Power transmission line windproof reinforcement construction operation state evaluation method

Abstract

The invention provides a power transmission line windproof reinforcement construction operation state evaluation method, and relates to the technical field of power state evaluation, and the method comprises the steps: analyzing an exit distance value, marking an area lower than a safety lower limit as a stress superposition risk area, and marking an area in a safety range as a normal adjustment area; recognizing a deflection angle abnormal section according to the angle sudden change cluster and the gradient sudden increase cluster, fusing the contact pressure intensity of the clamping section to determine a position boundary value, and supplementing and perfecting a fatigue damage potential recognition group; performing grading processing on the damage quantized value according to the zero damage mark and a preset damage degree grading threshold value to obtain a wire tightening operation damage degree grading result; and judging a risk level according to a line tightening operation damage degree grading result, identifying a triggering position of a high-risk section, updating the initial clamping position data set, and outputting a final damage assessment report. The precision and efficiency of lead fatigue damage monitoring are effectively improved, and reliable technical support is provided for safe operation of an electric power circuit.

Description

Technical Field

[0001] This invention relates to the field of power condition assessment technology, and in particular to a method for assessing the condition of power transmission line windproof reinforcement construction operations. Background Technology

[0002] Transmission lines are prone to galloping and vibration in strong winds, leading to conductor fatigue, strand breakage, and even tower collapse. Wind-resistant reinforcement has become a crucial measure to ensure the safe operation of the power grid. Among these measures, tensioning is the most direct link in the reinforcement process affecting the conductor's stress state, and its quality directly determines the reinforcement effect and the long-term reliability of the line. Currently, the commonly used experience-based positioning method in tensioning mainly relies on the distance between the clamping position and the tension clamp outlet to judge the adjustment sensitivity, assuming that the closer the position is to the outlet, the faster the sag adjustment response.

[0003] However, this judgment ignores the stiffness variation characteristics of the conductor at the exit section of the tension clamp. For example, the existing patent, "Overhead Transmission Line Tensioning Construction Design Method, Device, Terminal and Storage Medium," publication number CN113594966B, discloses a tensioning construction design method based on parameter data to calculate the maximum sag of the conductor and consider the influence of the tension insulator string. This method neglects the stiffness variation characteristics of the conductor at the exit section of the tension clamp, leading to problems of stress superposition and strain concentration caused by improper clamping position selection. There is a transitional region where the stiffness of the conductor gradually decreases from the clamp exit to the free section. When the clamping position is too close to the exit, the local clamping force generated by the clamp will be superimposed on the constraint force of the clamp itself in the same section, causing the conductor to undergo a sharp change in bending angle at this point, resulting in a concentrated area of ​​sharply increased strain on the aluminum strand surface. This strain concentration is far more likely to cause microcrack propagation than uniform bending at a slightly farther position, thus accelerating bending fatigue damage to the conductor.

[0004] In actual wire tensioning, construction workers face a difficult contradiction: to make sag adjustment more direct and effective, the tensioning clamp must be placed as close as possible to the tension clamp exit; however, if the position is too close, the stress superposition effect caused by the transition in stiffness of the exit section will be significantly amplified, leading to a sharp increase in the degree of local bending of the conductor, and the strain distribution on the aluminum strand surface will change from uniform to a steep gradient, thus significantly increasing the risk of fatigue damage. This contradictory phenomenon of "the closer, the more direct the adjustment, but the closer, the easier the damage" makes it impossible to accurately determine on-site which specific position can both meet the sag adjustment requirements and keep conductor damage within an acceptable range. For example, when the construction workers set the tensioning clamp only 20 centimeters from the exit, although the sag adjustment response is fast, the bending angle of the conductor in the exit section suddenly increases to nearly 30 degrees within a few centimeters, and the strain value of the outer layer of the aluminum strand rises rapidly from 0.15% in the normal area to more than 0.8%, forming a clear strain surge zone. Under the repeated action of this local high strain, early strand breakage is very likely to occur.

[0005] Therefore, how to simultaneously consider the response speed of sag adjustment and the control of local bending fatigue damage of the conductor in the exit section during the tensioning operation, and avoid stress superposition and strain concentration caused by improper selection of clamping position, has become a key issue in the quality control of current windproof reinforcement construction. Summary of the Invention

[0006] This invention provides a method for assessing the operational status of wind-resistant reinforcement construction of power transmission lines, including: Deploy a clamping force acquisition module and a strain probe in the sag adjustment monitoring device to obtain the distance from the clamping point to the outlet of the tension clamp, the contact pressure of the clamping section, the deflection angle of the conductor and the strain gradient value of the aluminum strand surface, and establish an initial clamping position dataset. The distance between the clamping point and the outlet of the tension clamp is analyzed. Areas below the preset safety lower limit are marked as stress superposition risk areas, and areas within the preset safety range are marked as normal adjustment areas. The deflection angle and strain gradient value are extracted from the stress superposition risk zone, and grouped by k-means clustering algorithm to obtain angle mutation cluster and gradient steep increase cluster. Collection points exceeding the preset threshold are selected from the angle mutation cluster and the gradient steep increase cluster respectively to construct fatigue damage potential identification group. Based on the angle mutation cluster and the gradient steep increase cluster, identify the abnormal deflection angle segment, fuse the contact pressure of the clamping segment to determine the position boundary value, and supplement the fatigue damage potential identification group; Analyze the strain gradient value change trend of the fatigue damage potential identification group, divide the segment type according to the preset gradient increase threshold, generate damage quantification value for the gradient steep increase segment, and generate zero damage mark for the gradient stable segment. The damage quantification value is graded according to the zero-damage mark and the preset damage level grading threshold to obtain the damage level grading result of the tensioning operation. The risk level is determined based on the damage severity classification results of the tensioning operation, the triggering location of high-risk sections is identified, the initial clamping position dataset is updated, and the final damage assessment report is output.

[0007] Furthermore, the clamping force acquisition module and strain probe are deployed in the sag adjustment monitoring device to obtain the distance from the clamping point to the tension clamp exit, the contact pressure of the clamping section, the conductor deflection angle, and the strain gradient value on the aluminum strand surface, and to establish an initial clamping position dataset, including: A pressure sensor, a displacement encoder, and a strain probe array are installed on the clamping mechanism of the sag adjustment monitoring device. The pressure sensor is arranged at each contact point of the clamping section, the displacement encoder is installed between the tension clamp and the tension clamp, and the strain probe array is pasted along the aluminum strand surface of the clamping section to obtain the distance value between the outlet of the tension clamp and the tension clamp and the pressure distribution value of each contact point in the clamping section, and generate a clamping distance record table and a contact pressure distribution map. An angle sensor is arranged at the bend of the conductor in the clamping section to collect the deflection angle value of the conductor relative to the exit direction of the tension clamp. Based on the deflection angle value and the corresponding position information in the clamping distance record table, an angle deflection association record for each clamping point is generated. The strain values ​​of the aluminum strand surface at each probe position are collected by the strain probe array. The difference between the strain values ​​of adjacent probes is calculated as the strain gradient value. The strain gradient value, the angle deflection association record and the contact pressure distribution map are matched and aligned according to the collection timestamp to establish an initial clamping position dataset.

[0008] Furthermore, the distance from the clamping point to the tension clamp exit is analyzed, and areas below the preset safety lower limit are marked as stress superposition risk zones, while areas within the preset safety range are marked as normal adjustment zones, including: Extract the exit distance value corresponding to each clamping point from the initial clamping position dataset, compare the exit distance value with the preset safety lower limit threshold, and if the exit distance value is lower than the safety lower limit threshold, mark the area where the clamping point is located as a stress superposition risk zone. For clamping points whose exit distance value is not lower than the safety lower limit threshold, they are determined to be within the safety range, and the area where the clamping point is located is marked as the normal adjustment zone. Combined with the stress superposition risk zone, the area division result is obtained.

[0009] Furthermore, deflection angles and strain gradient values ​​are extracted from the stress superposition risk zone, and grouped using a k-means clustering algorithm to obtain angle abrupt change clusters and gradient steep increase clusters. Collection points exceeding preset thresholds are selected from both the angle abrupt change clusters and the gradient steep increase clusters to construct a fatigue damage potential identification group, including: Extract the deflection angle value and strain gradient value corresponding to each acquisition point from the stress superposition risk zone, combine the deflection angle value and strain gradient value of each acquisition point into a two-dimensional feature vector, and gather the feature vectors of all acquisition points to form a data set to be clustered. The k-means clustering algorithm is used to group the dataset to be clustered, and the Euclidean distance between each feature vector and the cluster center is calculated. Based on the principle of minimum distance, each feature vector is assigned to the corresponding cluster, resulting in abrupt angle change cluster and steep gradient increase cluster. Extract the sampling points whose deflection angle values ​​exceed a preset angle threshold from the angle mutation cluster, extract the sampling points whose strain gradient values ​​exceed a preset gradient threshold from the gradient steep increase cluster, merge the two types of sampling points, and construct a fatigue damage potential identification group. The deflection angle time-series records and aluminum surface strain gradient distribution data of each acquisition point are retrieved from the stress superposition risk zone. The angle change difference and gradient increase rate between adjacent acquisition points are extracted. The acquisition point positions where the angle change difference exceeds the preset deflection threshold are analyzed to form angle mutation clusters. The acquisition point intervals where the gradient increase rate continuously increases are identified to form gradient steep increase clusters. The spatial overlap section of the angle mutation cluster and the gradient steep increase cluster is determined as the core group of the fatigue damage potential identification group.

[0010] Furthermore, based on the angle mutation clusters and the gradient steep increase clusters, abnormal deflection angle segments are identified; the contact pressure of the clamping segment is fused to determine the position boundary values; and the fatigue damage potential identification group is supplemented, including: The axial position coordinate range of each acquisition point is extracted from the angle mutation cluster, and the axial position coordinate range of each acquisition point is extracted from the gradient steep increase cluster. The axial position coordinate ranges of the two clusters are combined to obtain the initial boundary of the deflection angle abnormal section. The contact pressure values ​​at each position within the clamping section are obtained, the position intervals where the contact pressure exceeds a preset pressure threshold are identified, the start and end coordinates of the position intervals are used as pressure boundary values, the initial boundary of the deflection angle abnormal section is corrected based on the pressure boundary values, and the position boundary value of the deflection angle abnormal section is determined. The sampling points within the abnormal deflection angle range defined by the location boundary value are merged with the core group of the fatigue damage potential identification group, duplicate sampling point records are removed, and the fatigue damage potential identification group is supplemented and improved.

[0011] Furthermore, the strain gradient value variation trend of the potential fatigue damage identification group is analyzed, and the segment types are divided according to a preset gradient increase threshold. Damage quantification values ​​are generated for segments with steep gradient increases, and zero-damage markers are generated for segments with stable gradients, including: The strain gradient values ​​of each acquisition point are extracted from the fatigue damage potential identification group and arranged in order of axial position coordinates of the acquisition points. The difference between the strain gradient values ​​of adjacent acquisition points is calculated. If the difference is positive, it is marked as an upward trend. If the difference is negative or zero, it is marked as a stationary trend, thus obtaining the trend identifier corresponding to each acquisition point. Based on the trend indicators, the collection points in the potential fatigue damage identification group are divided into segments. The intervals where the trend indicators of multiple consecutive collection points are all upward and the corresponding difference exceeds the preset gradient increase threshold are classified into the steep gradient increase segment, and the intervals where the trend indicators of collection points are stable are classified into the stable gradient segment. For each acquisition point in the steep gradient increase section, a damage quantification value is generated based on the cumulative increase of its strain gradient value, and a zero-damage marker is generated for each acquisition point in the stable gradient section.

[0012] Furthermore, generating zero-damage markers for each acquisition point within the gradient stabilization region includes: Historical records of strain gradient values ​​for each segment are retrieved from the fatigue damage potential identification group. The strain gradient values ​​at each time point are arranged in the order of the acquisition timestamps. The difference between the strain gradient values ​​at adjacent time points is calculated as the gradient increment. The direction of gradient change at each time point is determined according to the positive or negative sign of the gradient increment, thus obtaining the gradient increment sequence and gradient change direction sequence for each segment. For the gradient change direction sequence, the gradient change direction is detected segment by segment to see if it is continuously positive. The gradient increment at each time point is accumulated to obtain the cumulative increase of the segment. It is then determined whether the cumulative increase exceeds the preset damage start threshold. If the cumulative increase exceeds the preset damage start threshold and the gradient change direction is continuously positive, then the damage quantization value corresponding to the segment is generated according to the magnitude of the cumulative increase. If the gradient increment alternates between positive and negative and the cumulative increase is lower than the preset damage start threshold, then the segment is marked as a zero damage state. Summarize the damage quantification values ​​and zero-damage status markers for each segment, arrange them in order of axial position coordinates, and establish a comparison list of damage quantification values ​​and zero-damage markers for each segment.

[0013] Furthermore, the damage quantification value is graded according to the zero-damage marker and a preset damage severity grading threshold to obtain the damage severity grading result for the tensioning operation, including: The segments marked as zero damage are selected and their damage level is directly set to the no-damage level. For the segments marked with damage quantification values, their damage quantification values ​​are extracted as input data for grading. The damage quantification value is classified into levels according to the preset damage severity grading threshold. The segment with damage quantification value below the first grading threshold is classified as mild damage level, the segment with damage quantification value between the first grading threshold and the second grading threshold is classified as moderate damage level, and the segment with damage quantification value exceeding the second grading threshold is classified as severe damage level, thus obtaining the damage severity level label corresponding to each segment. The no-damage level and each damage level label are arranged and integrated according to the axial position coordinates of the section to obtain the damage level classification result of the tensioning operation.

[0014] Furthermore, based on the damage severity grading results of the tensioning operation, the risk level is determined, the trigger locations of high-risk sections are identified, the initial clamping position dataset is updated, and a final damage assessment report is output, including: Based on the damage severity classification results of the tensioning operation, the sections with severe damage are identified as high-risk sections, and the axial position coordinates of the high-risk sections are extracted as the trigger positions of the high-risk sections. The trigger locations of the high-risk sections and their corresponding damage quantification values ​​are written into the initial clamping position dataset for updating.

[0015] Furthermore, the output final damage assessment report includes: The damage severity classification results of the tensioning operation, the triggering locations of the high-risk sections, and the updated initial clamping positions are summarized and integrated according to the axial position coordinates of the sections to form a final damage assessment report and output it.

[0016] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a method for assessing the operational status of wind-resistant reinforcement work on power transmission lines. By deploying a clamping force acquisition module and a strain probe in a monitoring device, relevant data on clamping points are obtained, establishing an initial clamping position dataset. Addressing the issue of identifying potential fatigue damage risks in conductors, this method innovatively combines exit distance analysis with stress superposition risk zone division. A clustering algorithm is used to group deflection angles and strain gradient values, constructing potential fatigue damage identification groups. Through time-series data analysis and threshold determination, regions of abrupt angle changes and steep gradient increases are accurately identified, generating damage quantification values ​​and zero-damage markers. Finally, a damage grading algorithm outputs the damage severity grading results and assessment report for the tightening operation. This invention achieves full automation from data acquisition to risk identification to damage assessment, effectively improving the accuracy and efficiency of conductor fatigue damage monitoring and providing reliable technical support for the safe operation of power lines. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for assessing the operational status of windproof reinforcement construction of power transmission lines according to the present invention.

[0018] Figure 2 This is a schematic diagram of a method for assessing the operational status of windproof reinforcement construction of power transmission lines according to the present invention.

[0019] Figure 3 This is another schematic diagram of the method for assessing the construction status of windproof reinforcement of transmission lines according to the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0021] like Figures 1-3 This embodiment of a method for assessing the operational status of windproof reinforcement construction of transmission lines may specifically include: S101. Deploy a clamping force acquisition module and a strain probe in the sag adjustment monitoring device to obtain the distance from the clamping point to the outlet of the tension clamp, the contact pressure of the clamping section, the deflection angle of the conductor and the strain gradient value of the aluminum strand surface, and establish an initial clamping position dataset.

[0022] Pressure sensors, displacement encoders, and strain probe arrays are installed on the clamping mechanism of the sag adjustment monitoring device. Pressure sensors are positioned at each contact point in the clamping section, displacement encoders are installed between the tension clamp and the tension clamp, and the strain probe array is attached along the aluminum strand surface of the clamping section. This acquires the distance between the outlets of the tension clamp and the tension clamp, as well as the pressure distribution at each contact point within the clamping section, generating a clamping distance record table and a contact pressure distribution map. Angle sensors are placed at the conductor bends in the clamping section to collect the deflection angle of the conductor relative to the outlet direction of the tension clamp. Based on the deflection angle values ​​and their corresponding positions in the clamping distance record table, an angle deflection correlation record for each clamping point is generated. The strain values ​​on the aluminum strand surface at each probe position are collected using the strain probe array. The difference in strain values ​​between adjacent probes is calculated as the strain gradient value. The strain gradient value, the angle deflection correlation record, and the contact pressure distribution map are matched and aligned according to the acquisition timestamps to establish an initial clamping position dataset.

[0023] When deploying sensors on the clamping mechanism of the sag adjustment monitoring device, the pressure sensor is a thin-film piezoresistive sensor, arranged on each contact surface inside the clamp jaws to sense the normal pressure between the clamped conductor and the jaws. The displacement encoder uses a draw-wire displacement sensor, with its fixed end installed on the tower crossarm at the outlet of the tension clamp, and the draw wire end connected to the clamp body. The distance between the clamp and the outlet of the tension clamp is calculated by the amount of draw wire extension.

[0024] For example, the strain probe array uses resistance strain gauges, which are pasted at preset intervals along the surface of the aluminum strands of the clamping section. The pasting direction is consistent with the axial direction of the conductor. The spacing between each group of strain gauges is determined according to the outer diameter of the conductor, covering the conductor section from the outlet of the tension clamp to the clamping position of the tight clamp.

[0025] In one possible implementation, the angle sensor is a tilt sensor or a dual-axis accelerometer, mounted on a fixed bracket at the bend of the wire. The bracket is clamped to the surface of the wire in a circumferential manner. The sensor's sensitive axis is aligned with the initial axis of the wire. When the wire deflects, the sensor outputs the angular offset relative to the initial direction.

[0026] Specifically, the clamping distance record table is generated as follows: the displacement encoder outputs the cable extension / retraction amount according to a fixed sampling period; the data processing unit in the monitoring device converts the cable extension / retraction amount into the distance value between the outlet of the tension clamp and the tension clamp, and records the corresponding acquisition time, forming a clamping distance record table with time as the index and distance value as the content. The contact pressure distribution map is generated as follows: each pressure sensor synchronously outputs the contact surface pressure value; the data processing unit maps the pressure value to a two-dimensional coordinate grid according to the spatial position of each sensor inside the jaws, forming a contact pressure distribution map with the jaw contact surface as the plane and the pressure value as the height.

[0027] It should be noted that the strain gradient value is calculated as follows: along the axial direction of the conductor, the strain values ​​ε collected by two adjacent strain probes are extracted. i With ε (i+1) Let the axial distance between the two probes be Δx, then the formula for calculating the strain gradient value G is G=(ε (i+1) -ε i The value ) / Δx reflects the degree of drastic change in strain along the axial direction on the surface of the aluminum strand. The larger the gradient value, the more uneven the strain distribution in this section.

[0028] In one embodiment, the timestamp matching and alignment of multi-source data adopts a unified clock reference. Each sensor is accompanied by a timestamp mark of the internal clock of the monitoring device when collecting data. The data processing unit uses the time in the clamping distance record table as a reference, searches for the data entry with the closest timestamp from the angle deflection association record and the strain gradient value sequence, and combines the spacing value, contact pressure distribution, deflection angle value and strain gradient value corresponding to the same time into a complete record. All records are aggregated to form the initial clamping position dataset.

[0029] S102. Analyze the exit distance values, mark the areas below the safety lower limit as stress superposition risk areas, and mark the areas within the safe range as normal adjustment areas.

[0030] Extract the exit distance value corresponding to each clamping point from the initial clamping position dataset. Compare the exit distance value with a preset safety lower limit threshold. If the exit distance value is lower than the safety lower limit threshold, the area where the clamping point is located is marked as a stress superposition risk zone. For clamping points whose exit distance value is not lower than the safety lower limit threshold, they are determined to be within the safe range, and the area where the clamping point is located is marked as a normal adjustment zone. Combined with the stress superposition risk zone, the region division result is obtained.

[0031] The extraction of the exit distance value is based on the previously established initial clamping position dataset, which records the distance values ​​between each clamping point and the exit of the tension clamp. By traversing each record in the dataset, the corresponding exit distance value is read one by one.

[0032] In one possible implementation, the safety lower limit threshold is set based on the stiffness transition characteristics of the conductor at the exit section of the tension clamp. When the clamping position is too close to the exit, the conductor stiffness has not been sufficiently attenuated, and the constraint force generated by the clamping and the inherent constraint force at the exit of the clamp are easily superimposed in the same section. Therefore, the safety lower limit threshold corresponds to the boundary position of the stiffness transition zone. This threshold can be determined by finite element simulation or field testing according to the conductor type and the specifications of the tension clamp.

[0033] Specifically, the region division process is performed by comparing each point one by one. For the exit distance value of each clamping point, it is determined whether it is lower than the safety lower limit threshold. If it is lower, the clamping point is classified into the stress superposition risk zone; if it is not lower, it is classified into the normal adjustment zone. After the comparison of all clamping points is completed, the region division result containing the stress superposition risk zone and the normal adjustment zone is output, providing a basis for subsequent differentiated treatment of different regions.

[0034] S103. Extract the deflection angle and strain gradient values ​​from the stress superposition risk zone, and group them using the k-means clustering algorithm to obtain angle mutation clusters and gradient steep increase clusters. Select collection points that exceed the preset threshold from the angle mutation clusters and gradient steep increase clusters to construct fatigue damage potential identification groups.

[0035] The deflection angle and strain gradient values ​​corresponding to each acquisition point are extracted from the stress superposition risk zone. These values ​​are combined into a two-dimensional feature vector, and the feature vectors of all acquisition points are aggregated to form a dataset to be clustered. The k-means clustering algorithm is used to group the dataset, calculating the Euclidean distance between each feature vector and the cluster center. Based on the principle of minimum distance, each feature vector is assigned to its corresponding cluster, resulting in angle abrupt change clusters and gradient steep increase clusters. Acquisition points with deflection angle values ​​exceeding a preset angle threshold are extracted from the angle abrupt change clusters, and acquisition points with strain gradient values ​​exceeding a preset threshold are extracted from the gradient steep increase clusters. The two types of acquisition points are merged to construct a fatigue damage potential identification group.

[0036] Each acquisition point in the stress superposition risk zone records the deflection angle value and the strain gradient value. When these two values ​​are combined into a two-dimensional feature vector, the deflection angle value is used as the first dimension component of the feature vector, and the strain gradient value is used as the second dimension component of the feature vector. Each acquisition point corresponds to an independent feature vector.

[0037] In one possible implementation, the k-means clustering algorithm consists of two phases: cluster center initialization and iterative update. In the cluster center initialization phase, a predetermined number of feature vectors are randomly selected from the dataset to be clustered as initial cluster centers. The number of clusters is set to two based on the power transmission line tensioning scenario, corresponding to the angle-change cluster and the gradient-increase cluster, respectively. In the iterative update phase, for each feature vector, the Euclidean distance between it and each cluster center is calculated. The formula for calculating the Euclidean distance d is... , where θ i g is the deflection angle value of the acquisition point. i c represents the strain gradient value at the acquisition point. θ With c g The eigenvector is assigned to the cluster containing the deflection angle component and the strain gradient component, respectively, and then assigned to the cluster containing the closest cluster center. After all eigenvectors are assigned, the mean of all eigenvectors in each cluster is recalculated as the new cluster center. This assignment and update process is repeated until the cluster center position no longer changes or the preset number of iterations is reached.

[0038] Specifically, the eigenvectors within the angle-change cluster share the common characteristic of large deflection angle values, while the eigenvectors within the gradient-increased cluster share the common characteristic of large strain gradient values. The two types of clusters are automatically distinguished through the clustering process.

[0039] It should be noted that when selecting collection points from the angle mutation cluster and the gradient increase cluster, preset angle thresholds and preset gradient thresholds are used for judgment. Collection points in the angle mutation cluster whose deflection angle value exceeds the preset angle threshold are extracted, and collection points in the gradient increase cluster whose strain gradient value exceeds the preset gradient threshold are extracted. The two sets of collection points are merged to form a fatigue damage potential identification group. The collection points in this identification group all have the characteristics of angle mutation or gradient increase.

[0040] The deflection angle time series records and aluminum surface strain gradient distribution data of each acquisition point are retrieved from the stress superposition risk zone. The angle change difference and gradient increase rate between adjacent acquisition points are extracted. The acquisition point locations where the angle change difference exceeds the preset deflection threshold are analyzed to form angle mutation clusters. The acquisition point intervals where the gradient increase rate continuously increases are identified to form gradient steep increase clusters. The spatial overlap section of the angle mutation cluster and the gradient steep increase cluster is determined as the core group of the fatigue damage potential identification group.

[0041] The deflection angle time-series records and strain gradient distribution data of each acquisition point are retrieved from the stress superposition risk zone. The position coordinates of each acquisition point in the axial direction of the conductor are recorded. According to the arrangement order of the axial position coordinates, the deflection angle difference and strain gradient increment between two adjacent acquisition points are extracted to obtain the angle change difference sequence and the gradient increment sequence. For each difference in the angle change difference sequence, it is determined whether the difference exceeds a preset deflection threshold. If it does, the acquisition point corresponding to the difference is marked as an angle abrupt change point, and all angle abrupt change points are aggregated to form an angle abrupt change cluster. For the gradient increment sequence, the changing trend of the gradient increment is detected point by point along the axial direction of the conductor. The acquisition point intervals with continuously increasing gradient increments are identified, and the acquisition points in the continuously increasing intervals are aggregated to form a gradient steep increase cluster. Obtain the axial position coordinates of each acquisition point in the angle mutation cluster and the axial position coordinates of each acquisition point in the gradient steep increase cluster. Compare the range of axial position coordinates of the two clusters, determine the segments where the axial position coordinates overlap as spatial overlapping segments, and mark the acquisition points in the spatial overlapping segments as the core group of the fatigue damage potential identification group.

[0042] During the tensioning process, each sampling point in the stress superposition risk zone continuously records the deflection angle values, forming a time-series record of deflection angles arranged in chronological order. This time-series record reflects the angle changes of the same sampling point at different times. The strain gradient distribution data on the aluminum strand surface describes the spatial distribution of strain gradient values ​​among the sampling points along the conductor's axial direction. By reading the position coordinates of each sampling point in the conductor's axial direction, the correspondence between the sampling point and its axial position is established.

[0043] In one possible implementation, the deflection angle difference between adjacent acquisition points is extracted as follows: along the axial direction of the conductor, the acquisition points are arranged in ascending order of position coordinates. The deflection angle value of the latter acquisition point is subtracted from the deflection angle value of the former acquisition point from the deflection angle value of the latter acquisition point. The result is the angle change difference at that position. After traversing all pairs of adjacent acquisition points, a complete sequence of angle change difference values ​​is formed.

[0044] Specifically, the extraction of the incremental strain gradient is done in a similar way: the strain gradient value of the latter sampling point is subtracted from the strain gradient value of the former sampling point from the strain gradient value of the former sampling point. Positive values ​​indicate that the strain gradient is increasing along the axial direction, and negative values ​​indicate that it is decreasing. After traversing, a gradient incremental sequence is formed.

[0045] It should be noted that the formation of angle abrupt change clusters is based on a preset deflection threshold judgment logic. When the absolute value of a certain difference in the angle change difference sequence exceeds the preset deflection threshold, it indicates that the deflection angle of the conductor at that location has suddenly changed, and the sampling point corresponding to this difference is marked as an angle abrupt change point. In the tensioning operation scenario, when the conductor near the tension clamp exit is subjected to the clamping force of the tension clamp, the local bending angle may change drastically within a short distance. This angle abrupt change phenomenon is identified by the deflection threshold judgment, and all angle abrupt change points converge to form an angle abrupt change cluster.

[0046] For example, the formation of gradient steep increase clusters is based on the continuous increasing characteristic of gradient increment. The gradient increment sequence is scanned point by point along the axial direction of the conductor. When the gradient increment of multiple consecutive acquisition points is positive and the values ​​increase sequentially, it is determined that the strain gradient within this interval is in a state of continuous steep increase. This continuous increasing phenomenon indicates that the surface strain of the aluminum strand is continuously intensified and concentrated within this section, converging all acquisition points within this continuously increasing interval to form a gradient steep increase cluster. Furthermore, the determination of the spatially overlapping section is achieved by comparing the axial position coordinate ranges of the angle abrupt change cluster and the gradient steep increase cluster. The axial position coordinates of all acquisition points in the angle abrupt change cluster are extracted, and their minimum and maximum values ​​are determined as the axial coverage range of the angle abrupt change cluster; similarly, the axial position coordinates of all acquisition points in the gradient steep increase cluster are extracted to determine its axial coverage range. The axial coverage ranges of the two types of clusters are intersected. If a common interval exists, this common interval is the spatially overlapping section. Acquisition points located within the spatially overlapping section simultaneously possess both angle abrupt change and gradient steep increase characteristics.

[0047] In one embodiment, the acquisition points in the spatially overlapping section are marked as the core group of the fatigue damage potential identification group. The conductor section where these acquisition points are located has both sudden changes in deflection angle and a continuous steep increase in strain gradient. The two adverse factors overlap and superimpose in space, making this section the part with the most concentrated fatigue damage risk.

[0048] Understandably, the delineation of the core group further subdivides the collection points in the stress superposition risk zone, accurately locating the key sections where angle abrupt changes and gradient steep increases occur simultaneously from a large risk area. The data collected in this core group has a higher priority in subsequent fatigue damage assessment.

[0049] S104. Identify abnormal deflection angle sections based on angle mutation clusters and gradient steep increase clusters, determine position boundary values ​​by integrating the contact pressure of the clamping section, and supplement and improve the potential fatigue damage identification group.

[0050] The axial position coordinate ranges of each acquisition point are extracted from the angle mutation cluster and the gradient increase cluster. The axial position coordinate ranges of the two clusters are then combined to obtain the initial boundary of the deflection angle anomaly segment. The contact pressure values ​​at each position within the clamping segment are obtained, and the position intervals where the contact pressure exceeds a preset pressure threshold are identified. The start and end coordinates of these position intervals are used as pressure boundary values. The initial boundary of the deflection angle anomaly segment is corrected based on these pressure boundary values ​​to determine the position boundary values ​​of the deflection angle anomaly segment. The core group of the fatigue damage potential identification group refers to the set of key acquisition points identified through previous fatigue damage analysis. The acquisition points within the deflection angle anomaly segment defined by the position boundary values ​​are merged with the core group of the fatigue damage potential identification group, duplicate acquisition point records are removed, and the fatigue damage potential identification group is supplemented and improved.

[0051] The angle mutation cluster and the gradient steep increase cluster record the acquisition points where the deflection angle changes abruptly and the acquisition points where the strain gradient continues to increase steeply, respectively. The acquisition points in both types of clusters have their own axial position coordinates. By extracting the minimum and maximum values ​​of the axial position coordinates of the acquisition points in each cluster, the axial coverage of each cluster is determined.

[0052] In one possible implementation, the union operation is performed as follows: the smaller of the minimum value of the axial coverage range of the angle mutation cluster and the minimum value of the axial coverage range of the gradient steep increase cluster is taken as the lower boundary of the union range, and the larger of the maximum values ​​of the axial coverage ranges of the two types of clusters is taken as the upper boundary of the union range. The lower boundary and the upper boundary together define the initial boundary of the deflection angle anomalous segment.

[0053] Specifically, the contact pressure distribution within the clamping section reflects the clamping force of the clamping clamp on the conductor at different positions. When the contact pressure in a certain position range exceeds the preset pressure threshold, it indicates that the conductor in that range is subjected to a large local clamping force. The starting and ending coordinates of this abnormal pressure range are extracted as pressure boundary values.

[0054] It should be noted that the correction of the pressure boundary value to the initial boundary is achieved through a boundary expansion method: if the starting coordinate of the pressure boundary value is less than the lower boundary of the initial boundary, the lower boundary of the initial boundary is updated to the starting coordinate of the pressure boundary value; if the ending coordinate of the pressure boundary value is greater than the upper boundary of the initial boundary, the upper boundary of the initial boundary is updated to the ending coordinate of the pressure boundary value. The corrected boundary is the position boundary value of the deflection angle anomaly section. This correction method includes the area of ​​contact pressure anomaly within the deflection angle anomaly section. Furthermore, the deflection angle anomaly section defined by the position boundary value contains several acquisition points. When merging these acquisition points with the core group of the fatigue damage potential identification group, the axial position coordinates of each acquisition point are compared one by one. If the axial position coordinates of a certain acquisition point already exist in the core group, it is marked as a duplicate record and removed. Non-duplicate acquisition points are retained and added to the fatigue damage potential identification group to complete the supplementation and improvement of the fatigue damage potential identification group.

[0055] S105. Analyze the strain gradient value change trend of the potential fatigue damage identification group, divide the segment type according to the preset gradient increase threshold, generate damage quantification value for the gradient steep increase segment, and generate zero damage mark for the gradient stable segment.

[0056] The strain gradient values ​​of each acquisition point are extracted from the fatigue damage potential identification group and arranged in order of their axial position coordinates. The difference between the strain gradient values ​​of adjacent acquisition points is calculated. If the difference is positive, it is marked as an upward trend; if the difference is negative or close to zero, it is marked as a stable trend, thus obtaining the trend identifier for each acquisition point. Based on the trend identifier, the acquisition points within the fatigue damage potential identification group are divided into segments. Intervals where multiple consecutive acquisition points show an upward trend and the corresponding difference exceeds a preset gradient increase threshold are classified as steep gradient increase segments, while intervals where the trend identifier is a stable trend are classified as stable gradient segments. For each acquisition point within the steep gradient increase segment, a damage quantification value is generated based on the cumulative increase of its strain gradient value. For each acquisition point within the stable gradient segment, a zero-damage marker is generated.

[0057] Each acquisition point in the fatigue damage potential identification group records strain gradient values. These strain gradient values ​​are arranged in ascending order according to the axial position coordinates of the acquisition points to form a strain gradient value sequence along the axial direction of the conductor. By calculating the difference between two adjacent strain gradient values ​​in the sequence, the direction of change of the strain gradient at that location is determined.

[0058] In one possible implementation, the trend indicator is determined by the difference sign. When the difference between the strain gradient value of the next acquisition point and the strain gradient value of the previous acquisition point is positive, it indicates that the strain gradient is on an upward trend at that position, and the acquisition point is marked as an upward trend. When the difference is negative or the absolute value of the difference is lower than the preset fluctuation threshold, it indicates that the strain gradient is relatively stable at that position, and the acquisition point is marked as a stable trend.

[0059] Specifically, the segment division is determined based on the continuity of the trend indicator and the magnitude of the difference. When the trend indicators of several consecutive collection points are all upward and the difference corresponding to each collection point exceeds the preset gradient increase threshold, the entire interval where these collection points are located is classified into the gradient steep increase segment. When the trend indicator of a collection point is a stable trend, or although it is an upward trend, the difference does not exceed the preset gradient increase threshold, the collection point is classified into the gradient stable segment.

[0060] It should be noted that the damage quantification value is calculated based on the cumulative increase of the strain gradient value within the steep gradient increase section, where the cumulative increase A is the strain gradient value G at the initial acquisition point within that section. start Strain gradient value G at the termination acquisition point end The difference between them, i.e., A=G end -G start The damage quantification value Q is directly proportional to the cumulative increase A, which can be expressed as Q = A / A. ref Normalization is performed, where A ref As a reference for the increase benchmark, a larger cumulative increase indicates a more dramatic rise in the strain gradient within that section, and a higher corresponding damage quantification value. Furthermore, since the strain gradient values ​​at each sampling point within the gradient plateau section remain relatively stable and do not exhibit a continuous steep increase, they are uniformly marked as zero-damage markers. This indicates that the strain distribution on the surface of the aluminum strands of the conductor within this section is uniform and does not show fatigue damage risk characteristics caused by strain concentration.

[0061] Historical data of strain gradient values ​​for each segment are retrieved from the fatigue damage potential identification group. The gradient increment and gradient change direction between adjacent time points are extracted. The segments with a continuously positive gradient increment and a cumulative increase exceeding the preset damage threshold are analyzed, and corresponding damage quantification values ​​are generated. Segments with alternating positive and negative gradient increments and a cumulative increase below the damage threshold are identified and marked as zero-damage states. A comparison list of damage quantification values ​​and zero-damage marks for each segment is established.

[0062] Historical data on strain gradient values ​​for each segment are retrieved from the fatigue damage potential identification group. The strain gradient values ​​at each time point are arranged in chronological order according to the acquisition timestamp. The difference between strain gradient values ​​at adjacent time points is calculated as the gradient increment. The direction of gradient change at each time point is determined based on the sign of the gradient increment, resulting in a gradient increment sequence and a gradient change direction sequence for each segment. For the gradient change direction sequence, the gradient change direction is checked segment by segment to see if it remains positive. The gradient increments at each time point are accumulated to obtain the cumulative increase for that segment. It is then determined whether the cumulative increase exceeds a preset damage threshold. If the cumulative increase exceeds the preset damage threshold and the gradient change direction remains positive, a damage quantification value corresponding to that segment is generated based on the magnitude of the cumulative increase. If the gradient increment alternates between positive and negative and the cumulative increase is lower than the preset damage threshold, the segment is marked as a zero-damage state. The damage quantification values ​​and zero-damage state markings for each segment are summarized and arranged in axial position coordinate order to establish a comparison list of damage quantification values ​​and zero-damage markings for each segment.

[0063] During the tensioning operation, strain gradient values ​​are continuously collected in each section of the fatigue damage potential identification group, forming a historical collection record arranged in chronological order. This record reflects the strain gradient changes of the same section at different times. By retrieving the historical collection records of each section and arranging them in chronological order from the collection timestamp, a time-series strain gradient value data sequence is formed.

[0064] In one possible implementation, the gradient increment is calculated using the difference method between adjacent time points. The strain gradient value at the next time point in the time series data sequence is subtracted from the strain gradient value at the previous time point; the difference is the gradient increment for that time period. A positive gradient increment indicates that the strain gradient is increasing during that time period, a negative gradient increment indicates that the strain gradient is decreasing during that time period, and a gradient increment close to zero indicates that the strain gradient remains relatively stable.

[0065] Specifically, the direction of gradient change is determined based on the sign of the gradient increment. The gradient increment sequence of each segment is traversed, and the gradient change direction corresponding to each time point is recorded one by one, forming a gradient change direction sequence that corresponds one-to-one with the gradient increment sequence.

[0066] It should be noted that the criterion for a continuously positive gradient change direction is: in the gradient change direction sequence of a certain segment, the gradient change direction at several consecutive time points is marked as an upward trend, and there are no interruptions of downward or stationary trends in between. This continuously positive characteristic indicates that the strain gradient in this segment is always monotonically increasing over a period of time, and the strain concentration on the aluminum surface is continuously intensifying.

[0067] For example, the cumulative increase is calculated as follows: within a time interval where the gradient change direction is consistently positive, the gradient increments at each time point are summed, and the sum is the cumulative increase for that segment within that time interval. The cumulative increase reflects the overall growth rate of the strain gradient during its continuous rise; a larger value indicates a more dramatic cumulative increase in the strain gradient. Furthermore, the preset damage threshold is set based on the fatigue characteristics of the aluminum conductor material. When the cumulative increase exceeds this threshold, it indicates that the cumulative increase in the strain gradient has reached a critical level that may trigger fatigue damage. The cumulative increase is compared with the preset damage threshold to determine whether each segment has entered a damaged state.

[0068] In one embodiment, the magnitude of the damage quantification value is positively correlated with the cumulative increase; the larger the cumulative increase, the higher the corresponding damage quantification value. The damage quantification value Q is obtained through standardization, and the calculation formula is Q=(A / T)×100, where A is the cumulative increase and T is a preset damage threshold. This threshold is set according to the fatigue characteristics of the aluminum strand material of the conductor. When the cumulative increase reaches this threshold, the corresponding damage quantification value is 100, making the damage degree of different sections comparable.

[0069] It is understandable that the alternating positive and negative gradient increments indicate that the strain gradient of this section sometimes rises and sometimes falls, without showing a continuous deterioration trend. When the cumulative increase of this alternating section is lower than the preset damage starting threshold, the section is determined to be in a zero-damage state and marked as a zero-damage state marker.

[0070] Preferably, the checklist is established by arranging the axial position coordinates of the segments from smallest to largest. Each record contains three fields: segment number, axial position range, and damage quantification value or zero-damage status marker, forming a structured data table. This checklist presents the damage status of each segment in the fatigue damage potential identification group in a quantitative manner. The damage quantification value directly reflects the degree of fatigue damage risk of each segment, while the zero-damage marker identifies segments that do not currently exhibit damage characteristics.

[0071] S106. Based on the zero-damage marker and the preset damage level classification threshold, the damage quantification value is classified to obtain the damage level classification result of the tensioning operation.

[0072] Sections marked with zero damage are selected from the checklist, and their damage level is directly set to the no-damage level. For sections marked with damage quantification values, these values ​​are extracted as input data for grading. The damage quantification values ​​are then classified according to preset damage grading thresholds: sections with damage quantification values ​​below the first grading threshold are classified as slightly damaged; sections with damage quantification values ​​between the first and second grading thresholds are classified as moderately damaged; and sections with damage quantification values ​​exceeding the second grading threshold are classified as severely damaged. This yields the damage level label for each section. The no-damage level and each damage level label are then arranged and integrated according to the axial position coordinates of the sections to obtain the grading result for the tensioning operation damage level.

[0073] The checklist records the damage quantification value and zero damage mark for each segment. By traversing each record in the checklist, segments marked as zero damage are identified. These segments have been determined in the previous processing to have stable strain gradient changes and cumulative increases below the damage threshold. Therefore, their damage level is directly set to no damage level.

[0074] In one possible implementation, the grading threshold is set based on the fatigue damage characteristics of the aluminum strands in the conductor and engineering experience in wire tensioning operations.

[0075] Specifically, a damage accumulation model for aluminum alloys was analyzed using fatigue test data. The first grading threshold was set at 30, corresponding to the boundary between mild and moderate damage when the damage quantification value reached 30. The second grading threshold was set at 70, corresponding to the boundary between moderate and severe damage when the damage quantification value reached 70. These thresholds were determined based on laboratory fatigue life curves and statistical data from on-site tensioning operations. The two thresholds divide the range of damage quantification values ​​into three continuous intervals.

[0076] Specifically, the grading process is as follows: For each segment marked with a damage quantification value in the comparison list, its damage quantification value is read one by one and compared with the first grading threshold. If it is lower than the first grading threshold, it is judged as a mild damage level; if it is not lower than the first grading threshold, it is compared with the second grading threshold. If it is lower than the second grading threshold, it is judged as a moderate damage level; if it is not lower than the second grading threshold, it is judged as a severe damage level. After completion, the corresponding damage level label is assigned to the segment.

[0077] It should be noted that the integration of the damage level classification results for the tensioning operation is based on the ascending position coordinates of each section in the conductor's axial direction. Sections with no damage level and sections labeled with mild, moderate, and severe damage levels are included in the classification results to form a damage distribution map covering the entire clamping section. This classification result visually presents the damage status of each section after the tensioning operation and can be used to guide subsequent maintenance strategies, predict conductor fatigue life, optimize operation parameters, and improve overall safety management and risk control. The no-damage level indicates that the section has not been significantly affected by damage, the mild damage level indicates a slight risk of fatigue damage, the moderate damage level indicates that the fatigue damage risk has reached a level that requires attention, and the severe damage level indicates that the section faces a high risk of fatigue damage.

[0078] S107. Determine the risk level based on the damage severity classification results of the tensioning operation, identify the triggering locations of high-risk sections, update the initial clamping position dataset, and output the final damage assessment report.

[0079] Based on the damage severity classification results of the tensioning operation, sections with severe damage are classified as high-risk. The axial coordinates of these high-risk sections are extracted as trigger positions, and the trigger positions and corresponding damage quantification values ​​are written into the initial clamping position dataset for updating. The damage severity classification results, high-risk section trigger positions, and updated initial clamping position datasets are then summarized and integrated according to the axial coordinate order to form a final damage assessment report, which is then output.

[0080] The damage severity classification results of the tensioning operation include damage severity level labels for each section. By traversing each record in the classification results, sections with damage severity level labels of severe damage are identified, and the risk level of these sections is determined to be high-risk. Their axial position coordinates are extracted as the trigger positions of high-risk sections.

[0081] In one possible implementation, the initial clamping position dataset is updated using an append-only method. The axial coordinates of the trigger location of the high-risk segment and the corresponding damage quantification value of that segment are added as new fields to the corresponding record in the initial clamping position dataset. This ensures that the updated clamping position dataset includes not only the originally acquired clamping position information but also the high-risk position information identified after damage assessment. The damage quantification value corresponding to the high-risk segment is extracted from the checklist in S105, and the damage severity level is converted into a numerical code and written into the dataset: mild damage is coded as 1, moderate damage as 2, and severe damage as 3.

[0082] Specifically, the final damage assessment report is integrated and arranged in ascending order of the axial position coordinates of the sections. The report content includes the damage level of each section, the trigger position marker of high-risk sections, and key fields in the updated initial clamping position dataset, forming a complete damage assessment output document covering the entire clamping section of the tensioning operation.

[0083] If the technical solution of this application involves personal information, the product using this solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If sensitive personal information is involved, the user's separate consent has been obtained before processing, and the "express consent" requirement is met. For example, a clear sign is placed at the collection device such as a camera to inform the user that they have entered the collection area, and the user's voluntary entry is considered as consent; or the processing device clearly indicates the processing rules and obtains authorization through pop-up windows or by asking the user to upload information themselves. The personal information processing rules include the processor, the purpose of processing, the processing method, and the types of personal information.

[0084] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for assessing the operational status of windproof reinforcement construction of transmission lines, characterized in that, include: Deploy a clamping force acquisition module and a strain probe in the sag adjustment monitoring device to obtain the distance from the clamping point to the outlet of the tension clamp, the contact pressure of the clamping section, the deflection angle of the conductor and the strain gradient value of the aluminum strand surface, and establish an initial clamping position dataset. The distance between the clamping point and the outlet of the tension clamp is analyzed. Areas below the preset safety lower limit are marked as stress superposition risk areas, and areas within the preset safety range are marked as normal adjustment areas. The deflection angle and strain gradient value are extracted from the stress superposition risk zone, and grouped by k-means clustering algorithm to obtain angle mutation cluster and gradient steep increase cluster. Collection points exceeding the preset threshold are selected from the angle mutation cluster and the gradient steep increase cluster respectively to construct fatigue damage potential identification group. Based on the angle mutation cluster and the gradient steep increase cluster, identify the abnormal deflection angle segment, fuse the contact pressure of the clamping segment to determine the position boundary value, and supplement the fatigue damage potential identification group; Analyze the strain gradient value change trend of the fatigue damage potential identification group, divide the segment type according to the preset gradient increase threshold, generate damage quantification value for the gradient steep increase segment, and generate zero damage mark for the gradient stable segment. The damage quantification value is graded according to the zero-damage mark and the preset damage level grading threshold to obtain the damage level grading result of the tensioning operation. The risk level is determined based on the damage severity classification results of the tensioning operation, the triggering location of high-risk sections is identified, the initial clamping position dataset is updated, and the final damage assessment report is output.

2. The method for assessing the operational status of wind-resistant reinforcement construction of transmission lines according to claim 1, characterized in that, The clamping force acquisition module and strain probe are deployed in the sag adjustment monitoring device to obtain the distance from the clamping point to the tension clamp exit, the contact pressure of the clamping section, the conductor deflection angle, and the strain gradient value of the aluminum strand surface, and to establish an initial clamping position dataset, including: A pressure sensor, a displacement encoder, and a strain probe array are installed on the clamping mechanism of the sag adjustment monitoring device. The pressure sensor is arranged at each contact point of the clamping section, the displacement encoder is installed between the tension clamp and the tension clamp, and the strain probe array is pasted along the aluminum strand surface of the clamping section to obtain the distance value between the outlet of the tension clamp and the tension clamp and the pressure distribution value of each contact point in the clamping section, and generate a clamping distance record table and a contact pressure distribution map. An angle sensor is arranged at the bend of the conductor in the clamping section to collect the deflection angle value of the conductor relative to the exit direction of the tension clamp. Based on the deflection angle value and the corresponding position information in the clamping distance record table, an angle deflection association record for each clamping point is generated. The strain values ​​of the aluminum strand surface at each probe position are collected by the strain probe array. The difference between the strain values ​​of adjacent probes is calculated as the strain gradient value. The strain gradient value, the angle deflection association record and the contact pressure distribution map are matched and aligned according to the collection timestamp to establish an initial clamping position dataset.

3. The method for assessing the operational status of wind-resistant reinforcement construction of transmission lines according to claim 1, characterized in that, The distance from the clamping point to the tension clamp exit is analyzed. Areas below the preset safety lower limit are marked as stress superposition risk zones, and areas within the preset safety range are marked as normal adjustment zones, including: Extract the exit distance value corresponding to each clamping point from the initial clamping position dataset, compare the exit distance value with the preset safety lower limit threshold, and if the exit distance value is lower than the safety lower limit threshold, mark the area where the clamping point is located as a stress superposition risk zone. For clamping points whose exit distance value is not lower than the safety lower limit threshold, they are determined to be within the safety range, and the area where the clamping point is located is marked as the normal adjustment zone. Combined with the stress superposition risk zone, the area division result is obtained.

4. The method for assessing the operational status of wind-resistant reinforcement construction of transmission lines according to claim 1, characterized in that, The deflection angle and strain gradient values ​​are extracted from the stress superposition risk zone, and grouped using the k-means clustering algorithm to obtain angle mutation clusters and gradient steep increase clusters. Collection points exceeding preset thresholds are selected from the angle mutation clusters and the gradient steep increase clusters to construct a fatigue damage potential identification group, including: Extract the deflection angle value and strain gradient value corresponding to each acquisition point from the stress superposition risk zone, combine the deflection angle value and strain gradient value of each acquisition point into a two-dimensional feature vector, and gather the feature vectors of all acquisition points to form a data set to be clustered. The k-means clustering algorithm is used to group the dataset to be clustered, and the Euclidean distance between each feature vector and the cluster center is calculated. Based on the principle of minimum distance, each feature vector is assigned to the corresponding cluster, resulting in abrupt angle change cluster and steep gradient increase cluster. Extract the sampling points whose deflection angle values ​​exceed a preset angle threshold from the angle mutation cluster, extract the sampling points whose strain gradient values ​​exceed a preset gradient threshold from the gradient steep increase cluster, merge the two types of sampling points, and construct a fatigue damage potential identification group. The deflection angle time-series records and aluminum surface strain gradient distribution data of each acquisition point are retrieved from the stress superposition risk zone. The angle change difference and gradient increase rate between adjacent acquisition points are extracted. The acquisition point positions where the angle change difference exceeds the preset deflection threshold are analyzed to form angle mutation clusters. The acquisition point intervals where the gradient increase rate continuously increases are identified to form gradient steep increase clusters. The spatial overlap section of the angle mutation cluster and the gradient steep increase cluster is determined as the core group of the fatigue damage potential identification group.

5. The method for assessing the operational status of windproof reinforcement construction of transmission lines according to claim 1, characterized in that, Based on the angle mutation cluster and the gradient steep increase cluster, abnormal deflection angle segments are identified; the contact pressure of the clamping segment is fused to determine the position boundary value; and the fatigue damage potential identification group is supplemented, including: The axial position coordinate range of each acquisition point is extracted from the angle mutation cluster, and the axial position coordinate range of each acquisition point is extracted from the gradient steep increase cluster. The axial position coordinate ranges of the two clusters are combined to obtain the initial boundary of the deflection angle abnormal section. The contact pressure values ​​at each position within the clamping section are obtained, the position intervals where the contact pressure exceeds a preset pressure threshold are identified, the start and end coordinates of the position intervals are used as pressure boundary values, the initial boundary of the deflection angle abnormal section is corrected based on the pressure boundary values, and the position boundary value of the deflection angle abnormal section is determined. The sampling points within the abnormal deflection angle range defined by the location boundary value are merged with the core group of the fatigue damage potential identification group, duplicate sampling point records are removed, and the fatigue damage potential identification group is supplemented and improved.

6. The method for assessing the operational status of wind-resistant reinforcement construction of transmission lines according to claim 1, characterized in that, Analyze the strain gradient value variation trend of the potential fatigue damage identification group, divide the segment types according to the preset gradient increase threshold, generate damage quantification values ​​for segments with steep gradient increases, and generate zero damage markers for segments with stable gradients, including: The strain gradient values ​​of each acquisition point are extracted from the fatigue damage potential identification group and arranged in order of axial position coordinates of the acquisition points. The difference between the strain gradient values ​​of adjacent acquisition points is calculated. If the difference is positive, it is marked as an upward trend. If the difference is negative or zero, it is marked as a stationary trend, thus obtaining the trend identifier corresponding to each acquisition point. Based on the trend indicators, the collection points in the potential fatigue damage identification group are divided into segments. The intervals where the trend indicators of multiple consecutive collection points are all upward and the corresponding difference exceeds the preset gradient increase threshold are classified into the steep gradient increase segment, and the intervals where the trend indicators of collection points are stable are classified into the stable gradient segment. For each acquisition point in the steep gradient increase section, a damage quantification value is generated based on the cumulative increase of its strain gradient value, and a zero-damage marker is generated for each acquisition point in the stable gradient section.

7. The method for assessing the operational status of wind-resistant reinforcement construction of transmission lines according to claim 6, characterized in that, The generation of zero-damage markers for each acquisition point within the gradient-stable region includes: Historical records of strain gradient values ​​for each segment are retrieved from the fatigue damage potential identification group. The strain gradient values ​​at each time point are arranged in the order of the acquisition timestamps. The difference between the strain gradient values ​​at adjacent time points is calculated as the gradient increment. The direction of gradient change at each time point is determined according to the positive or negative sign of the gradient increment, thus obtaining the gradient increment sequence and gradient change direction sequence for each segment. For the gradient change direction sequence, the gradient change direction is detected segment by segment to see if it is continuously positive. The gradient increment at each time point is accumulated to obtain the cumulative increase of the segment. It is then determined whether the cumulative increase exceeds the preset damage start threshold. If the cumulative increase exceeds the preset damage start threshold and the gradient change direction is continuously positive, then the damage quantization value corresponding to the segment is generated according to the magnitude of the cumulative increase. If the gradient increment alternates between positive and negative and the cumulative increase is lower than the preset damage start threshold, then the segment is marked as a zero damage state. Summarize the damage quantification values ​​and zero-damage status markers for each segment, arrange them in order of axial position coordinates, and establish a comparison list of damage quantification values ​​and zero-damage markers for each segment.

8. The method for assessing the operational status of wind-resistant reinforcement construction of transmission lines according to claim 1, characterized in that, The damage quantification value is graded according to the zero-damage marker and the preset damage severity grading threshold to obtain the damage severity grading result of the tensioning operation, including: The segments marked as zero damage are selected and their damage level is directly set to the no-damage level. For the segments marked with damage quantification values, their damage quantification values ​​are extracted as input data for grading. The damage quantification value is classified into levels according to the preset damage severity grading threshold. The segment with damage quantification value below the first grading threshold is classified as mild damage level, the segment with damage quantification value between the first grading threshold and the second grading threshold is classified as moderate damage level, and the segment with damage quantification value exceeding the second grading threshold is classified as severe damage level, thus obtaining the damage severity level label corresponding to each segment. The no-damage level and each damage level label are arranged and integrated according to the axial position coordinates of the section to obtain the damage level classification result of the tensioning operation.

9. The method for assessing the operational status of wind-resistant reinforcement construction of transmission lines according to claim 1, characterized in that, Based on the damage severity grading results of the tensioning operation, the risk level is determined, the trigger locations of high-risk sections are identified, the initial clamping position dataset is updated, and a final damage assessment report is output, including: Based on the damage severity classification results of the tensioning operation, the sections with severe damage are identified as high-risk sections, and the axial position coordinates of the high-risk sections are extracted as the trigger positions of the high-risk sections. The trigger locations of the high-risk sections and their corresponding damage quantification values ​​are written into the initial clamping position dataset for updating.

10. The method for assessing the operational status of windproof reinforcement construction of transmission lines according to claim 9, characterized in that, The output final damage assessment report includes: The damage severity classification results of the tensioning operation, the triggering locations of the high-risk sections, and the updated initial clamping positions are summarized and integrated according to the axial position coordinates of the sections to form a final damage assessment report and output it.

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