A data fusion method, system, device and medium for conductor icing monitoring

Abstract

This invention discloses a data fusion method, system, device, and medium for monitoring conductor icing, belonging to the field of conductor icing detection technology. The invention constructs a system for collecting multi-source sensor data, performs a first operation to align the multi-source data in time and perform quality diagnosis, achieving spatiotemporal unification and anomaly isolation of heterogeneous data. A second operation performs feature-level fusion on the processed data to form an icing feature set including thickness and density. A third operation uses the Kriging spatial interpolation algorithm to obtain ice distribution data. Finally, a risk assessment is performed. Through these methods, this application solves the problems of single, fragmented data and lack of multi-source collaborative sensing in existing icing monitoring technologies. It achieves multi-dimensional synchronous sensing and spatial distribution visualization reconstruction of icing thickness, density, and load, improving the comprehensiveness, accuracy, and timeliness of early warning response in icing monitoring.

Description

Technical Field

[0001] This invention relates to the field of conductor icing detection technology, specifically to a data fusion method, system, equipment, and medium for conductor icing monitoring. Background Technology

[0002] In high-altitude, humid, and cold regions, power grids frequently face the natural disaster of icing on transmission lines. Excessive icing on conductors can lead to a surge in line load, excessive sag, tower collapse, and line breakage, causing serious safety accidents. Therefore, to ensure the safe performance of equipment, it is necessary to monitor conductor icing and provide timely warnings and interventions.

[0003] Existing conductor monitoring devices are mainly single-point, single-parameter monitoring devices, such as measuring only ice thickness or weight, which have poor monitoring accuracy and effectiveness. Alternatively, they may use multi-source data for monitoring, but because the sources of various monitoring data are different, they cannot be effectively aggregated and used in a unified manner, resulting in poor early warning effects for icing. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention provides a data fusion method, system, device and medium for monitoring conductor icing.

[0005] Therefore, the technical problem solved by this invention is: how to solve the problems of single and poor continuity of traditional icing monitoring data.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: Acquire multi-source sensor data, perform a first operation on the multi-source sensor data, and obtain processed multi-source sensor data; A second operation is performed on the processed multi-source sensor data to obtain the icing feature set; A third operation is performed on the ice cover feature set to obtain three-dimensional ice condition distribution data; A risk assessment is performed on the three-dimensional ice condition distribution data, and the risk assessment results are output.

[0007] As a preferred embodiment of the data fusion method for monitoring conductor icing according to the present invention, the step of performing a first operation on the multi-source sensor data to obtain processed multi-source sensor data includes: The multi-source sensor data is time-aligned to obtain time-synchronized data; Data diagnostics are performed on the time synchronization data to obtain the processed multi-source sensor data; The data diagnostic steps include: When time synchronization data passes the data diagnostics, the passed time synchronization data is output as processed multi-source sensor data. If the time synchronization data fails the data diagnostic, the time synchronization data in the data diagnostic will be marked as abnormal and isolated. By performing data diagnostics on the time-synchronized data of multi-source sensor data, the validity and continuity of the data are ensured, thereby guaranteeing the accuracy and reliability of the data source during subsequent data fusion. As a preferred embodiment of the data fusion method for monitoring conductor icing according to the present invention, the step of performing a second operation on the processed multi-source sensor data to obtain an icing feature set includes: The first, second, and third data in the processed multi-source sensor data are correlated and fused to obtain the icing feature set. The steps of the correlation fusion analysis include: A first estimated value is obtained from the first data; A second estimate is obtained using the second data; The first estimate and the second estimate are weighted and fused to obtain the ice thickness estimate. A third estimate is obtained by using third-party data; Based on the estimated ice thickness and the third estimated value, the estimated ice density is obtained; The ice thickness estimate and ice density estimate are combined into an ice feature set.

[0008] By calculating and analyzing multi-source sensor data, the estimated values ​​of ice thickness and ice density are obtained, and they are combined into an ice feature set, which provides a data foundation for subsequent analysis. As a preferred embodiment of the data fusion method for monitoring conductor icing according to the present invention, the step of obtaining a first estimated value through first data includes: Edge detection is performed on the first data to obtain the ice-covered boundary; Multiply the pixel width of the ice-covered boundary by the cell size of the ice-covered boundary to obtain the median value; The quotient of the median value and the optical magnification is used as the first estimate; The steps to obtain the second estimate from the second data include: Obtain the feature values ​​from the second data; The eigenvalues ​​are subjected to vector calculation to obtain the resultant force direction vector, and the second estimated value is obtained by solving based on the resultant force direction vector. The steps to obtain a third estimate using third-party data include: Obtain the net weight of ice from the third data; By calibrating the icing net weight in the third data, a stable icing net weight is obtained as the third estimated value.

[0009] The first, second, and third estimates are obtained by calculating the first, second, and third estimates respectively, thus completing the transformation from the original sensor data to the preliminary estimate. As a preferred embodiment of the data fusion method for monitoring conductor icing according to the present invention, the step of performing a third operation on the icing feature set to obtain three-dimensional ice condition distribution data includes: Obtain the ice accretion feature set corresponding to multiple monitoring points that are discretely distributed in space; By performing a first algorithm to calculate the spatial locations of the multiple monitoring points and their corresponding icing feature sets, three-dimensional ice condition distribution data is obtained.

[0010] By performing the first algorithm calculation on the ice cover feature set, three-dimensional ice condition distribution data is obtained, and three-dimensional construction is realized, which facilitates subsequent risk assessment. As a preferred embodiment of the data fusion method for monitoring conductor icing according to the present invention, the first algorithm step includes: Obtain the spatial location of the monitoring points and their corresponding estimated ice thickness; The empirical semivariogram value is calculated based on the sum of squares of the differences between the spatial distances between the monitoring points and the estimated ice thickness values. The empirical semivariogram values ​​are fitted to a spherical model; Based on the spherical model, calculate the first half-variation value between any two monitoring points, and the second half-variation value between each monitoring point and the point to be estimated in space; Construct a coefficient matrix based on the first half-variable value, construct a constant term vector based on the second half-variable value, and introduce Lagrange multipliers to establish the Kriging equation system. Solve the Kriging equations to obtain the weights corresponding to each known monitoring point; The estimated ice thickness values ​​of each known monitoring point are weighted and summed according to the weights to obtain the predicted ice thickness value of the point to be estimated. By calculating multiple predicted ice thickness values, a load vector is obtained, which serves as the three-dimensional ice condition distribution data.

[0011] By calculating the semi-variogram value of the estimated ice thickness and constructing the Kriging equation system, the three-dimensional ice condition distribution can be obtained.

[0012] As a preferred embodiment of the data fusion method for monitoring conductor icing according to the present invention, the step of performing a risk assessment on the three-dimensional ice distribution data and outputting the risk assessment result includes: Based on the three-dimensional ice condition distribution data, the first risk assessment parameter is set; Verify whether the second estimated value exceeds the first risk assessment parameter. If it does, trigger an early warning; otherwise, perform a secondary assessment. A second risk assessment parameter is set based on the aforementioned three-dimensional ice condition distribution data; Verify whether the estimated ice thickness exceeds the second risk assessment parameter. If it exceeds the second risk assessment parameter, trigger an early warning; otherwise, perform a level 3 assessment. The third risk assessment parameter is set based on the three-dimensional ice condition distribution data; The growth rate of the third estimated value is obtained through the three-dimensional ice condition distribution data. The growth rate of the third estimated value is verified to see if it exceeds the third risk assessment parameter. If it exceeds the third risk assessment parameter, an early warning is triggered; otherwise, a qualified risk assessment result is output.

[0013] By conducting a three-level risk assessment and early warning system, it is possible to detect the ice conditions of conductors from multiple sources and with relatively high accuracy.

[0014] Secondly, the present invention provides a data fusion system for monitoring conductor icing, comprising: The sensor acquisition module is used to acquire multi-source sensor data; The data processing module is used to perform a first operation on the multi-source sensor data to obtain processed multi-source sensor data; and to perform a second operation on the processed multi-source sensor data to obtain an icing feature set. The ice condition reconstruction module is used to perform a third operation on the ice cover feature set to obtain three-dimensional ice condition distribution data. The risk assessment module is used to assess the risks of the three-dimensional ice condition distribution data and output the risk assessment results.

[0015] Thirdly, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the processor executes the computer program, it implements the steps of the data fusion method for monitoring conductor icing.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the data fusion method for monitoring conductor icing.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention collects multi-source sensor data and performs a first operation to align and diagnose the quality of the multi-source data, marking and isolating abnormal data and synchronizing normal multi-source data. Then, a second operation is performed to fuse the synchronized multi-source sensor data at the feature level to form an icing feature set. Next, a third operation is performed to obtain ice distribution data through the Kriging space interpolation algorithm. Finally, a risk assessment is performed to complete the early warning. This application solves the problems of single data and ineffective collaborative work of multi-source data in existing icing monitoring technologies by means of the above methods, realizing multi-source monitoring of icing thickness, density, and load, thereby improving the accuracy of conductor icing monitoring and the timeliness of early warning response. Attached Figure Description

[0018] 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.

[0019] Figure 1 The above is a general flowchart of a data fusion method for monitoring conductor icing, provided as an embodiment of the present invention.

[0020] Figure 2 This is a judgment diagram of the output risk assessment result of a data fusion method for monitoring conductor icing, provided in one embodiment of the present invention.

[0021] Figure 3 The overall timing diagram is provided for a data fusion method for monitoring conductor icing according to an embodiment of the present invention. Detailed Implementation

[0022] 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.

[0023] Example 1, referring to Figures 1-3 This is one embodiment of the present invention, which provides a data fusion method for monitoring conductor icing, such as... Figure 1 The overall flowchart of the present invention includes the following steps S1 to S4: S1. Acquire multi-source sensor data, perform a first operation on the multi-source sensor data, and obtain processed multi-source sensor data. When acquiring multi-source sensor data, the sources of multi-source sensor data include anti-freezing camera devices, anti-icing vector gravity sensors, and simulated conductor icing weight monitoring devices.

[0024] Specifically, an "L"-shaped steel ice-observing frame was deployed on a mountain. The frame is 10 meters high, with a long side span of 10 meters and a short side span of 5 meters. It has two levels with a total of four observation corridors. At the four key observation points in the east, west, south, and north of the two corridors, as well as at the top of the two main support columns, a total of six anti-freezing camera devices were installed. Sixteen anti-icing vector gravity sensors were installed at the suspension positions of the eight standard simulated guide wires in the two observation corridors. Twelve simulated guide wire icing weight monitoring devices were connected in series to the simulated guide wires. Through the above structure, image data, gravity, tilt angle, and roll angle data of the guide wires, as well as the total weight of the guide wires covered with ice, were collected.

[0025] In this embodiment, the collected gravity is Pitch angle Roll angle Total weight of ice covering the conductor .

[0026] The steps of performing a first operation on the multi-source sensor data to obtain processed multi-source sensor data include S1.1 to S1.2: S1.1 Time alignment of multi-source sensor data to obtain time-synchronized data.

[0027] Specifically, the system receives raw data packets with timestamps, and then performs time alignment based on the clock source. Minor time deviations caused by network latency or asynchronous acquisition cycles are corrected using interpolation.

[0028] Specifically, taking the collected total weight data of icing on the conductor as an example, the system receives the raw weight data packet {t} carrying the local timestamp. raw =10:00:00.023, W raw =15.470kg}, in response to clock source T ref Given a reference signal of 10:00:00.000, calculate the deviation δ of this data packet relative to the reference time. t =+23ms; To address this minute time deviation, the system extracts the time of the weight sensor at T ref For two consecutive valid sampling points {t1=09:59:59.990, W1=15.465kg} and {t2=10:00:00.010, W2=15.475kg}, a linear interpolation formula is used. By performing mapping and conversion, the theoretical weight value W aligned to the reference time is calculated. raw=15.470kg; Subsequently, three quality checks were performed on the aligned data: range validity, continuity of change, and noise threshold. When the checks passed, the time, value, and quality identifier were encapsulated into a standardized data unit. Therefore, the time synchronization data is represented as (10:00:00.000, 15.470kg). S1.2 Perform data diagnostics on the time synchronization data to obtain processed multi-source sensor data.

[0029] The data diagnostic steps include S1.21~S1.22: S1.21 When the time synchronization data passes the data diagnostic, the passed time synchronization data will be output as processed multi-source sensor data.

[0030] Specifically, since the acquisition frequencies of multi-source sensor data will be different, with image data at 1 frame / second, weight data at 0.1~1Hz, and mechanical data at 10Hz, and there is network delay during the transmission of each data, the clock source needs to ensure that the deviation is less than 1ms.

[0031] Specifically, since the time synchronization data is 10:00:00.000, the corresponding timestamp is 10:00:00.000, and the local clock is 10:00:00.0008. The calculated deviation is |10:00:00.0008-10:00:00.000|=0.8ms, which is less than the set 1ms. Therefore, the time synchronization data is determined to pass the data diagnostic.

[0032] S1.22. When the time synchronization data fails the data diagnostic, the time synchronization data in the data diagnostic will be marked as abnormal and isolated.

[0033] Specifically, when performing data diagnostics on multi-source sensor data, data with a timestamp deviation from the local clock greater than or equal to 1ms are marked as abnormal and isolated.

[0034] Data diagnostics check whether the data range is valid, whether the changes are continuous, and whether the signal noise exceeds the standard. Invalid or abnormal data is marked and isolated and not added to subsequent fusion.

[0035] S2. Perform a second operation on the processed multi-source sensor data to obtain the icing feature set; The steps to obtain the icing feature set include S2.1~S2.4: S2.1 Perform correlation and fusion analysis on the first, second, and third data in the processed multi-source sensor data; The steps of the correlation fusion analysis include A1 to A4: A1. Obtain the first estimated value using the first data, specifically through steps A1.1 to A1.3: A1.1 Perform edge detection on the first data to obtain the ice-covered boundary.

[0036] Specifically, the original image data is first converted to grayscale and adaptively enhanced for contrast. By suppressing low-light environmental noise, enhanced image data is obtained. Then, the Canny operator is used to calculate the multi-directional gradient magnitude of the enhanced image data to extract the image data of abrupt regions where the pixel intensity change rate exceeds the preset gradient. After non-maximum suppression and double threshold hysteresis connection, a coherent icing edge chain is obtained. Finally, the edge chain is checked for contour closure, and the pixel coordinate sequence that completely covers the outer contour of the conductor is extracted as the icing boundary. In this embodiment, the pixel width of the icing boundary identified in a certain observation segment is 15 pixels.

[0037] A1.2 Multiply the pixel width of the ice-covered boundary by the pixel size of the ice-covered boundary to obtain the intermediate value.

[0038] Specifically, the pixel width of the icing contour image identified by the algorithm for a certain observation section of the traverse. It's 15 pixels, the camera's pixel size. The value is 3.75 μm. The median value is obtained by multiplying the pixel width of the ice-covered boundary by the pixel size. .

[0039] A1.3. The quotient of the median value and the optical magnification is taken as the first estimate.

[0040] Specifically, optical magnification The value is 0.02, which is a preliminary estimate of the ice thickness. for: .

[0041] A2. Obtain the second estimated value using the second data. Specific steps include A2.1 to A2.2. A2.1 Obtain the feature values ​​from the second set of data; Specifically, a second set of data is collected using an anti-icing vector gravity sensor. This second set of data includes the icing load, and its characteristic values ​​include the gravity of the icing load. Pitch angle and roll angle .

[0042] A2.2 Perform vector calculations on the eigenvalues ​​to obtain the resultant force direction vector, and then calculate the second estimated value based on the resultant force direction vector. Specifically, the direction vector of the three-dimensional resultant force of the icing load. : The x-axis represents the direction along the conductor, the y-axis represents the horizontal direction perpendicular to the conductor, and the z-axis represents the vertical direction. The results indicate that the icing load in the vertical direction is 245.3 N of gravity, while the force in the horizontal direction perpendicular to the conductor is 10.70 N, suggesting the possible presence of slight uneven icing or wind pressure.

[0043] A3. Weighted fusion of the first estimate and the second estimate yields the estimated ice thickness. Specifically, a preliminary estimate of the ice thickness is needed. Ice thickness calculated based on weight Assuming the ice layer is a uniform cylinder and the wire diameter is... Observation segment length Estimate ice density Take the standard rime density as 300 kg / m³ 3 ,but Substituting the numerical values, we get The system, based on the current high image clarity and stable weight data, indicates a high signal-to-noise ratio and low variance after filtering, and assigns weights accordingly. , The optimal ice thickness was obtained. for: .

[0044] A4. Obtain the third estimate through the third data, and the specific steps include A4.1~A4.2; A4.1 Obtain the net weight of ice covering from the third data; Specifically, a third set of data is collected using a simulated conductor icing weight monitoring device. This third set of data includes the total weight of the conductor icing and the initial weight of the conductor. It is 15.2 kg. In this embodiment, the total weight of the conductor covered with ice is 15.47 kg obtained in step S1. The initial weight of the conductor in the third data is 15.2 kg. Therefore, when obtaining the net weight of ice, the value of 15.47 minus 15.2 kg, i.e. 0.27, is calculated as the net weight of ice.

[0045] A4.2 By calibrating the net weight of ice in the third data, a stable net weight of ice is obtained as the third estimate; When calibrating the net weight of iced icing, the weight monitoring data is compensated for by temperature drift and digitally filtered. Kalman filtering can be used for digital filtering to obtain a stable net weight of iced icing ΔW(t). .

[0046] S2.3. Based on the estimated ice thickness and the third estimated value, the estimated ice density is obtained; Calculate the average icing density based on the estimated icing thickness and the third estimate. : The density of rime ice ranges from 200-400 kg / m³. 3 The average ice density obtained Located within the range of rime density, the image shows that the ice is milky white and opaque, which is consistent with the characteristics of rime. Therefore, the type of ice accumulation can be determined to be rime.

[0047] S2.4 Combine the ice thickness estimate and ice density estimate into an ice feature set.

[0048] The aforementioned estimated ice thickness and ice density estimates The combination forms an icing feature set.

[0049] S3. Perform a third operation on the ice cover feature set to obtain three-dimensional ice condition distribution data; The steps to obtain three-dimensional ice condition distribution data include S3.1-S3.2: S3.1 Obtain the ice accretion feature set corresponding to multiple monitoring points that are discretely distributed in space; The ice accretion feature set of several monitoring points is statistically analyzed, and in addition to the ice accretion feature set, the spatial three-dimensional coordinates (x, y, z) of each monitoring point are also statistically analyzed.

[0050] Specifically, four monitoring points are first selected. Based on the spatial coordinates of the four monitoring points and the optimal estimated ice thickness parameters calculated by the second operation, the specific parameters are as follows: The spatial coordinates of monitoring point P1 are (2.0, 3.0, 10.0), with an ice thickness of 3.1 mm; the spatial coordinates of monitoring point P2 are (5.0, 3.0, 10.0), with an ice thickness of 3.3 mm; the spatial coordinates of monitoring point P3 are (2.0, 0.0, 10.0), with an ice thickness of 2.9 mm; and the spatial coordinates of monitoring point P4 are (5.0, 0.0, 10.0), with an ice thickness of 3.0 mm. The spatial coordinates and ice thickness of these discrete points are used as a sample set in the subsequent Kriging interpolation.

[0051] S3.2. By performing the first algorithm to calculate the spatial location of multiple monitoring points and their corresponding ice cover feature sets, three-dimensional ice condition distribution data is obtained.

[0052] The first algorithm step includes S3.2.1-S3.2.8: S3.2.1 Obtain the spatial location of the monitoring point and the corresponding estimated ice thickness; The three-dimensional coordinates and thickness values ​​of each monitoring point are extracted from the sample set obtained from S3.1 and used as the basis data for Kriging interpolation.

[0053] Specifically, the system reads the coordinates and thickness values ​​of P1 to P4: P1 (2, 3, 10, 3.1 mm), P2 (5, 3, 10, 3.3 mm), P3 (2, 0, 10, 2.9 mm), and P4 (5, 0, 10, 3.0 mm). The data is stored in memory as an array for subsequent calculation of distance and semi-variance values. S3.2.2 Calculate the empirical semivariogram value based on the sum of squares of the differences between the spatial distances between monitoring points and the estimated ice thickness; The semivariogram describes a space where the distance is... The degree of difference in attribute values ​​between two points is calculated using Euclidean distance. For ice thickness... Define the lag distance vector The empirical semivariogram is: ; in The distance vector is The number of all point pairs. In practical applications, isotropy is usually assumed, and only distance is considered. And calculate multiple hysteresis distances .

[0054] Specifically, calculate the distance h between P1 and P2. 12 =3.0m, thickness difference: -0.2mm, squared value: 0.04mm 2 Calculate the distance h between P3 and P4. 34 =3.0m, thickness difference: -0.1mm, squared value: 0.01mm 2 There are 2 pairs of points with a distance of 3.0m, so N(3) = 2N(3) = 2. The empirical half-variation value is: γ(3) = 0.0125mm. 2 Then, all the semivariogram values ​​are calculated sequentially.

[0055] S3.2.3 Fit the empirical semivariogram values ​​to a spherical model; The discrete empirical semivariogram values ​​are fitted to a continuous theoretical model. In this embodiment, a spherical model is used because it is suitable for parameters such as icing thickness that have spatially varying characteristics. In other embodiments, other models can also be used for fitting.

[0056] ; in: The nugget constant represents the measurement error or microscopic variability; The values ​​are partial sill values, reflecting spatially correlated variability; The sill value represents the total variation; For variable ranges, spatial autocorrelation disappears beyond this distance. The fitting method is to use least squares or weighted least squares to minimize the sum of squared errors between the theoretical semivariogram and the empirical value.

[0057] Specifically, based on the empirical points calculated in step S3.2.2 (h=3m, γ=0.0125; h=4m, γ=0.018; h=5m, γ=0.022; h=6m, γ=0.025; h=7m, γ=0.027), the spherical model parameter C0=0.02mm was fitted. 2 C=0.10mm 2， When a=8.0m, the theoretical value is 0.0736 when h=3m, but the actual empirical value is smaller.

[0058] S3.2.4. Based on the spherical model, calculate the first half-variable value between any two monitoring points, and the second half-variable value between each monitoring point and the point to be estimated in space. First semi-variance value γ ij According to the spherical model, the distance h between monitoring point i and monitoring point j is... ij The calculated second half-variable value γ i0 Based on the spherical model, the distance h between monitoring point i and the point to be estimated x0 is... i0 Calculated.

[0059] Specifically, suppose the point to be estimated, x0, is located at (3.5, 1.5, 10.0), calculate the distance h between P1 and the point to be estimated. 10 =2.121m, substituting into the spherical model: γ 10 =0.0588 Similarly, calculate γ for P2, P3, P4 and the point to be estimated. 20 γ 30 and γ 40 Next, calculate the first half-variable between monitoring points: for example, if the distance between P1 and P2 is 3.0m, γ 12 =0.0736.

[0060] S3.2.5 Construct a coefficient matrix based on the first half of the variation value, construct a constant term vector based on the second half of the variation value, and introduce Lagrange multipliers to establish the Kriging equation system; For the point to be estimated Let its adjacent The weights of the known sample points are: The ordinary Kriging equations can be written in matrix form: These are Lagrange multipliers used to ensure unbiasedness constraints. .

[0061] Specifically, for 4 monitoring points, the coefficient matrix is ​​5×5. When n=4, the Lagrange multipliers are added. After that, the system of equations has a total of +1 = 5 unknowns. Substitute the values ​​calculated in step S3.2.4 into the above system of equations.

[0062] S3.2.6 Solve the Kriging equations to obtain the weights corresponding to each known monitoring point; Solving the above system of linear equations involves using Gaussian elimination or Cholesky decomposition to obtain the weights. The sum of the weights satisfies ∑λi=1.

[0063] Solving the above system of equations yields: λ1=0.35, λ2=0.25, λ3=0.25, λ4=0.15, μ=-0.02. Verification shows that the sum of 0.35+0.25+0.25+0.15=1.0, which satisfies the unbiased condition. S3.2.7. The estimated ice thickness values ​​of each known monitoring point are weighted and summed to obtain the predicted ice thickness value of the point to be estimated. The predicted ice thickness at the point to be estimated is as follows: ; Specifically, the thickness of the point to be estimated is calculated as follows: ; S3.2.8. By calculating multiple predicted ice thickness values, the load vector is obtained as three-dimensional ice condition distribution data.

[0064] Ice load vector It has size and direction, The three components are spatially correlated. Co-kriging can also be used to perform ordinary kriging interpolation on each component separately. In this embodiment, we use separate interpolation on each component. Kriging interpolation was performed separately on the three scalar fields, and then the results were combined into a vector diagram. For example, repeat steps S3.2.1-S3.2.7 above for each component to obtain any point. Predicted value at location The final load vector at that point is: ; After obtaining the interpolation results of all nodes on the regular grid within the three-dimensional space of the entire ice shelf, contour maps and vector distribution maps can be generated.

[0065] Ice thickness contour maps are used to map a fixed layer at a certain height. Using the predicted thickness values ​​within the plane, contour lines are extracted using the Marching Squares algorithm, and the thickness is represented by a color map. Marching Squares is a graphical algorithm for extracting contour lines from two-dimensional data, also known as the moving squares algorithm or the two-dimensional contour line extraction algorithm.

[0066] A load distribution vector diagram is created by drawing arrows on the same grid points, with the length of the arrow representing the magnitude of the resultant force. The direction of the arrow is from Decide.

[0067] S4. Conduct a risk assessment on the three-dimensional ice condition distribution data, output the risk assessment results, and refer to... Figure 2 The specific steps include S4.1-S4.6: S4.1. Based on the three-dimensional ice condition distribution data, set the first risk assessment parameters; Specifically, the system sets a first risk assessment parameter R1 based on the line design parameters and operating specifications. The first risk assessment parameter corresponds to the safety threshold of the component of the icing load perpendicular to the conductor. In this embodiment, 70% of the conductor torsional design threshold is taken as the first risk assessment parameter. S4.2 Verify whether the second estimated value exceeds the first risk assessment parameter. If it exceeds the first risk assessment parameter, trigger an early warning; otherwise, perform a secondary assessment. The system reads the second estimated value of each monitoring point from the three-dimensional ice condition distribution data and compares it with the first risk assessment parameter. If the second estimated value of any monitoring point exceeds the first risk assessment parameter, a first-level warning is immediately triggered and a warning message is output; if the second estimated values ​​of all monitoring points do not exceed the first risk assessment parameter, the system proceeds to a second-level assessment.

[0068] The specific conductor torsional design threshold is 600 N·m, and 70% of this is taken as the first risk assessment parameter, i.e., R1 = 0.7 × 600 = 420 N. The system extracts the second estimated value, F, from the three-dimensional ice condition distribution data for a monitoring point on the northern corridor. N, because If the value is less than 420, the first risk assessment parameter is not exceeded, so the system will not trigger a first-level warning and will continue to perform the second-level assessment.

[0069] S4.3. Based on the three-dimensional ice condition distribution data, set the second risk assessment parameters; Based on the designed ice thickness of the line and operational experience, the system sets a second risk assessment parameter R2. In this embodiment, 60% of the designed ice thickness of the line is taken as the second risk assessment parameter. S4.4 Verify whether the estimated ice thickness exceeds the second risk assessment parameter. If it exceeds the second risk assessment parameter, trigger an early warning; otherwise, perform a level 3 assessment. The system reads the estimated ice thickness of each monitoring point and interpolation node from the three-dimensional ice distribution data, compares it with the second risk assessment parameter. If the estimated ice thickness at any location exceeds the second risk assessment parameter, a level-two warning is triggered and the warning information is output. If the thickness at all locations does not exceed the second risk assessment parameter, the system proceeds to level-three assessment.

[0070] The local line is designed for an ice thickness of 15mm. Taking 60% as the second risk assessment parameter, i.e., R² = 0.6 × 15 = 9mm. The system extracts the predicted ice thickness at the center point of the upper corridor from the three-dimensional ice distribution data, which is 3.085mm. Furthermore, the thickness at all monitoring points is between 2.9 and 3.3mm, all significantly less than 9mm. Therefore, the second risk assessment parameter is not exceeded, and the system does not trigger a level-two warning, continuing with the level-three assessment.

[0071] S4.5. Based on the three-dimensional ice condition distribution data, set the third risk assessment parameter; Based on historical icing event statistics, a third risk assessment parameter R3 is set. In this embodiment, the extreme value of the same historical period is taken as the third risk assessment parameter, which is 80% of the maximum icing weight growth rate within the same temperature range over the past 10 years. S4.6 Obtain the growth rate of the third estimated value through the three-dimensional ice condition distribution data, and verify whether the growth rate of the third estimated value exceeds the third risk assessment parameter. If it exceeds the third risk assessment parameter, trigger an early warning; otherwise, output a qualified risk assessment result.

[0072] The system reads the growth rate of the third estimate from the three-dimensional ice condition distribution data, that is, the change of stable net icing weight over time. If the current growth rate exceeds the third risk assessment parameter, a level three warning will be triggered and a warning message will be output; if it does not exceed the parameter, the risk assessment result will be output as qualified.

[0073] Specifically, under conditions of -5℃ and 95% humidity, the maximum growth rate of icing weight is 0.5 kg / h. Taking 80% as the third risk assessment parameter, i.e., R3 = 0.8 × 0.5 = 0.4 kg / h, the system reads the net icing weight of a monitoring point in the most recent hour: initial ΔW = 0.27 kg, 1 hour later ΔW = 0.52 kg, growth rate = (0.52 - 0.27) / 1 = 0.25 kg / h. Since 0.25 < 0.4, it does not exceed the third risk assessment parameter, the system determines that the current icing growth rate is within a safe range, and outputs a qualified risk assessment result.

[0074] Figure 3 This is a sequence diagram of the overall process. All the contents have been described in detail in the above embodiments and will not be repeated here.

[0075] Example 2 is an embodiment of the present invention, which provides a data fusion system for monitoring conductor icing based on the previous embodiment, including a sensor acquisition module for acquiring multi-source sensor data; The data processing module is used to perform a first operation on the multi-source sensor data to obtain processed multi-source sensor data; and to perform a second operation on the processed multi-source sensor data to obtain an icing feature set. The ice condition reconstruction module is used to perform a third operation on the ice cover feature set to obtain three-dimensional ice condition distribution data. The risk assessment module is used to assess the risks of the three-dimensional ice condition distribution data and output the risk assessment results.

[0076] This embodiment also provides a computer device applicable to a data fusion method for monitoring conductor icing, 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 implement the steps of the data fusion method for monitoring conductor icing as proposed in the above embodiment.

[0077] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the steps of a data fusion method for monitoring conductor icing as described in the above embodiments.

[0078] The storage medium proposed in this embodiment and the data fusion method for monitoring conductor icing 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.

[0079] 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.

[0080] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the scope of protection of the present invention. Although the above embodiments have described the present invention in detail, those skilled in the art should understand and be able to modify or make equivalent substitutions to the technical solutions of the present invention. As long as they do not depart from the spirit and scope of the technical solutions of the present invention, they are all covered within the scope of the claims of the present invention.

Claims

1. A data fusion method for monitoring conductor icing, characterized in that, include: Acquire multi-source sensor data, perform a first operation on the multi-source sensor data, and obtain processed multi-source sensor data; A second operation is performed on the processed multi-source sensor data to obtain the icing feature set; A third operation is performed on the ice cover feature set to obtain three-dimensional ice condition distribution data; A risk assessment is performed on the three-dimensional ice condition distribution data, and the risk assessment results are output.

2. The data fusion method for monitoring conductor icing as described in claim 1, characterized in that, The steps of performing a first operation on the multi-source sensor data to obtain processed multi-source sensor data include: The multi-source sensor data is time-aligned to obtain time-synchronized data; Data diagnostics are performed on the time synchronization data to obtain the processed multi-source sensor data; The data diagnostic steps include: When time synchronization data passes the data diagnostics, the passed time synchronization data is output as processed multi-source sensor data. If the time synchronization data fails the data diagnostic, the time synchronization data in the data diagnostic will be marked as abnormal and isolated.

3. The data fusion method for monitoring conductor icing as described in claim 1, characterized in that, The steps for performing a second operation on the processed multi-source sensor data to obtain the icing feature set include: The first, second, and third data in the processed multi-source sensor data are correlated and fused to obtain the icing feature set. The steps of the correlation fusion analysis include: A first estimated value is obtained from the first data; A second estimate is obtained using the second data; The first estimate and the second estimate are weighted and fused to obtain the ice thickness estimate. A third estimate is obtained by using the third data, and an ice density estimate is obtained based on the ice thickness estimate and the third estimate. The ice thickness estimate and ice density estimate are combined into an ice feature set.

4. The data fusion method for monitoring conductor icing as described in claim 3, characterized in that, The steps for obtaining the first estimate from the first data include: Edge detection is performed on the first data to obtain the ice-covered boundary; Multiply the pixel width of the ice-covered boundary by the cell size of the ice-covered boundary to obtain the median value; The quotient of the median value and the optical magnification is used as the first estimate; The steps to obtain the second estimate from the second data include: Obtain the feature values ​​from the second data; The eigenvalues ​​are subjected to vector calculation to obtain the resultant force direction vector, and the second estimated value is obtained by solving based on the resultant force direction vector. The steps to obtain a third estimate using third-party data include: Obtain the net weight of ice from the third data; By calibrating the icing net weight in the third data, a stable icing net weight is obtained as the third estimated value.

5. The data fusion method for monitoring conductor icing as described in claim 1, characterized in that, The steps for performing a third operation on the icing feature set to obtain three-dimensional ice condition distribution data include: Obtain the ice accretion feature set corresponding to multiple monitoring points that are discretely distributed in space; By performing a first algorithm to calculate the spatial locations of the multiple monitoring points and their corresponding icing feature sets, three-dimensional ice condition distribution data is obtained.

6. The data fusion method for monitoring conductor icing as described in claim 5, characterized in that, in, The first algorithm steps include: Obtain the spatial location of the monitoring points and their corresponding estimated ice thickness; The empirical semivariogram value is calculated based on the sum of squares of the differences between the spatial distances between the monitoring points and the estimated ice thickness values. The empirical semivariogram values ​​are fitted to a spherical model; Based on the spherical model, calculate the first half-variation value between any two monitoring points, and the second half-variation value between each monitoring point and the point to be estimated in space; Construct a coefficient matrix based on the first half-variable value, construct a constant term vector based on the second half-variable value, and introduce Lagrange multipliers to establish the Kriging equation system. Solve the Kriging equations to obtain the weights corresponding to each known monitoring point; The estimated ice thickness values ​​of each known monitoring point are weighted and summed according to the weights to obtain the predicted ice thickness value of the point to be estimated. By calculating multiple predicted ice thickness values, a load vector is obtained, which serves as the three-dimensional ice condition distribution data.

7. The data fusion method for monitoring conductor icing as described in claim 3, characterized in that, The steps for conducting a risk assessment on the three-dimensional ice condition distribution data and outputting the risk assessment results include: Based on the three-dimensional ice condition distribution data, the first risk assessment parameter is set; Verify whether the second estimated value exceeds the first risk assessment parameter. If it does, trigger an early warning; otherwise, perform a secondary assessment. A second risk assessment parameter is set based on the aforementioned three-dimensional ice condition distribution data; Verify whether the estimated ice thickness exceeds the second risk assessment parameter. If it exceeds the second risk assessment parameter, trigger an early warning; otherwise, perform a level 3 assessment. The third risk assessment parameter is set based on the three-dimensional ice condition distribution data; The growth rate of the third estimated value is obtained through the three-dimensional ice condition distribution data. It is then verified whether the growth rate of the third estimated value exceeds the third risk assessment parameter. If it exceeds the third risk assessment parameter, an early warning is triggered; otherwise, a qualified risk assessment result is output.

8. A system for data fusion in conductor icing monitoring, employing the data fusion method for conductor icing monitoring as described in any one of claims 1 to 7, characterized in that, include: The sensor acquisition module is used to acquire multi-source sensor data; The data processing module is used to perform a first operation on the multi-source sensor data to obtain processed multi-source sensor data. A second operation is performed on the processed multi-source sensor data to obtain the icing feature set. The ice condition reconstruction module is used to perform a third operation on the ice cover feature set to obtain three-dimensional ice condition distribution data. The risk assessment module is used to assess the risks of the three-dimensional ice condition distribution data and output the risk assessment results.

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 method for monitoring conductor icing as described in 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 method for monitoring conductor icing as described in any one of claims 1 to 7.

Images