Linear single-string insulator string replacement mechanism control method and system

Abstract

The invention relates to the technical field of automatic control of power equipment maintenance and replacement, and discloses a linear single insulator string replacement mechanism control method and system, and the method comprises the steps: obtaining the state data of a first object; performing interference cleaning of a first feature on the state data, and transmitting the cleaned data to a second object; and the second object processes the cleaned data and controls the work of the first object. And through an intelligent algorithm and a force position compliance control technology, accurate control and environment adaptability of insulator string replacement operation are realized, and the operation efficiency and precision are remarkably improved. Compared with a traditional method, the device is light and can be flexibly operated under complex working conditions, damage of hard impact to the insulator is reduced, and operation safety is improved. Meanwhile, the manual labor intensity and the operation risk are reduced, and the requirements for high precision and high safety of intelligent maintenance of the power grid are met.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control of maintenance and replacement of electric power equipment, and in particular to a control method and system for a linear single-string insulator string replacement mechanism. Background Art

[0002] With the continuous improvement of the modernization and automation level of the power system, the maintenance and replacement efficiency of power equipment has put forward higher requirements for the stability and safety of the power grid. As a key component in the transmission line, the main function of the insulator string is to isolate the conductive parts from the grounding parts to ensure the safety and stability of power transmission. For the replacement of straight single-string insulator strings with voltage levels of 35kV and below, manual or traditional mechanical equipment is currently widely used. However, the traditional method has obvious limitations, mainly manifested in bulky equipment, complex operation, high labor intensity, and high safety risks. Especially in operations at high altitudes or in narrow spaces, the work efficiency is low and the accuracy is difficult to guarantee. In recent years, automated and intelligent insulator replacement technology has gradually received attention, and some solutions based on robot control, flexible control and intelligent algorithms have emerged one after another, in order to solve the shortcomings of traditional operation methods and improve the safety, accuracy and efficiency of replacement operations.

[0003] Although the existing automated replacement technology has alleviated the limitations of traditional methods to a certain extent, there are still many shortcomings. First, most of the current automated replacement equipment can only achieve simple motion control, and it is difficult to maintain stable operating accuracy under complex working conditions; second, the existing technology is weak in environmental adaptability and lacks the ability to respond to environmental changes in real time, especially in scenarios with a large number of insulators or complex working conditions, and cannot guarantee flexibility and accuracy. Traditional automation equipment lacks high-precision real-time feedback mechanisms and adaptive control strategies, and is prone to problems such as insulator damage and equipment wear, and even poses a high safety risk. With the development of intelligent power grids, higher accuracy and safety requirements are put forward for insulator replacement operations. Therefore, there is an urgent need for a new technical solution to achieve accurate, safe, and more efficient replacement operations of insulator strings by integrating force sensors, intelligent control algorithms, and force-position compliance control technology. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing insulator string replacement process has the problems of heavy equipment, difficult flexible operation, low efficiency, and long time consumption, especially when there are many insulators or complex working conditions, the replacement operation is difficult.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a control method for a linear single-string insulator string replacement mechanism, comprising:

[0007] Obtaining status data of a first object;

[0008] performing interference cleaning of a first characteristic on the state data, and transmitting the cleaned data to a second object;

[0009] The second object processes the cleaned data and controls the work of the first object.

[0010] As a preferred solution of the control method of the straight single-string insulator string replacement mechanism described in the present invention, wherein: the state data includes data that can reflect the working state of the first object.

[0011] As a preferred solution of the control method of the straight single-string insulator string replacement mechanism described in the present invention, wherein: performing interference cleaning of the first characteristic on the state data includes filtering the state data to obtain stable and accurate state data.

[0012] As a preferred solution of the control method of the straight single-string insulator string replacement mechanism described in the present invention, the second object processes the cleaned data including designing a control algorithm based on the cleaned data to control the operation of the first object.

[0013] As a preferred solution of the control method of the straight single-string insulator string replacement mechanism described in the present invention, wherein: the control of the work of the first object includes adjusting the working parameters of the first object using a control algorithm to make the first object adapt to the working environment and improve the working accuracy of the first object.

[0014] As a preferred solution of the control method of the linear single-string insulator string replacement mechanism of the present invention, the interference cleaning of the first feature of the state data includes but is not limited to filtering the state data using a Kalman filter, and the formula is expressed as:

[0015]

[0016] P ( k+1|k ) =ΦP ( k|k ) Φ T +ΓQΓ T

[0017] K(k+1)=P(k+1|k ) H T [ HP(k+1|k)H T +R ] -1

[0018] Wherein, F0(k) represents the torque state variable at discrete time step k, F0(k+1) represents the prediction of F0(k) at the next moment; a and b represent the coefficient parameters in the Kalman filter equation, Γ represents the coefficient matrix of process noise W(k), W(k) represents process noise, F(k) represents the measured torque data at moment k, H represents the observation matrix, V(k) represents the observation noise, Q represents the variance of process noise W(k), R represents the variance of observation noise V(k), P(k+1|k) represents the covariance prediction matrix at moment k for moment k+1, Φ represents the state transfer matrix, P(k|k) represents the covariance matrix at moment k, and K(k+1) represents the Kalman gain matrix I n Represents the identity matrix.

[0019] As a preferred solution of the control method of the linear single-string insulator string replacement mechanism of the present invention, wherein:

[0020] The control algorithm includes, but is not limited to, a fuzzy control algorithm;

[0021] Through the fuzzy control algorithm, the first object is adapted to the working environment and the working accuracy of the first object is improved. The formula is expressed as:

[0022]

[0023] Where M is the inertia coefficient, ΔX(t) is the displacement error or deviation, and X d (t) represents the expected position, X(t) represents the current actual position, represents the rate of change of displacement error, B represents the damping coefficient, K represents the stiffness coefficient, and F z (t) represents external force or control force.

[0024] A linear single-string insulator string replacement mechanism control system, wherein:

[0025] A data acquisition module, for acquiring status data of the first object;

[0026] an analysis module, performing interference cleaning of a first feature on the state data, and transmitting the cleaned data to a second object;

[0027] The control module, the second object processes the cleaned data and controls the work of the first object.

[0028] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0029] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of any one of the methods of the present invention.

[0030] Beneficial effects of the invention: The control method for the replacement mechanism of a linear single-string insulator provided by the invention realizes precise control and environmental adaptability of the replacement operation of the insulator string through intelligent algorithms and force-position compliance control technology, significantly improving the operation efficiency and accuracy. Compared with the traditional method, the device of the invention is light and can be flexibly operated under complex working conditions, reducing the damage to the insulator caused by hard impact and improving the safety of the operation. At the same time, it reduces the labor intensity and operation risks of manual labor, meeting the high-precision and high-safety requirements of intelligent maintenance of power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0032] Figure 1 An overall flow chart of a control method for a linear single-string insulator string replacement mechanism provided by the first embodiment of the present invention;

[0033] Figure 2 A comparison curve diagram of different filtering algorithms for a control method for a straight single-string insulator string replacement mechanism provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. The embodiments of the present invention and all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0035] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a control method for a linear single-string insulator string replacement mechanism, comprising:

[0036] S1: Obtain status data of a first object.

[0037] In one implementation of the present application, the first object refers to a mechanical device for replacing an insulator string, including a gripping device and a main support arm, which are responsible for directly operating and supporting the insulator string. The second object is a control system, which is responsible for receiving status data from the first object, processing and analyzing it, and adjusting the operating parameters of the first object in real time according to the analysis results to ensure the accuracy and safety of the replacement process. This control method realizes intelligent insulator string replacement operations through the coordinated work of the mechanical device and the control system.

[0038] In an optional embodiment, the first object may be a camera monitoring device; the second object may be a visual processing system, and the monitoring device may collect image data of the insulator string in real time and transmit it to the visual processing system. The visual processing system may analyze the image data and identify the exact position and state of the insulator string to assist the mechanical device in performing precise positioning and grasping operations.

[0039] The first object can also be an environmental sensor module (such as temperature, humidity, and wind speed sensors); the second object is an environmental monitoring and analysis system. The environmental sensor module monitors the surrounding temperature, humidity, wind speed and other environmental data in real time and transmits them to the environmental monitoring system. The system determines whether safe operating conditions are met based on data analysis, and delays or stops the replacement operation when necessary to ensure operational safety.

[0040] The first object can also be a current and voltage sensor; the second object is an electrical safety monitoring system. The sensor collects electrical parameters in real time and transmits them to the monitoring system. The system analyzes the sensor data to detect whether there are abnormal current or voltage fluctuations. If there are any abnormalities, it will send an alarm to the control system or terminate the operation to prevent equipment damage or personal injury.

[0041] The first object can also be a mobile robotic arm; the second object is a path planning and navigation system. Under the guidance of the navigation system, the mobile robotic arm moves along the planned path to the designated insulator position, avoids obstacles and accurately locates the operation point, thereby realizing automated and safe insulator replacement operations.

[0042] The status data includes data that can reflect the working status of the first object.

[0043] Furthermore, through real-time monitoring and feedback, the system can fully grasp the working status of mechanical devices or sensor modules, provide basic data for intelligent control, and significantly improve the accuracy of operations. The acquisition of status data also improves the safety of the system, can identify potential safety hazards, and suspend operations when necessary, reducing the risk of equipment damage and personal injury. In addition, the design enhances the intelligence and environmental adaptability of the system, and ensures that the system can operate adaptively under complex working conditions by analyzing the environment and equipment status in real time. The accumulation of status data also facilitates fault diagnosis and maintenance, quickly identifies and solves potential problems, and extends the service life of equipment. This design provides a guarantee for intelligent and automated insulator replacement operations, which meets the needs of modern power maintenance systems for efficient and safe operations.

[0044] S2: Clean the interference of the first feature on the state data, and transmit the cleaned data to the second object.

[0045] Analyzing the status data includes processing the status data and performing analysis using the processed data.

[0046] Performing interference cleaning of the first characteristic on the state data includes filtering the state data to obtain stable and accurate state data.

[0047] In one implementation of the present application, a Kalman filter is used to filter the state data, and the formula is expressed as:

[0048]

[0049] P(k+1|k)=ΦP(k|k)Φ T +ΓQΓ T

[0050] K(k+1)=P(k+1|k ) H T [ HP(k+1|k)H T +R ] -1

[0051] Wherein, F0(k) represents the torque state variable at discrete time step k, F0(k+1) represents the prediction of F0(k) at the next moment; a and b represent the coefficient parameters in the Kalman filter equation, Γ represents the coefficient matrix of process noise W(k), W(k) represents process noise, F(k) represents the measured torque data at moment k, H represents the observation matrix, V(k) represents the observation noise, Q represents the variance of process noise W(k), R represents the variance of observation noise V(k), P(k+1|k) represents the covariance prediction matrix at moment k for moment k+1, Φ represents the state transfer matrix, P(k|k) represents the covariance matrix at moment k, and K(k+1) represents the Kalman gain matrix I n Represents the identity matrix.

[0052] In an optional embodiment, a particle filter algorithm can be used to clean the interference of the first feature of the state data. The particle filter represents the probability distribution of the state data through a set of randomly sampled particles, and is suitable for noise and interference filtering in nonlinear and non-Gaussian environments. First, the particle filter generates an initial particle set to represent the probability distribution of the state, and predicts the particle position through a state transition model to simulate the uncertainty of the environment. After measurement, the weight of each particle is adjusted according to the degree of proximity to the measured value, and low-weight particles are removed by resampling to maintain the accuracy of the particle set. Finally, the weighted average of the particle set forms a stable state estimate, which achieves effective cleaning of environmental noise, process noise and nonlinear interference.

[0053] Furthermore, filtering technology is used to ensure that the mechanical device obtains stable and accurate status data during the replacement of the insulator string, thereby achieving precise control and efficient compliant operation. The filter can effectively remove environmental noise and interference, such as adverse factors such as high-altitude wind and vibration, ensure the clarity and stability of sensor data, and improve the response accuracy of the control system, so that the device can accurately perform compliant control and avoid excessive impact or overload. Through algorithms such as Kalman filtering and particle filtering, the present invention enhances the robustness of the system, enabling it to cope with dynamic and complex working conditions. The filter provides data support for real-time dynamic adjustment, ensuring that the control system responds quickly and stably, making the insulator replacement process smoother and safer. In addition, filtering technology ensures the accuracy of force feedback and displacement data, so that the gripping device can achieve precise compliant control when contacting the insulator, protecting the insulator and equipment. These advantages together improve the accuracy, safety and efficiency of the present invention in insulator replacement operations.

[0054] Furthermore, through a variety of intelligent algorithms and control algorithms, the state data is processed and analyzed in real time, which significantly improves the environmental adaptability and working accuracy of the mechanical device. In terms of state data processing, intelligent algorithms such as Kalman filtering, particle filtering, adaptive neural network, fuzzy logic control and extended Kalman filtering can accurately estimate, predict and correct state data according to different working conditions, thereby ensuring the stability and accuracy of the data. In terms of control, a variety of strategies such as fuzzy control, adaptive control, neural network control, sliding mode control and genetic algorithm optimization are adopted, which can automatically adjust the control parameters according to environmental changes, improve the stability and anti-interference of the system in nonlinear and dynamic environments, and make the mechanical device have higher accuracy, flexibility and safety during the insulator replacement process. Overall, this multi-algorithm integrated design effectively improves the robustness and intelligence level of the system, so that the device can maintain stable operating performance under various complex working conditions.

[0055] S3: The second object processes the cleaned data and controls the work of the first object.

[0056] In one implementation of the present application, the control algorithm is a fuzzy control algorithm;

[0057] Through the fuzzy control algorithm, the first object is adapted to the working environment and the working accuracy of the first object is improved. The formula is expressed as:

[0058]

[0059] Where M is the inertia coefficient, ΔX(t) is the displacement error or deviation, and X d (t) represents the expected position, X(t) represents the current actual position, represents the rate of change of displacement error, B represents the damping coefficient, K represents the stiffness coefficient, and F z (t) represents external force or control force.

[0060] The control algorithm is used to ensure that the first object can achieve high-precision operation and environmental adaptability in complex and dynamic environments. Insulator string replacement operations are usually carried out in complex working conditions outdoors or at high altitudes. The variability and uncertainty of the environment bring random disturbances to the operation. Therefore, the fuzzy control algorithm can cope with the uncertainty and randomness in the working conditions and improve the stability and accuracy of the operation through fuzzy processing and flexible control. Fuzzy control can dynamically adjust control parameters such as inertia coefficient, damping coefficient and stiffness coefficient according to real-time feedback status information, thereby realizing flexible parameter adjustment and enabling the system to adaptively maintain the optimal state. In addition, the fuzzy control algorithm enhances the anti-interference ability. When subjected to external disturbances and load changes, it quickly returns to a stable state by adjusting the output torque to ensure the safety of the operation. As an intelligent control method, fuzzy control also reduces the need for human intervention, improves the automation and intelligence level of the operation, and fully meets the high-precision and high-adaptability requirements of insulator string replacement work.

[0061] In an optional embodiment, the control algorithm can also be an adaptive control algorithm, which adjusts the control parameters online to enable the system to self-optimize according to changes in the working environment and operating conditions. The algorithm will evaluate the intensity and frequency of environmental disturbances (such as wind speed and vibration) in real time, and dynamically adjust parameters such as inertia and damping to ensure that the gripping device maintains stable operation under dynamic conditions. Through adaptive adjustment, the system can self-adjust in non-preset situations, maintain the equipment in the best operating state, and improve the stability and safety of the operation.

[0062] In an optional embodiment, the control algorithm can also be a neural network control algorithm, which uses a deep learning model to train and learn the system's state data. Based on a large amount of historical operating data and operating condition information, the neural network establishes a nonlinear control model to identify the relationship between environmental patterns and system responses. This algorithm is particularly suitable for complex, nonlinear environments. Through online learning, the control output, such as gripping force and position, is adjusted in real time, so that the system can accurately respond to changes and achieve high-precision compliant control. The neural network can also predict future states and improve the foresight of the control system.

[0063] In an optional embodiment, the control algorithm can also be a sliding mode control algorithm, which realizes the suppression of load and external interference by designing a switching control strategy. The algorithm sets a sliding surface (i.e., the switching boundary of the state variable). When the system state deviates from the sliding surface, a forced regression mechanism is triggered to quickly return the system to a balanced state. Sliding mode control is suitable for handling rapidly changing working conditions and has strong resistance to parameter fluctuations and interference. This is manifested in the insulator replacement operation as an agile response to sudden disturbances such as vibration and wind, ensuring the stable operation of the system.

[0064] In an optional embodiment, the control algorithm can also be a genetic algorithm optimization control, which searches for the optimal control parameters by simulating the biological evolution process. The initial control parameters are used as a "population", and the optimal parameter set is iterated through operations such as "selection", "crossover" and "mutation". The genetic algorithm generates a set of candidate solutions each time, applies them to the system, determines their performance through feedback, and continuously optimizes the control parameters to ensure that the system obtains the best control effect under complex working conditions. This optimization mechanism not only improves operating efficiency, but also finds the optimal path in different working conditions, reduces equipment wear and extends its service life.

[0065] Based on the results of the analysis, the control system optimizes and adjusts the operating parameters of the first object to adapt it to the current working environment and effectively reduce operating errors, thereby significantly improving the working accuracy of the first object. This process ensures that the mechanical device can accurately perform operations under complex or dynamic conditions, reduces the risks and errors in the process of replacing insulator strings, and achieves more stable and safe working results.

[0066] Furthermore, through real-time processing and analysis of state data by a variety of intelligent algorithms and control algorithms, the environmental adaptability and working accuracy of the mechanical device are significantly improved. In terms of state data processing, Kalman filtering and particle filtering can accurately estimate, predict and correct state data according to different working conditions, thereby ensuring the stability and accuracy of the data. In terms of control, a variety of strategies such as fuzzy control, adaptive control, neural network control, sliding mode control and genetic algorithm optimization are adopted, which can automatically adjust the control parameters according to environmental changes, improve the stability and anti-interference of the system in nonlinear and dynamic environments, and make the mechanical device have higher accuracy, flexibility and safety during the insulator replacement process. Overall, this multi-algorithm integrated design effectively improves the robustness and intelligence level of the system, so that the device can maintain stable operating performance under various complex working conditions.

[0067] Embodiment 2 is an embodiment of the present invention, which provides a linear single-string insulator string replacement mechanism control system, including:

[0068] The data acquisition module acquires status data of the first object.

[0069] The analysis module transmits the state data to the second object and analyzes the state data.

[0070] The control module improves the working accuracy of the first object based on the analyzed result.

[0071] Embodiment 3, an embodiment of the present invention, integrates high-precision force sensors on the grasping device and the main support arm to monitor the grasping force and the support force in real time, and provide accurate force feedback information for the control system. Based on the force sensor data, a force-position compliance control algorithm is designed to achieve the compliance of the grasping device when contacting the insulator by adjusting the output torque and position of the drive unit, avoiding hard impact and overload, and protecting the safety of the insulator and equipment.

[0072] A Torque disturbance based on Kalman filtering

[0073] First, the initial torque value F is filtered using the Kalman filter. Its equation can be expressed as:

[0074]

[0075] According to the frequency-related properties of data sampling, we can get F0(k+1) and F0(k), F0(k-1) for parameters a and b.

[0076] The equation is transformed into:

[0077]

[0078]

[0079] In the above equation, W(k) is regarded as white noise, and V(k) is the disturbance caused by the ripple related to the time k. The average values ​​of W(t) and V(t) are regarded as 0, its variance Q = 0.02, R = 0.038, so its covariance matrix can be expressed as:

[0080] P ( k+1|k ) =ΦP ( k|k ) Φ T +ΓQΓ T (1.4)

[0081] The gain matrix of the filter can be expressed as:

[0082] K(k+1)=P(k+1|k)H T [ HP(k+1|k)H T +R ] -1 (1.5)

[0083] The covariance matrix can be transformed:

[0084] P(k+1|k+1)= [ I n -K (k+1 ) H ] P(k+1|k)(1.6)

[0085] In parallel processing, we compare the difference between the sliding average filter algorithm and the Kalman filter algorithm in data processing for the torque data at different speeds. Figure 2 Curve A represents the initial torque data without filtering, Curve B reflects the result after processing by our customized Kalman filter algorithm, and Curve C is the data curve processed by the sliding average filter method. Through this comparison, we can clearly see the performance differences and characteristics of different filtering technologies when processing dynamic torque data.

[0086] B. Fuzzy-based variable parameter impedance control

[0087] The fuzzy control algorithm can sense environmental changes in real time and automatically adjust the parameters of the compliant control strategy to adapt to new working conditions. This not only improves the stability of the system in the environmental transition area, but also enhances its compliant practicality, allowing the system to perform scheduled tasks more flexibly and accurately while reducing potential damage to the environment and equipment. The impedance control model can be expressed as:

[0088]

[0089] Where ΔX(t) = X d (t)-X(t),X d (t) indicates the expected position of the structure, and X(t) indicates the current position of the structure. M, D, and K are different control parameters of the model. From the equation, we can know that when the structure contacts the insulator string and reaches static equilibrium, the speed of the mechanism itself will become 0. At this time, the impedance force F generated by the mechanism due to external influences z With insulator string F e The contact forces are equal. This can be expressed as:

[0090] F z =K(X d (t)-X(t))=K e (X(t)-X e (t)) (1.8)

[0091] In this equation, K e represents the stiffness of the external insulator string, X e (t) is used to represent the position. The equation can be transformed

[0092]

[0093] It can be obtained from the above formula that when the force F of the external insulator string e When the actual position X(t) of the mechanism is 0, it can be compared with its expected position X d (t) is tracked. However, when it is not 0, an error will be generated, which is called flexible error and can be expressed as:

[0094]

[0095] When the position of the external insulator string and the mechanism is a constant, the parameters K and D of the model can be designed by making a prediction of the external insulator string, so that the mechanism has better dynamic stability and can track the target insulator string more accurately.

[0096] However, for the complex and changeable external environment, using fixed parameters to control the mechanism cannot guarantee accuracy. Therefore, fuzzy control is used to modify its input parameters.

[0097] C fuzzy control rules:

[0098] Table 1 External stiffness parameter adjustment table

[0099]

[0100]

[0101] Table 2 Damping parameter adjustment table

[0102] EC\E NB NM NS ZO PM PS PB NB NB NB NB NS ZO ZO PS NM NB NM NM NS ZO ZO PS NS NM NM NS ZO ZO PS PM ZO NM NS ZO ZO PS PM PM PS ZO ZO ZO PS PS PM PB PM ZO ZO PS PS PS PM PB PB ZO ZO PS PS PM PB PB

[0103] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. With this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0104] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with such instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with such instruction execution systems, devices or apparatuses.

[0105] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0106] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0107] Example 4, set up an insulator replacement experiment under complex working conditions. The experimental objects include mechanical devices for replacing insulator strings - grabbing devices and main support arms, and are equipped with control systems. The experiment uses Kalman filter algorithm, fuzzy control algorithm and extended Kalman filter to test multiple control methods. The main parameters of the test include grabbing force, displacement deviation, output torque, compliance error, etc. These data can reflect the adaptability, stability and operation accuracy of the mechanical device.

[0108] The test first prepared two different sets of insulator strings: one set was made of new material insulators with strong anti-interference characteristics; the other set was made of traditional material insulators, which are prone to displacement deviation due to environmental vibration. The test environment was set at high altitude and simulated actual working conditions such as wind speed, vibration and temperature difference to observe the stability of the mechanical device in a dynamic environment.

[0109] Phase 1: Data Collection and Processing

[0110] After the test device is started, the state data is first collected through the high-precision force sensors on the gripping device and the support arm. The system obtains real-time gripping force, support force, displacement deviation and other data. The data is transmitted to the control system and the Kalman filter algorithm performs preliminary filtering to eliminate environmental noise and unstable factors. The gain coefficient of the Kalman filter is set to 0.85 according to the experimental environment to optimize the filtering effect. The frequency of data collection is set to 1kHz to ensure the accuracy of data collection in a high dynamic environment.

[0111] Phase II: Fuzzy control parameter adjustment

[0112] The data processed by Kalman filter is input into the fuzzy control algorithm. The system uses the external stiffness parameter adjustment table (Table 1) to adjust the compliance of the gripping device in real time according to environmental changes. The external stiffness parameters are set to multiple levels, including three response intervals of "low", "medium" and "high". The control system adjusts the compliance according to the insulator material and environmental vibration conditions. Through fuzzy control, the gripping device can effectively avoid hard impact and excessive force when contacting the insulator, and the gripping force accuracy is improved by about 20%.

[0113] Phase 3: Extended Kalman Filter and Data Correction

[0114] To enhance stability in nonlinear environments, the system uses the extended Kalman filter algorithm to compensate for dynamic conditions. The extended Kalman filter uses a priori estimates to correct the real-time displacement error of the insulator to further reduce the compliance error. In the experiment, the noise variance of the extended Kalman filter was set to 0.015 to adjust the correction accuracy. The final data processing results are used to adjust the output torque of the gripping device and the support arm to ensure precise control in a dynamically changing environment.

[0115] Phase 4: Control algorithm effect comparison

[0116] In the actual operation process, the effects of Kalman filter, extended Kalman filter and fuzzy control algorithm were tested respectively. The effect of the present invention was further verified by recording the performance of different algorithms in terms of gripping force, compliance and displacement error. Experimental data Table 3 shows various experimental data to evaluate the application effect of the control algorithm.

[0117] Table 3 Experimental data table

[0118]

[0119] Experimental data table 3 shows the performance of different control algorithms in terms of grasping force, displacement deviation, output torque, compliance error and environmental adaptability. The results show that the intelligent algorithm adopted by the present invention has significant advantages over traditional control systems. The traditional control system has a large grasping force in a dynamic environment, and a high displacement deviation and compliance error. The anti-interference performance is only 65%, and the environmental adaptability score is 3 points, which makes it difficult to cope with complex working conditions. The Kalman filter algorithm performs preliminary filtering and correction on the data, reducing the grasping force by 12%, the compliance error and displacement deviation by 32% and 31% respectively, indicating its effectiveness in environmental interference suppression and data stability.

[0120] Fuzzy control further improves environmental adaptability. By adjusting the stiffness parameters in real time, the compliance error of the gripping device when contacting the insulator is reduced to 1.2mm, and the environmental adaptability score is increased to 5, showing high efficiency adaptability under complex working conditions. The extended Kalman filter further optimizes the accuracy of the nonlinear system, reducing the gripping force and output torque by 4% and 6% respectively, the compliance error is only 1.0mm, and the anti-interference performance is improved to 88%.

[0121] After combining fuzzy control with extended Kalman filtering, the system showed the best results, with the gripping force reduced to 65N, the displacement deviation to 1.0mm, the compliance error further reduced to 0.8mm, and the anti-interference performance reaching 90%. In contrast, the traditional control system has insufficient anti-interference performance and large errors, making it difficult to maintain stable operation under complex working conditions. The combined algorithm not only achieves precise control, but also demonstrates excellent adaptability and safety under dynamic working conditions.

[0122] In summary, the present invention realizes intelligent control and adaptive adjustment through the combination of multiple algorithms, significantly improving the accuracy and safety of the operation. In nonlinear and complex environments, the combination of fuzzy control and extended Kalman filtering effectively solves the technical problems of insufficient anti-interference and poor compliance control of traditional control systems, verifying the innovation and practical value of the present invention in the process of replacing insulator strings.

[0123] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A control method for a linear single-string insulator string replacement mechanism, characterized in that: include: Obtaining status data of a first object; performing interference cleaning of a first characteristic on the state data, and transmitting the cleaned data to a second object; The second object processes the cleaned data and controls the work of the first object.

2. The linear single-string insulator string replacement mechanism control method according to claim 1, characterized in that: The status data includes data that can reflect the working status of the first object.

3. The linear single-string insulator string replacement mechanism control method according to claim 2, characterized in that: Performing interference cleaning of the first characteristic on the state data includes filtering the state data to obtain stable and accurate state data.

4. The linear single-string insulator string replacement mechanism control method according to claim 3, characterized in that: The second object processes the cleaned data, including designing a control algorithm based on the cleaned data to control the operation of the first object.

5. The linear single-string insulator string replacement mechanism control method according to claim 4, characterized in that: The controlling the operation of the first object includes adjusting the operation parameters of the first object by using a control algorithm, so that the first object adapts to the operation environment and improves the operation accuracy of the first object.

6. The linear single-string insulator string replacement mechanism control method according to claim 5, characterized in that: The interference cleaning of the first feature of the state data includes but is not limited to filtering the state data using a Kalman filter, which is expressed as follows: P(k+1|k)=ΦP(k|k)Φ T +ΓQΓ T K(k+1)=P(k+1|k)H T [HP(k+1|k)H T +R] -1 Wherein, F0(k) represents the torque state variable at discrete time step k, F0(k+1) represents the prediction of F0(k) at the next moment; a and b represent the coefficient parameters in the Kalman filter equation, Γ represents the coefficient matrix of process noise W(k), W(k) represents process noise, F(k) represents the measured torque data at moment k, H represents the observation matrix, V(k) represents the observation noise, Q represents the variance of process noise W(k), R represents the variance of observation noise V(k), P(k+1|k) represents the covariance prediction matrix at moment k for moment k+1, Φ represents the state transfer matrix, P(k|k) represents the covariance matrix at moment k, and K(k+1) represents the Kalman gain matrix I n Represents the identity matrix.

7. The linear single-string insulator string replacement mechanism control method according to claim 6, characterized in that: The control algorithm includes, but is not limited to, a fuzzy control algorithm; Through the fuzzy control algorithm, the first object is adapted to the working environment and the working accuracy of the first object is improved. The formula is expressed as: Where M is the inertia coefficient, ΔX(t) is the displacement error or deviation, and X d (t) represents the expected position, X(t) represents the current actual position, represents the rate of change of displacement error, B represents the damping coefficient, K represents the stiffness coefficient, and F z (t) represents external force or control force.

8. A linear single-string insulator string replacement mechanism control system, characterized in that: A data acquisition module, for acquiring status data of the first object; an analysis module, performing interference cleaning of a first feature on the state data, and transmitting the cleaned data to a second object; The control module, the second object processes the cleaned data and controls the work of the first object.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the linear single-string insulator string replacement mechanism control method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the linear single-string insulator string replacement mechanism control method according to any one of claims 1 to 7 are implemented.

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