A network sealing quality control method and system based on network sealing robot

Abstract

The present invention relates to the field of automation control technology, and in particular to a network sealing quality control method and system based on a network sealing robot, including filtering the original tension data of a power line in a vibration environment to obtain tension filtering data; obtaining the wire expansion and contraction amount according to the wire surface temperature data; controlling the tension to return to a preset tension range according to the wire expansion and contraction amount and the tension filtering data, and after the tension control is completed, collecting the tension distribution data of each network cable fixed point on the power line, using the finite element analysis method to simulate and obtain the simulation result of the network cable fixed state; determining the network sealing quality optimization compensation parameters based on the network cable fixed state simulation result and the tension fluctuation data during the network sealing operation; and adaptively regulating the operation parameters of the network sealing robot based on the network sealing quality optimization compensation parameters. The present invention realizes stable and efficient wire network sealing in a vibration environment and temperature changes through finite element analysis and other technologies, ensuring the safety of power grid operation.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and in particular to a network sealing quality control method and system based on a network sealing robot. Background Art

[0002] In the construction and maintenance of power lines, conductor sealing is a crucial process, which is directly related to the safe operation and stability of the power grid. The sealing process usually relies on sealing robots for automated operations to improve efficiency and accuracy. However, in actual operations, the conductor sealing process faces many technical challenges, especially under complex and changeable environmental conditions. These challenges are more prominent. In particular, in the conductor sealing process, the accuracy of tension control is easily disturbed by environmental vibrations. Vibrations come not only from natural environments such as wind and earthquakes, but also from the operation of the construction equipment itself. These vibrations greatly reduce the measurement accuracy of the tension sensor, making it difficult to achieve the ideal tension control state. Once the tension control is inaccurate, the sealing will be unstable, increasing the risk of power grid operation.

[0003] In addition, the physical properties of the conductor material itself further increase the complexity of network sealing control. As the ambient temperature changes, the conductor will change in length due to thermal expansion and contraction. This change undoubtedly increases the difficulty of fixing the network cable for the network sealing robot that relies on precise network cable positioning. Although the network sealing robot has designed a compensation mechanism to cope with the dynamic changes in the conductor length and ensures the network sealing quality by real-time detection and adjustment of the network cable tightness, frequent adjustment operations will cause new tension fluctuation problems. This tension fluctuation will not only cause uneven fixation of the network cable, but also produce excessive impact loads at some locations, thereby reducing the strength of the joint and affecting the stability and safety of the entire network sealing structure. In summary, the existing network sealing technology has many problems under the influence of vibration environment and temperature changes. These problems affect the stability and efficiency of the network sealing process. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a network sealing quality control method and system based on a network sealing robot.

[0005] In a first aspect, the present invention provides a network sealing quality control method based on a network sealing robot, the method comprising the following steps:

[0006] Capturing original tension data of the power line in a vibration environment, and filtering the original tension data to obtain tension filtering data;

[0007] The thermal expansion and contraction effect of the conductor is analyzed based on the collected conductor surface temperature data to obtain the conductor expansion and contraction amount caused by temperature change;

[0008] According to the wire expansion and contraction amount and the tension filtering data, the tension is controlled to return to the preset tension range through the proportional-integral-differential control algorithm, and after the tension control is completed, the tension distribution data of each network cable fixed point installed by the network sealing robot on the power line is collected;

[0009] According to the tension distribution data, a finite element analysis method is used to simulate the fixed state of the network cable under different tensions to obtain a simulation result of the fixed state of the network cable;

[0010] Acquire tension fluctuation data during the net sealing operation, and determine net sealing quality optimization compensation parameters based on the simulation result of the fixed state of the net cable and the tension fluctuation data;

[0011] The operating parameters of the network sealing robot are adaptively controlled based on the network sealing quality optimization compensation parameters.

[0012] In a further embodiment, the step of filtering the original tension data to obtain tension filtered data comprises:

[0013] According to the mean and standard deviation of the original tension data, using a preset abnormal threshold to filter out abnormal data points from the original tension data;

[0014] According to the cubic spline interpolation method, the abnormally excluded data points in the original tension data are repaired by interpolating adjacent valid data points to obtain a repair signal;

[0015] Performing a fast Fourier transform on the repair signal to obtain a frequency spectrum, and determining a main frequency component of the vibration according to the frequency spectrum;

[0016] According to the main frequency component of vibration, the repair signal is filtered by using a third-order Butterworth bandpass filter to obtain a smooth signal.

[0017] The Daubechies fourth-order wavelet is used to decompose the smooth signal into four layers to obtain wavelet coefficients of different scales, and the wavelet coefficients are subjected to threshold shrinkage processing to reconstruct tension filter data.

[0018] In a further embodiment, the step of analyzing the thermal expansion and contraction effect of the wire according to the collected wire surface temperature data to obtain the wire expansion and contraction amount caused by temperature change includes:

[0019] Using a sliding average window to perform zero drift correction on the surface temperature data of the conductor to obtain calibration temperature data;

[0020] Calculating a temperature change rate according to the calibration temperature data, and if the temperature change rate is higher than a preset temperature change threshold, converting the temperature change into an initial expansion amount according to a temperature expansion coefficient of the conductor material;

[0021] The initial expansion and contraction is subjected to stress compensation to obtain the expansion and contraction of the wire caused by temperature change.

[0022] In a further embodiment, the step of controlling the tension to return to a preset tension range through a proportional-integral-differential control algorithm according to the wire expansion and contraction amount and the tension filtering data comprises:

[0023] Calculating the displacement compensation amount required for an ideal tension state according to the wire expansion and contraction amount and the tension filtering data;

[0024] The compensation preload is calculated based on the displacement compensation amount, and the target motion parameter that the servo motor driven compensation device should achieve is determined based on the compensation preload and the current network cable tension;

[0025] According to the target motion parameters that the servo motor should achieve, the optimal control parameters of the proportional-integral-differential control algorithm are calculated using the genetic algorithm;

[0026] Collect the current tension data during the net sealing operation, and calculate the root mean square value of the current tension data within the time window to obtain the tension fluctuation amplitude;

[0027] Comparing the tension fluctuation amplitude with a preset tension reference value to obtain a tension deviation;

[0028] According to the optimal control parameters, the proportional-integral-differential control algorithm is used to perform integral and differential operations on the tension deviation to obtain an original compensation amount, and the tension is controlled to return to a preset tension range according to the original compensation amount.

[0029] In a further embodiment, the step of controlling the tension to return to a preset tension range through a proportional-integral-differential control algorithm according to the wire expansion and contraction amount and the tension filtering data further includes:

[0030] The real-time tension data of the servo motor driven compensation device during the compensation process is continuously monitored by the tension sensor;

[0031] The stiffness attenuation is calculated according to the real-time tension data and the predetermined expected tension value, and the optimal control parameters of the proportional-integral-differential control algorithm are updated and adjusted according to the stiffness attenuation.

[0032] In a further embodiment, the step of simulating the fixed state of the network cable under different tensions using a finite element analysis method according to the tension distribution data to obtain a simulation result of the fixed state of the network cable comprises:

[0033] Collect the three-dimensional coordinate data of each fixed point of the network line on the power line, and construct a tetrahedral grid based on the three-dimensional coordinate data of each fixed point of the network line;

[0034] According to the tetrahedral mesh, the interaction force of the mesh wires at the intersection is simulated by using the finite difference method;

[0035] According to the interaction force of the network cables at the intersection, the deformation trend of the fixed point under different working conditions is obtained using a multi-layer perceptron.

[0036] Calculating the strain of the mesh wire at the intersection according to the deformation trend, and identifying the stress concentration area of ​​the intersection according to the strain of the mesh wire at the intersection using Hooke's law;

[0037] Collect vibration signals of fixed points of each network cable on the power line, extract the main vibration frequency from the vibration signal through Fourier transform, and use the main vibration frequency as the dynamic boundary condition;

[0038] According to the dynamic boundary conditions, the tension distribution data and the stress concentration area at the intersection, a finite element analysis method is used to simulate the fixed state of the network cable under different tensions to obtain a simulation result of the fixed state of the network cable.

[0039] In a further embodiment, the step of determining the net sealing quality optimization compensation parameter based on the net cable fixed state simulation result and the tension fluctuation data comprises:

[0040] Establishing a tension fluctuation and network sealing quality relationship model according to the network cable fixed state simulation result and the tension fluctuation data;

[0041] The displacement compensation gain is set according to the vibration response characteristics, and the temperature correction coefficient is calculated using the temperature influence coefficient according to the tension deviation and the ambient temperature data during the net sealing operation;

[0042] The temperature correction coefficient is combined with the displacement compensation gain, and the minimization of the sealing quality error is taken as the optimization goal. The sealing quality compensation parameters are iteratively optimized using a genetic algorithm until the preset convergence conditions are reached, and the optimized compensation parameters are output;

[0043] Generate compensation control instructions for a servo motor drive compensation device according to the optimized compensation parameters;

[0044] The reference trajectory of the servo motor is adjusted according to the compensation control instruction to obtain the motion trajectory of the servo motor driven compensation device;

[0045] The motion trajectory is simulated and analyzed, and the net sealing quality error caused by the tension fluctuation is evaluated using the tension fluctuation and net sealing quality relationship model, and the net sealing quality optimization compensation parameters are output.

[0046] In a further embodiment, the step of establishing a tension fluctuation and network sealing quality relationship model according to the network cable fixed state simulation result and the tension fluctuation data comprises:

[0047] Extracting fixed point stress distribution data from the simulation result of the fixed state of the network cable, and reducing the dimension of the fixed point stress distribution data by using the principal component analysis method, and extracting the principal component of the fixed point stress distribution data greater than a preset stress threshold;

[0048] Calculating the stress transfer coefficient between different mesh cable fixing points according to the principal component, and performing weighted processing on the tension fluctuation data using the stress transfer coefficient to obtain tension fluctuation correction data;

[0049] Collecting ambient temperature data and vibration response data during the net sealing operation, integrating the tension fluctuation correction data, the ambient temperature data and the vibration response data, and constructing a net sealing quality scoring matrix using a hierarchical weighting method;

[0050] The regression analysis algorithm is used to correlate the tension fluctuation correction data with the net sealing quality scoring matrix, and a relationship model between tension fluctuation and net sealing quality is constructed.

[0051] In a second aspect, the present invention provides a network sealing quality control system based on a network sealing robot, the system comprising:

[0052] A data acquisition module is used to capture the original tension data of the power line in a vibration environment, and filter the original tension data to obtain tension filtering data;

[0053] The temperature analysis module is used to analyze the thermal expansion and contraction effect of the wire according to the collected wire surface temperature data, and obtain the wire expansion and contraction caused by temperature change;

[0054] A tension control module, used to control the tension to return to a preset tension range through a proportional-integral-differential control algorithm according to the wire expansion and contraction amount and the tension filtering data, and after the tension control is completed, collect the tension distribution data of each network cable fixed point installed by the network sealing robot on the power line;

[0055] A fixing analysis module, used to simulate the fixing state of the network cable under different tensions by using a finite element analysis method according to the tension distribution data, and obtain a simulation result of the fixing state of the network cable;

[0056] An optimization compensation module, used to obtain tension fluctuation data during the network sealing operation, and determine network sealing quality optimization compensation parameters based on the simulation result of the fixed state of the network cable and the tension fluctuation data;

[0057] The compensation control module is used to adaptively control the operating parameters of the network sealing robot based on the optimization of compensation parameters of the network sealing quality.

[0058] In a further embodiment, the data acquisition module is specifically used for:

[0059] According to the mean and standard deviation of the original tension data, using a preset abnormal threshold to filter out abnormal data points from the original tension data;

[0060] According to the cubic spline interpolation method, the abnormally excluded data points in the original tension data are repaired by interpolating adjacent valid data points to obtain a repair signal;

[0061] Performing a fast Fourier transform on the repair signal to obtain a frequency spectrum, and determining a main frequency component of the vibration according to the frequency spectrum;

[0062] According to the main frequency component of vibration, the repair signal is filtered by using a third-order Butterworth bandpass filter to obtain a smooth signal.

[0063] The Daubechies fourth-order wavelet is used to decompose the smooth signal into four layers to obtain wavelet coefficients of different scales, and the wavelet coefficients are subjected to threshold shrinkage processing to reconstruct tension filter data.

[0064] The present invention provides a network sealing quality control method and system based on a network sealing robot. The method captures original tension data of a power line in a vibration environment, and filters the original tension data to obtain tension filtering data; analyzes the thermal expansion and contraction effect of the conductor according to the collected conductor surface temperature data to obtain the conductor expansion and contraction caused by temperature change; controls the tension to return to a preset tension range through a proportional-integral-differential control algorithm according to the conductor expansion and contraction and tension filtering data, and after the tension control is completed, collects the tension distribution data of each network cable fixed point installed by the network sealing robot on the power line; simulates the fixed state of the network cable under different tensions using a finite element analysis method according to the tension distribution data to obtain a simulation result of the fixed state of the network cable; obtains the tension fluctuation data during the network sealing operation, and determines the network sealing quality optimization compensation parameters based on the network cable fixed state simulation result and the tension fluctuation data; and adaptively controls the operation parameters of the network sealing robot based on the network sealing quality optimization compensation parameters. Compared with the existing technology, this method achieves stable and efficient conductor sealing under vibration environment and temperature changes by combining PID control algorithm and finite element analysis simulation technology. It can accurately control the tension in the conductor sealing process, which not only improves the firmness and uniformity of the network cable fixation, but also improves the quality and efficiency of the sealing operation, ensuring the safety of power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1It is a schematic flow chart of a network sealing quality control method based on a network sealing robot provided in an embodiment of the present invention;

[0066] Figure 2 It is a block diagram of a network sealing quality control system based on a network sealing robot provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0068] refer to Figure 1 The embodiment of the present invention provides a network sealing quality control method based on a network sealing robot, such as Figure 1 As shown, the method comprises the following steps:

[0069] S1. Capturing original tension data of a power line in a vibration environment, and filtering the original tension data to obtain tension filtering data.

[0070] In this embodiment, the step of filtering the original tension data to obtain tension filtered data includes:

[0071] According to the mean and standard deviation of the original tension data, using a preset abnormal threshold to filter out abnormal data points from the original tension data;

[0072] According to the cubic spline interpolation method, the abnormally excluded data points in the original tension data are repaired by interpolating adjacent valid data points to obtain a repair signal;

[0073] Performing a fast Fourier transform on the repair signal to obtain a frequency spectrum, and determining a main frequency component of the vibration according to the frequency spectrum;

[0074] According to the main frequency component of vibration, the repair signal is filtered by using a third-order Butterworth bandpass filter to obtain a smooth signal.

[0075] The Daubechies fourth-order wavelet is used to decompose the smooth signal into four layers to obtain wavelet coefficients of different scales, and the wavelet coefficients are subjected to threshold shrinkage processing to reconstruct tension filter data.

[0076] During the power overhead line sealing operation, this embodiment obtains the frequency corresponding to the vibration generated by the external environment (such as wind, temperature, etc.) or the sealing robot's own movement, obtains the vibration frequency, and sets the sampling time interval according to the vibration frequency to ensure that the sampling frequency is greater than 2 times the vibration frequency to comply with the Nyquist sampling theorem and avoid information loss. For example, assuming the vibration frequency is 10 Hz, the sampling frequency is set to be above 20 Hz, and 25 data points are collected in each sampling time interval.

[0077] In this embodiment, a tension sensor is used to capture the original tension data of the power line in a vibration environment at a sampling time interval, and the mean and standard deviation of the original tension data are calculated. The abnormal elimination data points are screened out using a preset abnormal threshold. In this embodiment, 3 times the standard deviation can be set as the abnormal threshold to screen out the abnormal elimination data points that exceed the abnormal threshold. For the screened abnormal elimination data points, cubic spline interpolation is performed using adjacent valid data points. The repair value of the abnormal elimination data point is obtained by interpolation calculation, thereby obtaining a repair signal. The repair signal is subjected to a fast Fourier transform (FFT) to obtain a spectrum diagram, and the frequency component with the largest amplitude is found in the spectrum diagram to obtain the main frequency component of the vibration. The parameters of the third-order Butterworth bandpass filter (such as the center frequency, bandwidth parameters, etc.) are set according to the main frequency component of the vibration to ensure that the filter can filter out the power frequency interference and retain the main frequency component of the vibration. In this embodiment, the set third-order The repair signal is filtered by a Butterworth bandpass filter to obtain a smooth signal. For example, in this embodiment, the center frequency of the Butterworth bandpass filter can be set to 10 Hz, and the bandwidth can be set to a certain range of the main frequency component of the vibration (such as ±1 Hz). The repair signal is filtered by a third-order Butterworth bandpass filter to obtain a preliminary smooth signal, in which high-frequency noise and low-frequency interference have been effectively suppressed. Then, the preliminary smooth signal is subjected to a median smoothing operation with twice the vibration period as the smoothing window length to obtain a final smoothed signal. The final smoothed signal is subjected to a four-layer Daubechies fourth-order wavelet decomposition to obtain wavelet coefficients of four scales. The wavelet coefficients of each scale are subjected to a soft threshold shrinkage process (such as a hard threshold or a soft threshold method), such as setting a threshold to twice the mean of the wavelet coefficients to remove noise and detail information, and reconstructed according to the processed wavelet coefficients to obtain the final tension filtering data.

[0078] S2. Analyze the thermal expansion and contraction effect of the wire based on the collected wire surface temperature data to obtain the wire expansion and contraction amount caused by temperature change.

[0079] In this embodiment, the step of analyzing the thermal expansion and contraction effect of the wire according to the collected surface temperature data of the wire to obtain the expansion and contraction amount of the wire caused by the temperature change includes:

[0080] Using a sliding average window to perform zero drift correction on the surface temperature data of the conductor to obtain calibration temperature data;

[0081] Calculating a temperature change rate according to the calibration temperature data, and if the temperature change rate is higher than a preset temperature change threshold, converting the temperature change into an initial expansion amount according to a temperature expansion coefficient of the conductor material;

[0082] The initial expansion and contraction is subjected to stress compensation to obtain the expansion and contraction of the wire caused by temperature change.

[0083] In this embodiment, the temperature data of the surface of the conductor is continuously collected by the temperature sensor. In order to obtain accurate measurement, the sampling interval is set to 1 second based on the response time of the thermal resistor in this embodiment. It should be noted that during the long-term operation of the temperature sensor, due to the influence of environmental factors (such as humidity, electromagnetic interference, etc.), the output signal of the temperature sensor will have a zero drift error, causing the measurement result to deviate from the true value. The zero drift will directly affect the accuracy of the temperature measurement, and then affect the accuracy of the subsequent thermal expansion and contraction effect analysis. Therefore, in order to correct these errors, the length of the sliding average window is selected according to the sampling frequency and drift degree of the conductor surface temperature data in this embodiment. According to the length of the sliding average window, the sliding average filtering method is used to correct the zero drift of the conductor surface temperature data. Specifically, the sliding average window length is set based on 5 times the value of the thermal resistor response time in this embodiment. For the conductor surface temperature data at each time point, the average value of several time points before and after it (for example, the first 5 and the last 5 time points, a total of 11 data) is calculated. This average value is used as the calibration temperature data at the time point, so that the low-frequency drift component in the temperature data can be effectively removed, and the accuracy of temperature measurement is improved.

[0084] For two adjacent data points in the calibration temperature data, this embodiment calculates the temperature change of the two adjacent data points in unit time to obtain the temperature change rate, and sets the temperature change threshold according to the material characteristics of the wire. If the calculated temperature change rate is lower than the preset temperature change threshold, it is considered that the temperature change is relatively slow, and the influence of the temperature change on the expansion and contraction of the wire can be ignored, and no subsequent calculation is performed; otherwise, this embodiment obtains the expansion coefficient of the material at different temperatures by consulting the temperature expansion coefficient table of the wire material. The temperature expansion coefficient represents the relative change of the wire length under unit temperature change, and uses the temperature transfer function to calculate the wire's The initial expansion and contraction, specifically, in this embodiment, the temperature change rate is multiplied by the temperature expansion coefficient and the original length of the wire to calculate the initial expansion and contraction of the wire. Since the wire will be affected by mechanical stresses such as tension and pressure in actual applications, these stresses will change the length of the wire. Therefore, it is necessary to perform stress compensation on the initial expansion and contraction to obtain a more accurate expansion and contraction. In this embodiment, the compensation coefficient of the wire under different stresses can be determined through experiments or theoretical calculations. This coefficient describes the degree of influence of stress on the expansion and contraction of the wire. The initial expansion and contraction is multiplied by the stress compensation coefficient to calculate the influence of stress on the expansion and contraction of the wire, and the expansion and contraction of the wire caused by temperature change is obtained.

[0085] S3. According to the wire expansion and contraction amount and the tension filtering data, the tension is controlled to return to the preset tension range through the proportional-integral-differential control algorithm, and after the tension control is completed, the tension distribution data of each network cable fixed point installed by the network sealing robot on the power line is collected.

[0086] In this embodiment, the step of controlling the tension to return to a preset tension range through a proportional-integral-differential control algorithm according to the wire expansion and contraction amount and the tension filtering data includes:

[0087] Calculating the displacement compensation amount required for an ideal tension state according to the wire expansion and contraction amount and the tension filtering data;

[0088] The compensation preload is calculated based on the displacement compensation amount, and the target motion parameter that the servo motor driven compensation device should achieve is determined based on the compensation preload and the current network cable tension;

[0089] According to the target motion parameters that the servo motor should achieve, the optimal control parameters of the proportional-integral-differential control algorithm are calculated using the genetic algorithm;

[0090] Collect the current tension data during the net sealing operation, and calculate the root mean square value of the current tension data within the time window to obtain the tension fluctuation amplitude;

[0091] Comparing the tension fluctuation amplitude with a preset tension reference value to obtain a tension deviation;

[0092] According to the optimal control parameters, the proportional-integral-differential control algorithm is used to perform integral and differential operations on the tension deviation to obtain an original compensation amount, and the tension is controlled to return to a preset tension range according to the original compensation amount.

[0093] Specifically, this embodiment consults the specification table of the lead screw to obtain the linear displacement generated by each rotation of the lead screw (i.e., the lead of the lead screw), and simplifies the wire and its connected parts into a spring model. In the spring model, the elastic behavior of the wire and its connected parts can be described by the stiffness (k) of the spring, that is, the tension change caused by a unit displacement change. Then, the ideal tension value is set according to the requirements of the network sealing operation and the material properties of the network cable. The spring model is used to calculate the displacement compensation amount that needs to be generated in theory according to the ideal tension value and the tension filtering data. This embodiment can calculate the theoretical compensation amount through the spring model and Hooke's law (F=kx). Theoretically, the displacement compensation amount that needs to be generated, where F is the tension compensation demand, which is obtained by the tension difference between the ideal tension value and the tension filter data, k is the spring stiffness, and x is the displacement compensation amount. It should be noted that due to the elasticity of the wire itself, the displacement compensation amount is divided by the linear displacement generated by each rotation of the screw to obtain the number of turns the screw needs to rotate. According to the transmission ratio between the screw and the motor, the number of turns of the screw is converted into the amount of rotation of the motor, such as the angle or number of pulses that the motor needs to rotate. The displacement compensation amount indicates the displacement amount that needs to be compensated for the wire in order to achieve the ideal tension state.

[0094] Then, after obtaining the displacement compensation amount, this embodiment obtains the preload force according to the product of the tensile stress of the wire and the cross-sectional area of ​​the wire, obtains the theoretical elongation of the wire under the action of the preload force according to the product of the preload force and the length of the wire, obtains the wire stiffness-related parameters according to the product of the wire cross-sectional area and the wire elastic modulus, obtains the deformation of the wire when subjected to force according to the ratio of the theoretical elongation and the wire stiffness-related parameters, compares the deformation of the wire when subjected to force with the displacement compensation amount, and iteratively adjusts the preload force until the deformation matches the displacement compensation amount. When the deformation matches the displacement compensation amount, the preload force at this time is the compensating preload force that needs to be applied. After obtaining the compensating preload force, combined with the current network cable tension, the force control algorithm can be used to determine the target motion parameters that the servo motor needs to achieve. The target motion parameters include the motor speed, acceleration or target position, etc., to ensure that the tension of the network cable can be adjusted as expected. Adjustment is performed, and then the servo motor is taken as the controlled object, a proportional-integral-differential (PID) controller is established, and the optimal control parameters of the proportional-integral-differential (PID) controller are found by using a genetic algorithm. These parameters can enable the control system to achieve the best response speed and stability. In the genetic algorithm, this embodiment randomly generates a group of parameter combinations that are better than the PID controller as the initial population, applies each parameter combination to the control system, simulates the degree of reduction of its tension fluctuation, and evaluates its fitness according to the degree of reduction of the tension fluctuation. According to the fitness, excellent individuals are selected for crossover and mutation operations to generate a new population. After multiple generations of iterative optimization, until the preset number of iterations is reached, the optimal control parameters of the proportional coefficient (Kp), integral coefficient (Ki) and differential coefficient (Kd) of the PID controller are output. These parameters can enable the control system to achieve the best response speed and stability.

[0095] During the net sealing operation, this embodiment monitors the change of tension in real time through the tension sensor installed on the equipment, continuously collects the current tension data, processes the collected data within a certain time window, calculates the root mean square value (RMS) of the current tension data during this period, and this value represents the fluctuation amplitude of the tension. The calculated tension fluctuation amplitude is compared with the preset tension reference value to obtain the tension deviation. This tension reference value is set according to the process requirements and material properties and represents the ideal tension state. This embodiment inputs the tension deviation into the PID control algorithm for proportional, integral, and differential operations, and the proportional link will respond immediately. Deviation, the integral link will eliminate long-term deviation, while the differential link will predict the trend of the deviation so that adjustments can be made in advance. Through the combination of these three control actions, the original compensation amount can be obtained. This original compensation amount is the basis for adjusting the motion parameters of the servo motor. Finally, the original compensation amount is converted into a control signal of the servo motor, the servo motor is driven to move, and the position or force of the compensation device is adjusted to adjust the tension so that it returns to the preset tension range. Repeat the above steps, continuously collect tension data, calculate the deviation amount, perform PID control operations, drive the servo motor to adjust, and realize real-time closed-loop control of the tension to ensure that the tension is always stable within the preset range.

[0096] In this embodiment, the step of controlling the tension to return to a preset tension range through a proportional-integral-differential control algorithm according to the wire expansion and contraction amount and the tension filtering data further includes:

[0097] The real-time tension data of the servo motor driven compensation device during the compensation process is continuously monitored by the tension sensor;

[0098] The stiffness attenuation is calculated according to the real-time tension data and the predetermined expected tension value, and the optimal control parameters of the proportional-integral-differential control algorithm are updated and adjusted according to the stiffness attenuation.

[0099] High-precision tension sensors are installed at key locations of the network sealing robot (such as wire connection points or near compensation devices) to ensure that the sensors can accurately and in real time capture the tension changes of the wires during the compensation process. The tension sensors continuously monitor the real-time tension of the wires and transmit the data to the control system of the network sealing robot. The control system performs preliminary processing on the received tension data, such as filtering, to remove noise and abnormal values. The expected tension value is pre-set in the control system. The expected tension value is determined based on the requirements of the network sealing operation and the material properties of the wire. The real-time tension data is compared with the expected tension value (tension reference value) to calculate the difference between the two, i.e. the tension deviation.

[0100] Stiffness attenuation refers to the phenomenon that the ability of a wire to resist deformation gradually weakens after long-term use or external force. This embodiment analyzes the trend of tension deviation over time, and analyzes the correlation between the wire expansion and contraction amount and tension deviation in combination with the wire expansion and contraction amount data, such as whether there is a positive correlation or negative correlation. The stiffness attenuation of the wire is analyzed in combination with the tension deviation and wire expansion and contraction amount data. When the tension deviation gradually increases and the wire expansion and contraction amount also increases accordingly, it can be inferred that the stiffness of the wire is gradually attenuating. In order to obtain a specific stiffness attenuation amount, this embodiment can use linear or nonlinear regression analysis to describe the tension deviation-deformation relationship of the wire. The tension deviation-deformation relationship of the wire describes the expansion and contraction behavior of the wire under different tensions. By comparing the regression analysis results at different time points, the changes in model parameters (such as slope, intercept, etc.) can be observed. These changes can reflect the attenuation of the wire stiffness and the wire stiffness attenuation can be calculated. In order to obtain a specific stiffness attenuation amount, this embodiment The differences between the regression model parameters at different time points can be further calculated, and these differences can be converted into quantitative indicators of stiffness attenuation. The PID control algorithm includes three parameters: proportion (P), integration (I) and differentiation (D), which together determine the response speed and stability of the control system. In this embodiment, the control system dynamically adjusts the PID control parameters according to the estimation result of the stiffness attenuation. If the stiffness attenuation is serious, it is necessary to increase the proportional coefficient (Kp) to improve the response speed; if the stiffness attenuation decreases, the proportional coefficient Kp can be reduced; at the same time, according to the size of the stiffness attenuation, the integral coefficient Ki and the differential coefficient Kd are appropriately adjusted to optimize the steady-state error and dynamic response of the system. The adjusted PID parameters will be used to update the parameters of the PID controller, thereby achieving precise control of the tension. Through this closed-loop control method, the preset tension range can be continuously approached and maintained, ensuring that the net sealing robot can accurately control the tension of the wire during the compensation process, thereby improving the quality and efficiency of the net sealing operation.

[0101] S4. According to the tension distribution data, a finite element analysis method is used to simulate the fixing state of the network cable under different tensions to obtain a simulation result of the fixing state of the network cable.

[0102] In this embodiment, the step of simulating the fixing state of the network cable under different tensions by using a finite element analysis method according to the tension distribution data to obtain the simulation result of the fixing state of the network cable includes:

[0103] Collect the three-dimensional coordinate data of each fixed point of the network line on the power line, and construct a tetrahedral grid based on the three-dimensional coordinate data of each fixed point of the network line;

[0104] According to the tetrahedral mesh, the interaction force of the mesh wires at the intersection is simulated by using the finite difference method;

[0105] According to the interaction force of the network cables at the intersection, the deformation trend of the fixed point under different working conditions is obtained using a multi-layer perceptron.

[0106] Calculating the strain of the mesh wire at the intersection according to the deformation trend, and identifying the stress concentration area of ​​the intersection according to the strain of the mesh wire at the intersection using Hooke's law;

[0107] Collect vibration signals of fixed points of each network cable on the power line, extract the main vibration frequency from the vibration signal through Fourier transform, and use the main vibration frequency as the dynamic boundary condition;

[0108] According to the dynamic boundary conditions, the tension distribution data and the stress concentration area at the intersection, a finite element analysis method is used to simulate the fixed state of the network cable under different tensions to obtain a simulation result of the fixed state of the network cable.

[0109] Specifically, when the network sealing robot completes the installation of the network cable on the power line, it uses its built-in tension sensor to measure the tension components of each network cable fixed point in different directions along the power line one by one to form tension distribution data, and then uses GPS locators or laser rangefinders and other equipment to measure and record the three-dimensional coordinate data of each network cable fixed point on the power line. According to the collected three-dimensional coordinate data, the Delaunay triangulation algorithm is used to simulate the spatial distribution of the network cable fixed points, and a tetrahedral mesh is constructed. Based on the constructed tetrahedral mesh, the positions of all network cable intersections are identified in the tetrahedral mesh, and finite difference is used to calculate the position of each network cable intersection. The FDM sets a small calculation domain around each intersection, and simulates the interaction force of the wire mesh at the intersection by calculating the tension gradient of the grid cells in the domain. Each intersection has a corresponding force value. The interaction force data of the wire mesh at the intersection includes the magnitude and direction of the interaction force. Then, the interaction force data set of the intersection is used as input, and the multi-layer perceptron (MLP) neural network is used to predict the deformation trend of the fixed point under different working conditions. According to the deformation trend, the strain calculation formula in material mechanics is used to calculate the strain of the wire mesh at the intersection, and then the strain value is converted into a stress value according to Hooke's law. According to the stress value, the stress concentration area of ​​the intersection, such as the area with large strain, is identified. A vibration sensor is installed at each fixed point of the network cable, and the vibration sensor is used to collect the vibration signal of each fixed point of the network cable on the power line. The vibration signal is subjected to spectrum analysis by Fourier transform to extract the main vibration frequency of each fixed point of the network cable, and the main vibration frequency is used as the dynamic boundary condition in the finite element analysis method. The dynamic boundary condition is used to simulate the behavior of the network cable under the action of dynamic load. According to the tension distribution data, the dynamic boundary condition and the stress concentration area of ​​the intersection, the finite element analysis method (ANSYS) is used to simulate the fixed state of the network cable in the sealing process for the tetrahedral mesh. In the finite element analysis process, this embodiment can set the main vibration frequency of the network cable as the dynamic load according to the dynamic boundary condition, and define the action mode of the load. According to the tension distribution data, the tension of each fixed point is applied to the corresponding grid node as a static load, and the key area in the grid is refined according to the information of the stress concentration area of ​​the intersection to improve the accuracy of the analysis, so as to simulate the fixed state simulation results of the network cable under different tensions. The fixed state simulation results include the displacement of the fixed point of the network cable, the stress distribution and other information. These simulation results can be used to evaluate the sealing quality and optimize it.

[0110] S5. Obtaining tension fluctuation data during the network sealing operation, and determining network sealing quality optimization compensation parameters based on the network cable fixed state simulation result and the tension fluctuation data.

[0111] S6. Adaptively control the operating parameters of the network sealing robot based on the network sealing quality optimization compensation parameters.

[0112] In this embodiment, the step of determining the network sealing quality optimization compensation parameter based on the network cable fixing state simulation result and the tension fluctuation data includes:

[0113] Establishing a tension fluctuation and network sealing quality relationship model according to the network cable fixed state simulation result and the tension fluctuation data;

[0114] The displacement compensation gain is set according to the vibration response characteristics, and the temperature correction coefficient is calculated using the temperature influence coefficient according to the tension deviation and the ambient temperature data during the net sealing operation;

[0115] The temperature correction coefficient is combined with the displacement compensation gain, and the minimization of the sealing quality error is taken as the optimization goal. The sealing quality compensation parameters are iteratively optimized using a genetic algorithm until the preset convergence conditions are reached, and the optimized compensation parameters are output;

[0116] Generate compensation control instructions for a servo motor drive compensation device according to the optimized compensation parameters;

[0117] The reference trajectory of the servo motor is adjusted according to the compensation control instruction to obtain the motion trajectory of the servo motor driven compensation device;

[0118] The motion trajectory is simulated and analyzed, and the net sealing quality error caused by the tension fluctuation is evaluated using the tension fluctuation and net sealing quality relationship model, and the net sealing quality optimization compensation parameters are output.

[0119] The step of establishing a tension fluctuation and network sealing quality relationship model according to the network cable fixed state simulation result and the tension fluctuation data includes:

[0120] Extracting fixed point stress distribution data from the simulation result of the fixed state of the network cable, and reducing the dimension of the fixed point stress distribution data by using the principal component analysis method, and extracting the principal component of the fixed point stress distribution data greater than a preset stress threshold;

[0121] Calculating the stress transfer coefficient between different mesh cable fixing points according to the principal component, and performing weighted processing on the tension fluctuation data using the stress transfer coefficient to obtain tension fluctuation correction data;

[0122] Collecting ambient temperature data and vibration response data during the net sealing operation, integrating the tension fluctuation correction data, the ambient temperature data and the vibration response data, and constructing a net sealing quality scoring matrix using a hierarchical weighting method;

[0123] The regression analysis algorithm is used to correlate the tension fluctuation correction data with the net sealing quality scoring matrix, and a relationship model between tension fluctuation and net sealing quality is constructed.

[0124] Specifically, this embodiment extracts stress distribution data of each fixed point of the network cable from the simulation results of the fixed state of the network cable, and uses the principal component analysis (PCA) method to perform dimensionality reduction processing on the extracted fixed point stress distribution data. Assuming that there are 100 fixed points, this embodiment calculates the deviation of the stress value of each fixed point from its average value, and then calculates the covariance matrix of these deviations. By solving the eigenvalues ​​and eigenvectors of the covariance matrix, the first 10 principal components with the largest eigenvalues ​​are selected. The fixed point stress distribution data corresponding to these principal components are greater than a preset stress threshold (such as 50N / m²). By analyzing each The correlation between the principal components determines the mutual influence of stress between adjacent fixed points, and obtains the stress transfer coefficient between different fixed points of the network cable. The stress transfer coefficient describes the efficiency and characteristics of stress transfer between different fixed points when the network cable is subjected to external force. For example, the stress transfer coefficient from fixed point A to fixed point B is 0.7, which means that 70% of the stress change at point A will be transferred to point B. Then, these stress transfer coefficients are used to perform weighted processing on the tension fluctuation data to obtain tension fluctuation correction data. The ambient temperature data and vibration response data during the network sealing operation are collected. For example, the ambient temperature is 25℃, the vibration response data is 0.1m / s², these data are integrated with the tension fluctuation correction data, and the hierarchical weight method is used to construct a network sealing quality scoring matrix. The hierarchical weight method determines the degree of influence of different factors on the network sealing quality by assigning weights to different factors. In the hierarchical weight method, this embodiment determines the scoring indicators according to the actual needs of the network sealing quality, such as the stability of tension fluctuation, the suitability of ambient temperature, the severity of vibration response, etc., and establishes a hierarchical structure according to the logical relationship between the scoring indicators. The hierarchical structure includes a target layer (network sealing quality), a criterion layer (such as tension fluctuation, ambient temperature, vibration response, etc.) and an indicator layer (specific scoring indicators). The network sealing quality scoring matrix is ​​constructed according to the determined weights and scoring indicators. Each row of the network sealing quality scoring matrix represents a sample (such as a network sealing operation), and each column represents a scoring indicator and its corresponding weight. Then, a regression analysis algorithm (such as linear regression, polynomial regression, etc.) is used to associate the tension fluctuation correction data with the network sealing quality scoring matrix to construct a tension fluctuation and network sealing quality relationship model. For example, this embodiment can obtain a linear relationship between tension fluctuation and network sealing quality score through linear regression analysis: y = 2x + 10, where y is the sealing quality score and x is the tension fluctuation correction data.

[0125] This embodiment measures the vibration response characteristics of the network sealing robot under different frequencies and amplitude excitations during operation. The vibration response characteristics include key parameters such as vibration frequency, amplitude and duration. According to the vibration response characteristics, the target area that needs displacement compensation is determined, and the displacement compensation gain is set according to the target area that needs displacement compensation. For example, the initial displacement compensation gain is set to 0.5. At the same time, according to the tension deviation (the difference between the actual tension and the expected tension) and the ambient temperature data during the network sealing operation, the temperature correction coefficient is calculated using the temperature influence coefficient (for example, the tension deviation increases by 0.1N for every 1°C increase). For example, if the ambient temperature is 30°C, which is 5°C higher than the reference temperature of 25°C, the temperature correction coefficient is 0.5. The temperature correction coefficient is combined with the displacement compensation gain, and the network sealing quality compensation parameters are iteratively optimized using a genetic algorithm with the goal of minimizing the network sealing quality error. The specific process is as follows:

[0126] In this embodiment, the temperature correction coefficient and the displacement compensation gain are weighted and summed to obtain a comprehensive compensation coefficient, for example, the comprehensive compensation coefficient = temperature correction coefficient × 0.7 + displacement compensation gain × 0.3, and then a group of compensation parameter combinations are randomly generated as the initial population, each individual represents a group of compensation parameters, and the compensation parameters also include the weights and proportional factors of the temperature correction coefficient and the displacement compensation gain. For each individual, the comprehensive compensation coefficient is used to calculate the sealing quality error, and the sealing quality error is used as the fitness value of the individual. The smaller the fitness value, the better the sealing quality. According to the fitness value, individuals with higher fitness are selected to enter the next generation population, and the selected individuals are cross-operated to generate new offspring individuals. For example, the temperature correction coefficient weights and displacement compensation gain weights of two individuals are exchanged, and the offspring individuals are mutated to randomly change a certain compensation parameter of the individual to increase the population diversity. The above steps are repeated until the maximum number of iterations is reached, and the optimized compensation parameters corresponding to the optimal individual are output. These parameters can better adapt to the influence of tension fluctuations and temperature changes on the sealing quality.

[0127] This embodiment generates compensation control instructions for the servo motor drive compensation device according to the optimized compensation parameters. For example, if the optimized compensation parameters are a displacement compensation gain of 0.6 and a temperature correction coefficient of 0.45, these parameters are converted into control instructions for the servo motor, such as setting the rotation speed, acceleration and other parameters of the servo motor to achieve the desired displacement compensation and temperature correction. In the absence of compensation control instructions, the servo motor usually moves along a preset reference trajectory. This embodiment adjusts the reference trajectory of the servo motor according to the compensation control instructions. For example, if the original reference trajectory is a linear motion, it is adjusted to a curved motion with a certain compensation amount according to the compensation control instructions. Then, this embodiment maps the adjusted reference trajectory to the compensation device. Based on the adjustment The control unit will calculate the motion trajectory of the compensation device (servo motor driven compensation device) at different time points based on the reference trajectory and the motion model of the compensation device. This motion trajectory includes information such as the position, velocity and acceleration of the compensation device at different time points. The motion trajectory of the servo motor driven compensation device is simulated and analyzed, and the tension fluctuation and net sealing quality relationship model is used to evaluate the net sealing quality error caused by tension fluctuation. For example, the net sealing quality error is 0.05N obtained through simulation analysis, which is less than the preset net sealing quality error threshold (such as 0.1N). It is considered that the optimized compensation parameters are effective. Finally, the net sealing quality optimized compensation parameters are output. The net sealing quality optimized compensation parameters will be used to guide the actual operation of the net sealing robot to improve the quality and efficiency of net sealing.

[0128] The embodiment of the present invention provides a network sealing quality control method based on a network sealing robot, the method captures original tension data of a power line in a vibration environment, and filters the original tension data to obtain tension filtering data; analyzes the thermal expansion and contraction effect of the conductor according to the collected conductor surface temperature data, and obtains the conductor expansion and contraction caused by temperature change; controls the tension to return to a preset tension range through a proportional-integral-differential control algorithm according to the conductor expansion and contraction and tension filtering data, and after the tension control is completed, collects the tension distribution data of each network cable fixed point installed by the network sealing robot on the power line; simulates the fixed state of the network cable under different tensions using a finite element analysis method according to the tension distribution data, and obtains a simulation result of the fixed state of the network cable; obtains the tension fluctuation data during the network sealing operation, and determines the network sealing quality optimization compensation parameters based on the network cable fixed state simulation result and the tension fluctuation data; and adaptively controls the operation parameters of the network sealing robot based on the network sealing quality optimization compensation parameters. Compared with the existing technology, this method achieves stable and efficient conductor sealing under vibration environment and temperature changes by combining PID control algorithm and finite element analysis simulation technology. It can accurately control the tension in the conductor sealing process, which not only improves the firmness and uniformity of the network cable fixation, but also improves the quality and efficiency of the sealing operation, ensuring the safety of power grid operation.

[0129] It should be noted that the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0130] In one embodiment, Figure 2 As shown, an embodiment of the present invention provides a network sealing quality control system based on a network sealing robot, the system comprising:

[0131] The data acquisition module 101 is used to capture the original tension data of the power line in a vibration environment, and filter the original tension data to obtain tension filtering data;

[0132] The temperature analysis module 102 is used to analyze the thermal expansion and contraction effect of the conductor according to the collected conductor surface temperature data, and obtain the expansion and contraction amount of the conductor caused by the temperature change;

[0133] The tension control module 103 is used to control the tension to return to a preset tension range through a proportional-integral-differential control algorithm according to the wire expansion and contraction amount and the tension filtering data, and after the tension control is completed, collect the tension distribution data of each network cable fixed point installed by the network sealing robot on the power line;

[0134] A fixing analysis module 104 is used to simulate the fixing state of the network cable under different tensions using a finite element analysis method according to the tension distribution data to obtain a simulation result of the fixing state of the network cable;

[0135] The optimization compensation module 105 is used to obtain the tension fluctuation data during the network sealing operation, and determine the network sealing quality optimization compensation parameters based on the simulation result of the fixed state of the network cable and the tension fluctuation data;

[0136] The compensation control module 106 is used to adaptively control the operating parameters of the network sealing robot based on the network sealing quality optimization compensation parameters.

[0137] In this embodiment, the data acquisition module 101 is specifically used for:

[0138] According to the mean and standard deviation of the original tension data, using a preset abnormal threshold to filter out abnormal data points from the original tension data;

[0139] According to the cubic spline interpolation method, the abnormally excluded data points in the original tension data are repaired by interpolating adjacent valid data points to obtain a repair signal;

[0140] Performing a fast Fourier transform on the repair signal to obtain a frequency spectrum, and determining a main frequency component of the vibration according to the frequency spectrum;

[0141] According to the main frequency component of vibration, the repair signal is filtered by using a third-order Butterworth bandpass filter to obtain a smooth signal.

[0142] The Daubechies fourth-order wavelet is used to decompose the smooth signal into four layers to obtain wavelet coefficients of different scales, and the wavelet coefficients are subjected to threshold shrinkage processing to reconstruct tension filter data.

[0143] For the specific definition of a network blocking quality control system based on a network blocking robot, please refer to the above-mentioned definition of a network blocking quality control method based on a network blocking robot, which will not be repeated here. A person of ordinary skill in the art can appreciate that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0144] The embodiment of the present invention provides a network sealing quality control system based on a network sealing robot, wherein the system captures original tension data of a power line in a vibration environment through a data acquisition module, and filters the original tension data to obtain tension filtering data; the temperature analysis module analyzes the thermal expansion and contraction effect of the conductor according to the collected conductor surface temperature data, and obtains the expansion and contraction of the conductor caused by temperature change; the tension control module controls the tension to return to a preset tension range through a proportional-integral-differential control algorithm according to the expansion and contraction of the conductor and the tension filtering data, and after the tension control is completed, collects the tension distribution data of each network cable fixed point installed by the network sealing robot on the power line; the fixed analysis module simulates the fixed state of the network cable under different tensions using a finite element analysis method according to the tension distribution data, and obtains a simulation result of the fixed state of the network cable; the optimization compensation module obtains the tension fluctuation data during the network sealing operation, and determines the network sealing quality optimization compensation parameters based on the network cable fixed state simulation result and the tension fluctuation data; the compensation control module adaptively controls the operating parameters of the network sealing robot based on the network sealing quality optimization compensation parameters. Compared with the existing technology, this system achieves stable and efficient conductor sealing in a vibration environment and temperature changes by combining PID control algorithm and finite element analysis simulation technology. It can accurately control the tension during the conductor sealing process, which not only improves the firmness and uniformity of the network cable fixation, but also improves the quality and efficiency of the sealing operation, ensuring the safety of the power grid operation.

[0145] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A network sealing quality control method based on a network sealing robot, characterized in that: The following steps are involved: Capturing original tension data of the power line in a vibration environment, and filtering the original tension data to obtain tension filtering data; The thermal expansion and contraction effect of the conductor is analyzed based on the collected conductor surface temperature data to obtain the conductor expansion and contraction amount caused by temperature change; According to the wire expansion and contraction amount and the tension filtering data, the tension is controlled to return to the preset tension range through the proportional-integral-differential control algorithm, and after the tension control is completed, the tension distribution data of each network cable fixed point installed by the network sealing robot on the power line is collected; According to the tension distribution data, a finite element analysis method is used to simulate the fixed state of the network cable under different tensions to obtain a simulation result of the fixed state of the network cable; Acquire tension fluctuation data during the net sealing operation, and determine net sealing quality optimization compensation parameters based on the simulation result of the fixed state of the net cable and the tension fluctuation data; The operating parameters of the network sealing robot are adaptively controlled based on the network sealing quality optimization compensation parameters.

2. A network sealing quality control method based on a network sealing robot as claimed in claim 1, characterized in that: The step of filtering the original tension data to obtain tension filter data comprises: According to the mean and standard deviation of the original tension data, using a preset abnormal threshold to filter out abnormal data points from the original tension data; According to the cubic spline interpolation method, the abnormally excluded data points in the original tension data are repaired by interpolating adjacent valid data points to obtain a repair signal; Performing a fast Fourier transform on the repair signal to obtain a frequency spectrum, and determining a main frequency component of the vibration according to the frequency spectrum; According to the main frequency component of vibration, the repair signal is filtered by using a third-order Butterworth bandpass filter to obtain a smooth signal. The Daubechies fourth-order wavelet is used to decompose the smooth signal into four layers to obtain wavelet coefficients of different scales, and the wavelet coefficients are subjected to threshold shrinkage processing to reconstruct tension filter data.

3. A network sealing quality control method based on a network sealing robot as claimed in claim 1, characterized in that: The step of analyzing the thermal expansion and contraction effect of the wire according to the collected wire surface temperature data to obtain the wire expansion and contraction amount caused by temperature change includes: Using a sliding average window to perform zero drift correction on the surface temperature data of the conductor to obtain calibration temperature data; Calculating a temperature change rate according to the calibration temperature data, and if the temperature change rate is higher than a preset temperature change threshold, converting the temperature change into an initial expansion amount according to a temperature expansion coefficient of the conductor material; The initial expansion and contraction is subjected to stress compensation to obtain the expansion and contraction of the wire caused by temperature change.

4. A network sealing quality control method based on a network sealing robot as claimed in claim 1, characterized in that: The step of controlling the tension to return to a preset tension range through a proportional-integral-differential control algorithm according to the wire expansion and contraction amount and the tension filtering data comprises: Calculating the displacement compensation amount required for an ideal tension state according to the wire expansion and contraction amount and the tension filtering data; The compensation preload is calculated based on the displacement compensation amount, and the target motion parameter that the servo motor driven compensation device should achieve is determined based on the compensation preload and the current network cable tension; According to the target motion parameters that the servo motor should achieve, the optimal control parameters of the proportional-integral-differential control algorithm are calculated using the genetic algorithm; Collect the current tension data during the net sealing operation, and calculate the root mean square value of the current tension data within the time window to obtain the tension fluctuation amplitude; Comparing the tension fluctuation amplitude with a preset tension reference value to obtain a tension deviation; According to the optimal control parameters, the proportional-integral-differential control algorithm is used to perform integral and differential operations on the tension deviation to obtain an original compensation amount, and the tension is controlled to return to a preset tension range according to the original compensation amount.

5. A network sealing quality control method based on a network sealing robot as claimed in claim 4, characterized in that: The step of controlling the tension to return to a preset tension range through a proportional-integral-differential control algorithm according to the wire expansion and contraction amount and the tension filtering data also includes: The real-time tension data of the servo motor driven compensation device during the compensation process is continuously monitored by the tension sensor; The stiffness attenuation is calculated according to the real-time tension data and the predetermined expected tension value, and the optimal control parameters of the proportional-integral-differential control algorithm are updated and adjusted according to the stiffness attenuation.

6. A network sealing quality control method based on a network sealing robot as claimed in claim 1, characterized in that: The step of simulating the fixed state of the network cable under different tensions by using a finite element analysis method according to the tension distribution data to obtain the simulation result of the fixed state of the network cable comprises: Collect the three-dimensional coordinate data of each fixed point of the network line on the power line, and construct a tetrahedral grid based on the three-dimensional coordinate data of each fixed point of the network line; According to the tetrahedral mesh, the interaction force of the mesh wires at the intersection is simulated by using the finite difference method; According to the interaction force of the network cables at the intersection, the deformation trend of the fixed points under different working conditions is obtained using a multi-layer perceptron. Calculating the strain of the mesh wire at the intersection according to the deformation trend, and identifying the stress concentration area of ​​the intersection according to the strain of the mesh wire at the intersection using Hooke's law; Collect vibration signals of fixed points of each network cable on the power line, extract the main vibration frequency from the vibration signal through Fourier transform, and use the main vibration frequency as the dynamic boundary condition; According to the dynamic boundary conditions, the tension distribution data and the stress concentration area at the intersection, a finite element analysis method is used to simulate the fixed state of the network cable under different tensions to obtain a simulation result of the fixed state of the network cable.

7. A network sealing quality control method based on a network sealing robot as claimed in claim 4, characterized in that: The step of determining the net sealing quality optimization compensation parameter based on the simulation result of the net cable fixing state and the tension fluctuation data comprises: Establishing a tension fluctuation and network sealing quality relationship model according to the network cable fixed state simulation result and the tension fluctuation data; The displacement compensation gain is set according to the vibration response characteristics, and the temperature correction coefficient is calculated using the temperature influence coefficient according to the tension deviation and the ambient temperature data during the net sealing operation; The temperature correction coefficient is combined with the displacement compensation gain, and the minimization of the sealing quality error is taken as the optimization goal. The sealing quality compensation parameters are iteratively optimized using a genetic algorithm until the preset convergence conditions are reached, and the optimized compensation parameters are output; Generate compensation control instructions for a servo motor drive compensation device according to the optimized compensation parameters; The reference trajectory of the servo motor is adjusted according to the compensation control instruction to obtain the motion trajectory of the servo motor driven compensation device; The motion trajectory is simulated and analyzed, and the net sealing quality error caused by the tension fluctuation is evaluated using the tension fluctuation and net sealing quality relationship model, and the net sealing quality optimization compensation parameters are output.

8. A network sealing quality control method based on a network sealing robot as claimed in claim 7, characterized in that: The step of establishing a tension fluctuation and network sealing quality relationship model according to the network cable fixed state simulation result and the tension fluctuation data comprises: Extracting fixed point stress distribution data from the simulation result of the fixed state of the network cable, and reducing the dimension of the fixed point stress distribution data by using the principal component analysis method, and extracting the principal component of the fixed point stress distribution data greater than a preset stress threshold; Calculating the stress transfer coefficient between different mesh cable fixing points according to the principal component, and performing weighted processing on the tension fluctuation data using the stress transfer coefficient to obtain tension fluctuation correction data; Collecting ambient temperature data and vibration response data during the net sealing operation, integrating the tension fluctuation correction data, the ambient temperature data and the vibration response data, and constructing a net sealing quality scoring matrix using a hierarchical weighting method; The regression analysis algorithm is used to correlate the tension fluctuation correction data with the net sealing quality scoring matrix, and a relationship model between tension fluctuation and net sealing quality is constructed.

9. A network sealing quality control system based on a network sealing robot, characterized in that: The system comprises: A data acquisition module is used to capture the original tension data of the power line in a vibration environment, and filter the original tension data to obtain tension filtering data; The temperature analysis module is used to analyze the thermal expansion and contraction effect of the wire according to the collected wire surface temperature data, and obtain the wire expansion and contraction amount caused by temperature change; A tension control module, used to control the tension to return to a preset tension range through a proportional-integral-differential control algorithm according to the wire expansion and contraction amount and the tension filtering data, and after the tension control is completed, collect the tension distribution data of each network cable fixed point installed by the network sealing robot on the power line; A fixing analysis module, used to simulate the fixing state of the network cable under different tensions by using a finite element analysis method according to the tension distribution data, and obtain a simulation result of the fixing state of the network cable; An optimization compensation module, used to obtain tension fluctuation data during the network sealing operation, and determine network sealing quality optimization compensation parameters based on the simulation result of the fixed state of the network cable and the tension fluctuation data; The compensation control module is used to adaptively control the operating parameters of the network sealing robot based on the optimization of compensation parameters of the network sealing quality.

10. A network sealing quality control system based on a network sealing robot as claimed in claim 9, characterized in that: The data acquisition module is specifically used for: According to the mean and standard deviation of the original tension data, using a preset abnormal threshold to filter out abnormal data points from the original tension data; According to the cubic spline interpolation method, the abnormally excluded data points in the original tension data are repaired by interpolating adjacent valid data points to obtain a repair signal; Performing a fast Fourier transform on the repair signal to obtain a frequency spectrum, and determining a main frequency component of the vibration according to the frequency spectrum; According to the main frequency component of vibration, the repair signal is filtered by using a third-order Butterworth bandpass filter to obtain a smooth signal. The Daubechies fourth-order wavelet is used to decompose the smooth signal into four layers to obtain wavelet coefficients of different scales, and the wavelet coefficients are subjected to threshold shrinkage processing to reconstruct tension filter data.

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