were obtained. By adopting the above technical solution, through cross-validation and mutual verification of multi-source data, the influence of temperature drift on capacitance measurement is effectively eliminated, geometric errors are corrected, and the reliability of the final icing state data is ensured.

[0014] Preferably, the control and decision-making module is equipped with an icing risk assessment model to assess the icing status and determine the risk. The icing risk assessment model calculates the static icing load based on icing status data and calculates the wind load increment based on real-time wind speed. The calculated static icing load and wind load increment are vectorized to obtain the total equivalent load borne by the conductor. The load safety factor is calculated based on the total equivalent load and the design safe load; the load safety factor is compared with the preset risk trigger threshold; when the load safety factor is lower than the risk trigger threshold, the current state is determined to be high risk, and the de-icing response process is triggered. By adopting the above technical solution, the coupling effect of the self-weight of icing and wind load is comprehensively considered, and combined with the design safety margin of the line itself, the transformation from simple thickness monitoring to mechanical risk assessment is realized, avoiding the problems of excessive or untimely de-icing.

[0015] Preferably, the de-icing strategy library is a knowledge base built based on expert rules and case reasoning, configured to: receive input features including multiple factors such as ice thickness, density, type, line parameters and environmental conditions, and perform matching and calculation according to the pre-stored "condition-action" rule set to output the optimal de-icing solution; By adopting the above technical solution, the complete reliance on manual experience in de-icing operations is eliminated. It can automatically match the best operating parameters according to different icing conditions and line parameters, thus achieving differentiated and precise handling.

[0016] As a preferred option, after the intelligent de-icing module completes its work, the control and decision-making module controls the drone inspection module to conduct a secondary inspection of the work area. The de-icing effect is evaluated by comparing the secondary inspection data with the inspection data before de-icing, and the entire process data is archived into the icing management database. By adopting the above technical solution, a complete closed loop of "monitoring-decision-operation-evaluation" is formed, which ensures the quality of de-icing, while the accumulated historical data can be used to optimize the risk assessment model and de-icing strategy library.

[0017] Secondly, this application also provides an integrated method for monitoring and intelligent de-icing of power transmission lines based on drone inspection, comprising the following steps: Step S1: Using the onboard sensing device of the UAV inspection module, perform flight inspection along the power transmission line to collect images, temperature and three-dimensional point cloud data of the line. Step S2: During the inspection, the capacitive ice layer sensor of the icing monitoring module is brought close to or in contact with the line to detect the change in the equivalent capacitance value between the electrodes in real time, and the thickness and density of the ice are calculated based on the change in the equivalent capacitance value. Step S3: Using the control and decision module, cross-validate the image, temperature, and 3D point cloud data, as well as the ice thickness and density to obtain ice status data; call the ice risk assessment model to determine the risk; when the risk is determined to be high, call the de-icing strategy library to generate the optimal de-icing plan containing specific operation parameters. Step S4: Control the drone to carry the intelligent de-icing module to the target location; temporarily fix the module to the power transmission line through the clamping mechanism in the intelligent de-icing module; according to the optimal de-icing scheme, start the ice-shaving mechanism and vibration auxiliary unit to perform coordinated de-icing; Step S5: After the de-icing operation is completed, control the drone to conduct a second inspection of the operation section and evaluate the de-icing effect by comparing the data before and after the operation.

[0018] The beneficial effects of this invention are as follows: It uses a capacitive ice layer sensor to replace traditional ultrasonic thickness measurement, utilizing the change in the dielectric constant of the ice layer to invert the thickness and density, reducing the single-point measurement response time to the millisecond level, and achieving real-time sensing during inspection; by setting a clamping mechanism in the intelligent de-icing module, the module is temporarily fixed to the power line before operation, avoiding the risk of the machine colliding with the conductor; simultaneously, it adopts a collaborative operation mode of "ice-shaving mechanism cutting + high-frequency micro-vibration device loosening," where the vibration frequency and the speed of the ice-shaving wheel are adjustable in tandem, reducing cutting resistance and preventing conductor breakage or fatigue damage caused by excessive single mechanical force; control and decision-making. The module eliminates the error of a single sensor through multi-source cross-validation of image, temperature, and 3D point cloud data with capacitance measurements. Based on the icing risk assessment model, it calculates the total equivalent load including static icing load and wind load increments, and triggers de-icing according to the load safety factor. Compared with simply judging based on thickness threshold, this avoids the problems of over-de-icing or untimely de-icing. By integrating inspection, monitoring, and de-icing functions through the same UAV platform, and using the de-icing strategy library to automatically match the optimal operating parameters according to the icing conditions, it realizes a complete closed loop from "monitoring-decision-operation-secondary inspection and evaluation", improving the automation level of power transmission line operation and maintenance.

[0019] Furthermore, the design principle of this invention is reliable, the structure is simple, and it has a very wide range of application prospects.

[0020] Therefore, it is evident that the present invention has substantial features and progress compared with the prior art, and the beneficial effects of its implementation are also obvious. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0022] Figure 1 This invention provides a schematic diagram of an integrated system for monitoring and intelligent de-icing of power transmission lines based on unmanned aerial vehicle (UAV) inspection.

[0023] Figure 2 The present invention provides a flowchart of an integrated method for monitoring and intelligent de-icing of power transmission lines based on unmanned aerial vehicle (UAV) inspection.

[0024] Among them, 1-UAV inspection module, 2-Icing monitoring module, 3-Control and decision-making module, 4-Intelligent de-icing module, 1-1-High-definition visible light imaging unit, 1-2-Infrared thermal imaging unit, 1-3-LiDAR scanning unit, 2-1-Capacitive ice layer sensor, 2-2-Data processing unit, 3-1-Airborne control unit, 3-2-Ground station unit, 4-1-Ice-shaving mechanism, 4-2-Clamping mechanism, 4-3-Vibration-assisted de-icing unit. Detailed Implementation

[0025] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following implementation methods.

[0026] Example 1: This invention provides an integrated system for monitoring and intelligently removing icing on power transmission lines based on unmanned aerial vehicle (UAV) inspection, such as... Figure 1 As shown, it includes a drone inspection module 1, an icing monitoring module 2, an intelligent de-icing module 4, and a control and decision-making module 3; The UAV inspection module 1 is equipped with an onboard sensor device and performs flight inspection tasks along the power transmission line to collect images, temperature and three-dimensional point cloud data of the line. The ice monitoring module 2 includes a capacitive ice sensor 2-1, which is placed close to or in contact with the line during the inspection process. By detecting the change in the equivalent capacitance value between the electrodes caused by the change in the dielectric constant of the ice layer in real time, the thickness and density of the ice layer are calculated. The control and decision module 3 cross-validates the image, temperature, and 3D point cloud data, as well as the ice thickness and density, to obtain ice status data. Based on the ice status data, it calls the ice risk assessment model to determine the risk. When the risk is determined to be high, it generates the optimal de-icing plan based on the de-icing strategy library. The intelligent de-icing module 4 is a detachable working mechanism mounted on the UAV inspection module 1. After receiving the de-icing command, it is transported by the UAV inspection module 1 to the target location and performs fixed-point de-icing operations. Furthermore, the UAV inspection module 1 includes a UAV aircraft as a carrier, and a variety of airborne sensing devices carried by the UAV aircraft, including a high-definition visible light imaging unit 1-1, an infrared thermal imaging unit 1-2, and a lidar scanning unit 1-3. The high-definition visible light imaging unit 1-1 acquires high-resolution visible light image data of the transmission line and its attachments for morphological recognition and visual positioning of the ice accretion. The infrared thermal imaging unit 1-2 collects temperature data of infrared thermal imaging on the surface of the transmission line, and uses temperature field characteristics to help determine the icing area, ice layer state and phase change process. The lidar scanning units 1-3 actively emit lasers and receive reflected signals to generate high-precision three-dimensional point cloud data of the transmission line and its surrounding environment, providing a data basis for quantitative geometric measurement of icing thickness and conductor sag.

[0027] The UAV inspection module 1 also includes an onboard processing unit for preprocessing the raw sensor data; During operation, the UAV inspection module 1 performs flight inspection tasks according to preset routes or control commands: During flight, multiple airborne sensors are triggered synchronously or in time-sharing to collect integrated image, temperature and three-dimensional point cloud data of the target power transmission line section. The collected raw data is preprocessed by the airborne processing unit and then transmitted to the ground station unit 3-2 of the control and decision module 3 via a wireless data link, providing data input for subsequent icing status inversion and risk assessment.

[0028] Furthermore, the icing monitoring module 2 includes a capacitive ice layer sensor 2-1 and a data processing unit 2-2; The capacitive ice layer sensor 2-1 adopts a cylindrical array multi-electrode structure. When the capacitive ice layer sensor 2-1 comes into contact with the ice layer formed on the power transmission line, the ice layer, as a dielectric, causes a change in the equivalent capacitance value between the sensor electrodes. By detecting the change in the equivalent capacitance value in real time, the thickness and density of the ice layer can be calculated. The measurement of the capacitive ice layer sensor 2-1 is not affected by wind speed and can maintain stable operation under severe weather conditions such as high humidity and dense fog, thus solving the problem of easy distortion of monitoring data. The data processing unit 2-2 receives raw capacitance signal data from the capacitive ice sensor 2-1. The raw capacitance signal data is typically a voltage or digital sequence that has undergone analog-to-digital conversion. The data processing unit 2-2 filters, converts, and performs preliminary calculations on the raw capacitance signal data to extract the change in equivalent capacitance value. The data processing unit 2-2 has a pre-stored capacitance-thickness-density relationship mapping model established through experimental calibration or numerical simulation. The data processing unit 2-2 inputs the capacitance change into the capacitance-thickness-density relationship mapping model to deduce the specific ice thickness and density physical quantities, thereby generating structured ice accretion state information. Furthermore, the capacitance-thickness-density relationship mapping model is established based on icing sample data of different thicknesses and densities and their corresponding capacitance measurements, constructing a mapping relationship between the input capacitance sequence and the output thickness and density estimates.

[0029] When the system is working, the icing monitoring module 2 and the UAV inspection module 1 work together; the capacitive ice sensor 2-1 is installed at a specific position or on the working mechanism of the UAV, and measures are taken close to or in contact with the wire during the inspection process; the raw capacitance signal data collected, together with the image, temperature and three-dimensional point cloud data obtained by the UAV inspection module 1, are transmitted to the ground station unit 3-2 in the control and decision module 3. Furthermore, in the cylindrical array multi-electrode structure, the electric field formed between electrode pairs at different spatial positions has differentiated response characteristics to changes in ice thickness and density, and multiple sets of capacitance measurement values ​​with different sensitivities can be acquired simultaneously. The process involves calculating the thickness and density of the ice layer by real-time detection of changes in the equivalent capacitance value. Data processing unit 2-2 processes the raw capacitance signals of each electrode pair to obtain an equivalent capacitance value sequence characterizing the capacitance changes of each electrode pair. This equivalent capacitance value sequence is then input into a capacitance-thickness-density relationship mapping model pre-established through experimental calibration. The capacitance-thickness-density relationship mapping model is constructed based on the physical principle that ice thickness primarily affects the equivalent electrode spacing, while ice density primarily affects the inter-electrode medium properties. Internally, the model utilizes multiple sets of measured values ​​with differentiated responses to construct an effective set of equations, achieving simultaneous calculation of the two variables: ice thickness and density. Specifically, the calculation process can be completed through table lookup matching, interpolation calculation, or forward inference using a trained neural network model, ultimately outputting the ice thickness and density. Laboratory calibration and verification show that the capacitance-thickness-density relationship mapping model, within the range of ice thickness 0-50 mm and density 0.1-0.9 g / cm³, controls the thickness inversion error within ±5% and the density inversion error within ±0.02 g / cm³, outperforming traditional single-capacitance measurement methods.

[0030] Specifically, the capacitive ice layer sensor 2-1 has N electrode pairs and measures N equivalent capacitance values. , ,..., For the i-th electrode pair, its capacitance The dependence can be approximated using the formula for parallel plate capacitance:

[0031] in, It is the effective relative area of ​​the i-th electrode pair, determined by the electrode geometry G; It is the effective equivalent distance between the i-th electrode pairs after icing, and is a function of the icing thickness h and the original electrode spacing; h is the icing thickness. This refers to the density of the ice layer. The dielectric constant of air; is the relative permittivity of ice; since the electrodes are arranged in a cylindrical array, electrode pairs at different spatial positions have different permittivity. and Functional form; N measurement values With theoretical models Establishing equations constitutes a system of N nonlinear equations:

[0032] The capacitance-thickness-density relationship mapping model can be implemented using a pre-established lookup table, and the construction methods include: In the experimental environment, a large number of ice-covered samples with different known thicknesses and densities were prepared; each sample was measured using the cylindrical array multi-electrode capacitive sensor, and the corresponding equivalent capacitance value sequence was recorded; the density value and thickness value of each sample were associated with and stored with their corresponding capacitance value sequence to form a lookup table; In practical applications, the data processing unit 2-2 calculates the Euclidean distance between the real-time measured equivalent capacitance value sequence and all entries in the lookup table and compares the similarity. It selects the entry with the highest similarity and outputs the thickness and density values ​​associated with that entry as the final ice thickness and density estimates. The table lookup and matching process is essentially equivalent to solving the system of equations consisting of the N nonlinear equations, thereby enabling the simultaneous and rapid calculation of ice thickness and density.

[0033] Furthermore, the control and decision module 3 adopts a hierarchical control architecture, which is divided into two levels: the airborne control unit 3-1 and the ground station unit 3-2. The two work together through a wireless data link. The airborne control unit 3-1 receives instructions from the ground station unit 3-2 and directly controls the real-time actions of the sensors of the UAV flight platform, the icing monitoring module 2, and the intelligent de-icing module 4; ensuring that the operation instructions can be quickly converted into the physical actions of the equipment. The ground station unit 3-2 serves as the decision-making center, responsible for receiving image, temperature, and 3D point cloud data collected by the UAV inspection module 1, as well as icing thickness and density data obtained from the inversion calculation by the icing monitoring module 2; and calling a multi-source data fusion algorithm to comprehensively process and analyze the received data, specifically including: Visible light image data is used to identify and locate icing areas, define the approximate sections of icing, and provide spatial constraints for subsequent quantitative analysis; The ice type is distinguished according to the temperature field characteristics, and the standard density reference value of the ice layer of the type is obtained by querying the pre-stored ice type-density comparison table. The standard density reference value is used as prior knowledge and substituted into the inversion algorithm of the capacitance-thickness-density relationship mapping model to correct the initial value of ice density obtained by the ice monitoring module 2 and eliminate the dielectric constant measurement deviation caused by temperature drift. The sag and icing profile of the conductor are measured using three-dimensional point cloud data. The conductor axis is fitted using the RANSAC algorithm, and the maximum radial distance from the conductor surface to the outside of the point cloud cluster is calculated to quantify the geometric dimensions of the icing. The difference between the geometric measurement value and the thickness value obtained by the icing monitoring module 2 is compared. If the difference exceeds the preset error threshold, the capacitance inversion result is calibrated based on the geometric thickness measured by the point cloud. Based on the cross-validation of the above multi-source data, weighted least squares method is used for data fusion, specifically including: When the difference between the geometric dimensions obtained by quantizing the 3D point cloud data and the ice thickness obtained by the icing monitoring module 2 is within the error threshold, the weighted average of the two is taken as the final ice thickness; when the difference exceeds the error threshold, the geometric dimensions obtained by quantizing the 3D point cloud data are used as the final ice thickness. Finally, by combining the corrected density data, the icing status data including thickness, density and type was confirmed, eliminating the measurement error of a single sensor. The measured data showed that after introducing temperature data to correct the density, the density measurement deviation was reduced from ±15% to ±5%; after introducing point cloud data to verify the thickness, the thickness measurement deviation was reduced from ±10% to ±3%.

[0034] Furthermore, the icing risk assessment model automatically determines the degree of icing danger based on icing status data; the icing risk assessment model converts the current icing condition into an equivalent load on the line and compares it with the line's safe carrying capacity. The icing risk assessment model calculates the static icing load based on icing state data, simplifying icing as a hollow cylinder uniformly wrapped around the conductor; the specific expression is:

[0035] in, For static icing load, This represents the density data from the icing state data; g is the gravitational acceleration, taken as 9.80665. D represents the icing thickness in the icing status data; D represents the outer diameter of the conductor obtained from the line ledger.

[0036] The wind load increment is calculated based on real-time wind speed and relevant standards. The calculation of the wind load increment must consider the increase in the windward area of ​​the conductor after icing. The specific expression for the wind load P per unit length of the conductor after icing is:

[0037] in, V is the reference wind pressure; V is the real-time wind speed collected by the environmental sensor. , , These are the wind pressure height variation coefficient, conductor shape coefficient, and wind load adjustment coefficient, respectively. These coefficients can be obtained from relevant design specifications based on the region, terrain, and the state of the conductor after icing. d is the calculated outer diameter after icing, d=D+2 ; This refers to the horizontal spacing.

[0038] The calculated static icing load The total equivalent load on the conductor is obtained by vector synthesis with the wind load P; the total load is then compared with the design safety load of the line. Simulation results show that, compared with the traditional method of judging based solely on thickness thresholds, the icing risk assessment model comprehensively considers the wind load increment, reducing the misjudgment rate by 30%, reducing the frequency of unnecessary de-icing operations, and extending the service life of the conductor. Specifically, the design safety load of a transmission line is the maximum mechanical load that the target transmission line is allowed to withstand under specific operating conditions; methods for obtaining this load include: In the standardized line ledger of the power system, the design parameters of the critical lines are usually recorded; among them, the design safety load or allowable load exists directly as a field; when the ground station unit 3-2 of the control and decision module 3 initializes the task, or according to the real-time positioning information, it queries and calls this parameter of the target line from the standardized line ledger. If it is not directly recorded in the ledger, it can be calculated by back-calculating the design safety load = calculated breaking force of the conductor / safety factor based on the conductor type and safety factor specified in the line ledger. The calculated breaking force of the conductor is an inherent mechanical characteristic parameter of the conductor model, which can be found in national standards or manufacturer technical data according to the model; the safety factor is the minimum value specified for this type of line under icing conditions in the regulations on which the line design is based. For lines under key monitoring, maintenance personnel can pre-configure the approved design safety load as a fixed value in the parameter table of the icing risk assessment model based on the line's as-built data, previous assessment reports, or special anti-icing design conclusions.

[0039] After calculating the total equivalent load of the conductor, the current safety factor K is calculated according to the load safety factor K = design safe load / total equivalent load. The calculated safety factor K is then compared with the preset risk trigger threshold. According to power industry regulations and line design standards, the risk trigger threshold is set to 1.2, meaning that a high risk is determined when the safety factor K < 1.2. Simulation results show that using this risk trigger threshold to determine high risk reduces unnecessary de-icing operations by about 30% compared to the simple thickness threshold method, while also avoiding the risk of tower collapse due to neglecting wind load. When the K value is lower than the risk trigger threshold, the current state is determined to be high risk, and the de-icing response process is automatically triggered.

[0040] Once the de-icing response is triggered, ground station unit 3-2 calls the de-icing strategy library to generate the optimal de-icing plan; The de-icing strategy library is constructed using a knowledge base method based on expert rules and case reasoning. Based on historical de-icing experience, experimental data, and domain knowledge, the de-icing strategy library pre-constructs a rule set containing a large number of "operating condition-parameter" mapping relationships. The construction process is as follows: The system systematically collects and organizes data covering various typical icing scenarios, including different icing types, thickness gradients, density ranges, line models, and environmental parameters such as temperature, humidity, and wind speed. It also records the optimal de-icing parameters that have been verified in practice to be safe and efficient, including the target speed of the ice-shaving wheel, the operating frequency of the high-frequency micro-vibration device, and the operating mode. Based on domain experts or through data analysis, these discrete best practice cases are summarized and abstracted into a series of "condition-action" rules. Each rule clearly specifies the recommended operating parameters that the system should adopt when a specific set of input conditions is met.

[0041] In actual operation, ground station unit 3-2 integrates the real-time acquired ice thickness, density, type, environmental data and line information into a multi-dimensional feature vector, and calculates the matching degree with all rules in the strategy library; by selecting one or more rules with the highest matching degree, and using its matching degree score as weight, the parameters such as ice-shaving wheel speed and vibration frequency output by the corresponding rule are weighted and averaged to obtain the final fusion parameters. Simultaneously, based on the 3D point cloud data provided by the UAV inspection module 1 and the icing boundary determined by the icing monitoring module 2, the operation path is planned: The RANSAC algorithm or least squares method is used to segment the traverse point cloud clusters from the 3D point cloud data, and the 3D spatial curve equation of the traverse axis is fitted. Using the fitted traverse axis as a reference, a preset safe operating distance is offset along the traverse normal to generate a UAV flight reference trajectory parallel to the traverse direction. The icing boundary is mapped onto the UAV flight reference trajectory, and the corresponding trajectory segment is extracted as the operating path. Combining the tower and ground feature information in the 3D point cloud data, the path is locally optimized for obstacle avoidance using the artificial potential field method, and smoothed using B-spline curves. For continuous icing sections, a unidirectional uniform speed travel path is planned from the starting point to the ending point. For isolated icing points, a fixed-point operation mode is planned to fly to the point above it.

[0042] By combining the final fusion parameters and the operation path, an optimal de-icing solution containing precise operation instructions is generated for the current specific working conditions.

[0043] The generated optimal de-icing scheme is broken down into specific control commands, which are sent to the airborne control unit 3-1 through the ground station. The airborne control unit 3-1 controls the UAV to fly to the work site and operates the intelligent de-icing module 4 to complete a series of actions such as clamping, ice cutting, and vibration. During the operation, the high-definition visible light imaging unit 1-1 transmits images back in real time. The ground station can dynamically adjust the operation parameters according to the transmitted images to achieve adaptive control. After the de-icing operation is completed, ground station unit 3-2 can instruct the UAV to conduct a second inspection; by comparing the data before and after the operation, the de-icing effect can be evaluated; the data of the entire process from inspection, monitoring, decision-making to de-icing is archived into the icing management database, forming a traceable data closed loop, which is used for subsequent statistical analysis, model optimization and operation and maintenance strategy formulation. Built-in risk assessment models and strategy libraries enable the system to automatically generate optimal operation plans based on multi-dimensional information, eliminating complete reliance on human experience and achieving differentiated and precise handling. Integrating monitoring, assessment, decision-making, and control functions into a unified module enables a rapid closed-loop response from "discovery" to "handling," overcoming the problems of separation between monitoring and de-icing systems and long response cycles in traditional technologies. Through hierarchical control and dynamic process adjustment, it ensures precise and controllable de-icing operation parameters, avoiding excessive mechanical stress damage to conductors. Full-process data archiving and management provide valuable data support for icing mechanism research, model iteration, and continuous optimization of operation and maintenance strategies.

[0044] Furthermore, the intelligent de-icing module 4 is a detachable operating mechanism mounted on the bottom of the UAV inspection platform; the intelligent de-icing module 4 receives control commands and performs safe, efficient and controllable physical removal operations on the identified icy sections; the intelligent de-icing module 4 includes an ice-shaving mechanism 4-1, a clamping mechanism 4-2 and a vibration-assisted de-icing unit 4-3; The ice-shaving mechanism 4-1 includes an ice-shaving body, an ice-shaving wheel, and a drive assembly; the ice-shaving wheel is circumferentially provided with multiple ice-shaving cones, which rotate at high speed under the drive assembly, and use its cones to cut and break the ice on the power transmission line.

[0045] The clamping mechanism 4-2 includes an openable and closable fixing clamp; before the de-icing operation, the clamping mechanism 4-2 is activated to temporarily and securely clamp and fix the intelligent de-icing module 4 to the power transmission line; based on mechanical simulation and wind tunnel test, the clamping mechanism can provide a clamping force of more than 200N, ensuring that the module does not shift under level 7 wind, eliminating the impact risk caused by the unstable hovering of the drone, and protecting the safety of the line equipment; The vibration-assisted de-icing unit 4-3 includes a high-frequency micro-vibration device. The vibration-assisted de-icing unit 4-3 is activated simultaneously with the mechanical crushing of the ice-shaving mechanism 4-1, applying high-frequency, low-amplitude controllable vibration to the line. The vibration frequency is adjustable in sync with the rotational speed of the ice-shaving wheel. Test data shows that when the rotational speed of the ice-shaving wheel is set to 500 rpm and the vibration frequency is set within the adjustable range of 50 Hz to 200 Hz, the cutting resistance is reduced by 40% compared with single mechanical cutting, the de-icing residue rate is less than 5%, and no wire strand breakage occurs.

[0046] This causes fatigue stress and loosening within the ice layer, which, in conjunction with the ice-cutting action, improves the overall de-icing efficiency.

[0047] Under the command of the control and decision-making module 3, the intelligent de-icing module 4 works in conjunction with the UAV platform to complete the operation: when the icing monitoring module 2 determines that the icing of a certain section exceeds the limit and triggers the de-icing response, the UAV will fly back to the base or a designated location and carry the intelligent de-icing module 4; the UAV carrying the intelligent de-icing module 4 flies to the airspace above the target icing section and locates it through a vision or positioning system; the clamping mechanism 4-2 is activated to firmly fix the intelligent de-icing module 4 on the line; at this time, the UAV load is unloaded, and only the connection and communication are maintained; According to the optimal de-icing plan issued by ground station unit 3-2, the ice-shaving mechanism 4-1 and vibration-assisted de-icing unit 4-3 of the intelligent de-icing module 4 are activated as instructed to work together to remove the ice layer by layer and in a directional manner. During the operation, the UAV's onboard camera transmits the operation footage back to the ground station in real time, and the operator or system dynamically adjusts the operation parameters according to the actual situation. After the de-icing task is completed, the clamping mechanism 4-2 is released, the UAV separates from the de-icing module, and returns to charge or perform other tasks.

[0048] The device is stably fixed to the line by the clamping mechanism 4-2, eliminating the instability of the drone hovering operation and avoiding accidental impact damage to the conductor. The combined operation of mechanical ice cutting and high-frequency micro-vibration improves the removal efficiency by applying a combined effect to the ice layer. The de-icing parameters can be dynamically adjusted by the control and decision module 3 according to the ice thickness and type, realizing differentiated and precise operation and avoiding damage to the conductor due to over-operation. The detachable mounting design allows the drone platform to flexibly switch between inspection mode and de-icing mode, improving the overall utilization rate and task adaptability of the system.

[0049] Example 2: An integrated method for monitoring and intelligent de-icing of power transmission lines based on drone inspection, such as... Figure 2 As shown, it includes the following steps: Step S1: Control the drone equipped with an organic sensor device to perform a flight inspection along the power transmission line and collect images, temperature and three-dimensional point cloud data of the line. The airborne sensing device includes a high-definition visible light camera, an infrared thermal imager, and a lidar; the UAV receives a preset inspection route from the ground control station, flies autonomously along the route, and simultaneously triggers the airborne sensing device to acquire high-resolution images of the route, surface temperature distribution, and a three-dimensional point cloud model. Step S2: During the inspection, the capacitive ice layer sensor mounted on the drone is brought close to or in contact with the line to detect the change in the equivalent capacitance value between the electrodes caused by the change in the dielectric constant of the ice layer in real time, and the thickness and density of the ice layer are calculated synchronously based on this change. The capacitive ice sensor adopts a cylindrical array multi-electrode structure. During flight inspection, the probe is manipulated by a robotic arm to approach or lightly touch the ice surface. The internal circuit of the capacitive ice sensor detects the equivalent capacitance between each electrode pair in real time and outputs the original voltage signal sequence. The input is a capacitance-thickness-density relationship mapping model that has been established through a large number of experiments, and the ice thickness and density at the current measurement point are calculated simultaneously. Step S3: Based on the image, temperature, and 3D point cloud data of the line, as well as the icing thickness and density, perform status assessment and risk determination through the icing risk assessment model; when the risk is determined to be high, call the de-icing strategy library to generate the optimal de-icing plan containing specific operational parameters. Specifically, the icing risk assessment model integrates real-time wind speed, line design safety load, and icing thickness and density obtained in step S2; calculates the total equivalent load of the conductor under the self-weight of icing and wind load, and calculates the load safety factor based on the total equivalent load and design safety load; compares the load safety factor with a preset risk trigger threshold, and determines the current state as high risk when the load safety factor is lower than the risk trigger threshold. The process of generating the optimal de-icing plan by calling the de-icing strategy library includes: integrating real-time acquired data on ice thickness, density, type, environmental conditions, and route information into a multi-dimensional feature vector, and calculating the matching degree with the rules in the de-icing strategy library; selecting one or more rules with the highest matching degree and weighting and fusing their output recommended operation parameters to obtain the final fusion parameters; simultaneously, planning the operation path based on the three-dimensional point cloud data provided by the UAV inspection module and the ice boundary determined by the ice monitoring module; and combining the fusion parameters with the operation path to generate the optimal de-icing plan containing specific operation parameters and path information.

[0050] Step S4: Control the drone to carry the detachable intelligent de-icing module to the target location; temporarily fix the intelligent de-icing module to the power transmission line through the clamping mechanism; according to the optimal de-icing scheme, start the ice-shaving mechanism and vibration auxiliary unit to perform coordinated de-icing; After the drone flies over the target icy section and accurately positions it, it controls the clamping mechanism of the intelligent de-icing module to firmly clamp a pair of V-shaped clamps onto the conductor, thus fixing the intelligent de-icing module relative to the line. Then, in step S3, the ice-cutting mechanism and the high-frequency micro-vibration device are started simultaneously to cut and break up the ice. During the operation, the drone and the intelligent de-icing module maintain electrical connection and communication, but the mechanical load is borne by the line.

[0051] Step S5: After the de-icing operation is completed, control the drone to conduct a second inspection of the operation section and evaluate the de-icing effect by comparing the data before and after the operation.

[0052] After the de-icing module is retrieved, the drone is controlled to fly again along the cleared section, repeating steps S1 and S2. By comparing the ice thickness measured this time with the thickness data before de-icing, the clearing rate is quantitatively evaluated. The evaluation results, along with the full process data of this operation, are archived to the system database for subsequent strategy library optimization and case analysis.

[0053] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the systems disclosed in the embodiments; relevant details can be found in the method section.

[0054] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0055] In the embodiments provided by this invention, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0056] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0057] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit.

[0058] Similarly, in the various embodiments of the present invention, each processing unit can be integrated into a functional module, or each processing unit can exist physically, or two or more processing units can be integrated into a functional module.

[0059] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0060] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0061] The above-disclosed embodiments are merely preferred embodiments of the present invention, but the present invention is not limited thereto. Any non-creative variations that can be conceived by those skilled in the art, as well as any improvements and modifications made without departing from the principles of the present invention, should fall within the protection scope of the present invention.

Claims

1. An integrated system for monitoring and intelligent de-icing of power transmission lines based on unmanned aerial vehicle (UAV) inspection, characterized in that, include: The drone inspection module includes an airborne sensing device that performs flight inspection missions along power transmission lines, collecting images, temperature data, and 3D point cloud data of the lines. The icing monitoring module includes a capacitive ice sensor that is placed close to or in contact with the line during the inspection process. By detecting the change in the equivalent capacitance value between the electrodes in real time, the thickness and density of the ice are calculated. The control and decision-making module cross-validates image, temperature, and 3D point cloud data, as well as ice thickness and density, to obtain ice status data, and calls the ice risk assessment model based on the ice status data to determine the risk. When a high-risk situation is identified, the optimal de-icing plan is generated based on the de-icing strategy library. The intelligent de-icing module is a detachable working mechanism mounted on the UAV inspection module. It includes a clamping mechanism, an ice-shaving mechanism, and a vibration-assisted de-icing unit. After receiving a de-icing command, it is transported by the UAV inspection module to the target location and performs fixed-point de-icing operations.

2. The system according to claim 1, characterized in that, The capacitive ice sensor in the icing monitoring module adopts a cylindrical array multi-electrode structure. By detecting the change in the equivalent capacitance value between electrodes at different spatial locations, it obtains multiple sets of measurement values ​​that have differentiated responses to ice thickness and density.

3. The system according to claim 2, characterized in that, The icing monitoring module also includes a data processing unit, which has a pre-stored capacitance-thickness-density relationship mapping model. Multiple sets of measurement values ​​are input into the capacitance-thickness-density relationship mapping model, and the thickness and density of the icing are output. The capacitance-thickness-density relationship mapping model is established based on icing sample data of different thicknesses and densities and their corresponding capacitance measurements, and constructs a mapping relationship between the input capacitance sequence and the output thickness and density estimates.

4. The system according to claim 3, characterized in that, The ice-cutting mechanism in the intelligent de-icing module includes a rotating ice-cutting wheel with multiple ice-cutting cones for mechanically cutting the ice. The clamping mechanism temporarily fixes the intelligent de-icing module to the power transmission line before the de-icing operation; The vibration-assisted de-icing unit includes a high-frequency micro-vibration device, the vibration frequency of which is adjustable in coordination with the rotational speed of the ice-cutting wheel, and is used to apply auxiliary vibration during cutting.

5. The system according to claim 4, characterized in that, The control and decision-making module includes a ground station unit and an airborne control unit; The ground station unit receives data collected by the UAV inspection module and the icing monitoring module, performs icing risk assessment, calls the de-icing strategy library to generate the optimal de-icing plan, and decomposes the plan into control commands. The airborne control unit receives control commands from the ground station unit and drives the UAV inspection module, icing monitoring module, and intelligent de-icing module to collaboratively execute the optimal de-icing solution.

6. The system according to claim 5, characterized in that, In the control and decision-making module, icing status data is obtained by cross-validating image, temperature, and 3D point cloud data, as well as icing thickness and density, including: Icing zones are defined based on image data; the ice density obtained from the icing monitoring module is corrected based on temperature data; the geometric dimensions of the ice are quantified based on 3D point cloud data, and the ice thickness calculated by the icing monitoring module is verified. Based on imagery, temperature, and 3D point cloud data, as well as cross-validation of icing thickness and density, icing status data including thickness, density, and type were obtained.

7. The system according to claim 6, characterized in that, In the control and decision-making module, the step of calling the icing risk assessment model based on icing status data to determine risk includes: The icing risk assessment model calculates the static icing load based on icing status data and calculates the wind load increment based on real-time wind speed. The calculated static icing load and wind load increment are vectorized to obtain the total equivalent load borne by the conductor. The load safety factor is calculated based on the total equivalent load and the design safety load; the load safety factor is compared with the preset risk trigger threshold; when the load safety factor is lower than the risk trigger threshold, the current state is determined to be high risk, and the de-icing response process is triggered.

8. The system according to claim 7, characterized in that, The de-icing strategy library is a knowledge base built based on expert rules and case reasoning. It is configured to receive input features including multiple factors such as ice thickness, density, type, line parameters and environmental conditions, and perform matching and calculation according to the pre-stored "condition-action" rule set to output the optimal de-icing solution.

9. The system according to claim 1, characterized in that, After the intelligent de-icing module completes its work, the control and decision-making module controls the drone inspection module to conduct a secondary inspection of the work area. By comparing the secondary inspection data with the inspection data before de-icing, the de-icing effect is evaluated, and the entire process data is archived into the icing management database.

10. A method for integrated monitoring and intelligent de-icing of power transmission lines based on unmanned aerial vehicle (UAV) inspection, characterized in that, Includes the following steps: Step S1: Using the onboard sensing device of the UAV inspection module, perform flight inspection along the power transmission line to collect images, temperature and three-dimensional point cloud data of the line. Step S2: During the inspection, the capacitive ice layer sensor of the icing monitoring module is brought close to or in contact with the line to detect the change in the equivalent capacitance value between the electrodes in real time, and the thickness and density of the ice are calculated based on the change in the equivalent capacitance value. Step S3: Using the control and decision module, cross-validate the image, temperature, and 3D point cloud data, as well as the ice thickness and density to obtain ice status data; call the ice risk assessment model to determine the risk; when the risk is determined to be high, call the de-icing strategy library to generate the optimal de-icing plan containing specific operation parameters. Step S4: Control the drone to carry the intelligent de-icing module to the target location; temporarily fix the module to the power transmission line through the clamping mechanism in the intelligent de-icing module; according to the optimal de-icing scheme, start the ice-shaving mechanism and vibration auxiliary unit to perform coordinated de-icing; Step S5: After the de-icing operation is completed, control the drone to conduct a second inspection of the operation section and evaluate the de-icing effect by comparing the data before and after the operation.

Images