High-voltage line plug-in operation control method, robot, and storage medium

Abstract

The application discloses a high-voltage line plug operation control method, a robot and a storage medium, and belongs to the technical field of power maintenance. The application executes a first plug changing action on a target line through first control data, the first plug changing action includes a plug-in action and a plug-out action; a tension detection device is controlled to detect first tension data of the target line and collect environmental data of the target line; when the first plug changing action fails, a tension response model of the first plug changing action is determined according to the first tension data, the environmental data and the first control data; second control data is generated according to the tension response model and the abnormal action of the execution failure, and a second plug changing action is executed according to the second control data, thereby realizing the technical effect of improving the scene adaptability of the robot.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power maintenance, in particular to a high-voltage line plug operation control method, a robot and a storage medium. BACKGROUND

[0002] Line plug operation is an important maintenance project in power system maintenance and maintenance. At present, in order to improve the operation efficiency and reduce the risk of personnel in the operation process, robots are often used for plug operation. However, in some complex environments, due to the difficulty of robots to adapt to harsh working conditions, such as low temperature, icing and rust, plug action abnormalities occur frequently, that is, during the execution according to the program, the actions such as pulling out the plug cannot be realized, or due to the inability to judge that the tension of the cable under the current environment is at the critical point of breaking, the problem of cable breaking is caused, thereby greatly reducing the working conditions of the robot in the power system.

[0003] The above content is only used to assist in understanding the technical solutions of the present application, and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0004] The main purpose of the present application is to provide a high-voltage line plug operation control method, a robot and a storage medium, which aims to improve the scene adaptability of the robot. In order to achieve the above purpose, the present application provides a high-voltage line plug operation control method applied to a plug robot, which comprises the following steps:

[0005] According to the first control data, a first plug changing action is performed on the target line, which includes plug-in action and plug-out action;

[0006] The tension detection device detects the first tension data of the target line and collects the environmental data of the target line;

[0007] When the first plug changing action fails, the tension response model of the first plug changing action is determined according to the first tension data, the environmental data and the first control data;

[0008] According to the tension response model and the abnormal action of execution failure, the second control data is generated, and the second plug changing action is executed according to the second control data.

[0009] Optionally, the step of determining the tension response model of the first plug changing action according to the first tension data, the environmental data and the first control data comprises:

[0010] According to the environmental data and the first control data, the first tension data is grouped into a plurality of second tension data;

[0011] Based on the environmental data, the corresponding environmental characteristics are determined, and each second tension data is filtered according to the environmental characteristics to obtain multiple third tension data, wherein each third tension data corresponds to one second tension data.

[0012] The tension response model is determined based on multiple third tension data and the first control data.

[0013] Optionally, the first control data includes: surface treatment control data, pre-pull-out control data, pin pull-out control data, and pin insertion control data, wherein the surface treatment control data, the pre-pull-out control data, the pin pull-out control data, and the pin insertion control data each correspond to a control data type, and the step of determining the tension response model based on multiple third tension data and the first control data includes:

[0014] The control data type corresponding to each of the third tension data is determined based on the first control data;

[0015] Extract the tension features of each of the third tension data, and associate the tension features with the corresponding control data type to obtain multiple association relationships;

[0016] The action response vector is determined based on the multiple relationships and the control data types described above;

[0017] The tension response model is determined based on the action response vector;

[0018] Wherein, the surface control data is data for performing surface processing on the target circuit, the pre-pull control data is data for performing a test on the tightness of the pins in the target circuit, the pin pull control data is data for performing a pull-out of the pins in the target circuit, and the pin insertion control data is data for performing a process of inserting pins into the target circuit.

[0019] Optionally, the step of determining the tension response model based on the action response vector includes:

[0020] Calculate the first similarity between the action response vector and multiple preset response vectors;

[0021] The tension response model is determined based on the first similarity among multiple cable dynamics models;

[0022] The preset response vector corresponds one-to-one with the cable dynamics model.

[0023] Optionally, before the step of determining the tension response model based on the action response vector, the method further includes:

[0024] The tension response model is determined from multiple cable response models based on the action response vector;

[0025] The cable response model is a model trained on a preset deep learning model based on the operating data of the pin robot prior to the current moment.

[0026] Optionally, the step of generating second control data based on the tension response model and the failed abnormal action includes:

[0027] Based on the abnormal action, determine multiple alternative control data;

[0028] The second control data is determined based on multiple alternative control data and the tension response model.

[0029] Optionally, the step of determining the second control data based on multiple alternative control data and the tension response model includes:

[0030] The execution process of the alternative control data is simulated in the tension response model, and the corresponding simulated tension is calculated to obtain multiple simulated tensions;

[0031] The second control data is determined from among the multiple alternative control data based on the simulated tension.

[0032] Optionally, the environmental data includes at least one of temperature data, icing data, and wind speed.

[0033] Furthermore, to achieve the above objectives, the present invention also provides a robot, the robot comprising: a memory, a processor, and a high-voltage line pin operation control program stored in the memory and executable on the processor, the high-voltage line pin operation control program being configured to implement the steps of the high-voltage line pin operation control method described in any of the above claims.

[0034] In addition, to achieve the above objectives, the present invention also provides a storage medium, characterized in that the storage medium stores a high-voltage line pin operation control program, which, when executed by a processor, implements the steps of the high-voltage line pin operation control method described above.

[0035] This invention proposes a control method for high-voltage line pin replacement operations. This method executes a first pin replacement action on the target line using first control data and detects first tension data and environmental data. Essentially, it identifies the environmental conditions during the pin replacement operation. When the first pin replacement action fails, a tension response model for the first pin replacement action is determined based on the first tension data, environmental data, and the first control data. Compared to traditional robots that are only controlled according to preset logic, this method effectively identifies the impact of various actions on the cable during the current pin replacement operation. It then generates second control data based on the tension response model and the failed abnormal actions, and executes a second pin replacement action based on the second control data. This effectively avoids cable breakage caused by the robot pulling or inserting pins, significantly improving the robot's adaptability to different scenarios. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the structure of the robot in the hardware operating environment involved in the embodiments of the present invention;

[0037] Figure 2 This is a flowchart illustrating the first embodiment of the high-voltage line pin operation control method of the present invention.

[0038] Figure 3 This is a flowchart illustrating the second embodiment of the high-voltage line pin operation control method of the present invention;

[0039] Figure 4 This is a flowchart illustrating the third embodiment of the high-voltage line pin operation control method of the present invention;

[0040] Figure 5 This is a schematic diagram of the robot's operation according to the present invention;

[0041] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0042] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0043] Reference Figure 1 , Figure 1 This is a schematic diagram of the robot structure in the hardware operating environment involved in the embodiments of the present invention.

[0044] like Figure 1As shown, the robot may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interaction device 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The interaction device 1003 may include a display screen or an input unit such as a keyboard. Optionally, the interaction device 1003 may also connect to the communication bus via standard wired or wireless interfaces. The network interface 1004 may optionally include standard wired or wireless interfaces (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0045] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the robot and may include more or fewer parts than shown, or combine certain parts, or have different arrangements of parts.

[0046] The robot described is a pin-changing robot, specifically equipped with environmental sensors, including temperature, humidity, air pressure, wind speed, and noise sensors. Furthermore, the pin-changing robot can be equipped with rollers, allowing it to be mounted on the cable of the target line to replace pins or latches in devices requiring replacement. Optionally, the pin-changing robot may also be equipped with an ice-breaking device and a rust-removing device.

[0047] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a high-voltage line plug operation control program.

[0048] exist Figure 1 In the robot shown, the network interface 1004 is mainly used for data communication with other devices; the interactive device 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the robot of the present invention can be set in the robot. The robot calls the high-voltage line pin operation control program stored in the memory 1005 through the processor 1001 and executes the high-voltage line pin operation control method provided in the embodiment of the present invention.

[0049] This invention provides a high-voltage line plug operation control method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a high-voltage line pin operation control method according to the present invention.

[0050] In this embodiment, the high-voltage line plug operation control method includes:

[0051] Step S1: Perform a first pin-changing action on the target line according to the first control data. The first pin-changing action includes: a pin insertion action and a pin removal action.

[0052] In this embodiment, the first control data is pre-set and usually does not need to be changed. Of course, for different types of pins, the specific control methods for removing and inserting pins in the first control data may vary. Common pins include cotter pins, spring pins, and threaded pins. For example, for cotter pins, the removal steps are as follows: hook positioning, using a triangular hook to precisely hook the bent end of the cotter pin; axial pressing, the hydraulic jack applies a preset thrust to release the pin foot catch; unlocking by rotating the pin counterclockwise by a preset angle to align the straight arm with the pin hole; smooth withdrawal, maintaining axial tension and pulling out at a uniform speed. For spring pins, the removal steps are as follows: clamping preparation, inserting the internal expansion clamp into the pin hole and expanding it; slow withdrawal: slowly pulling out at a preset speed and axial tension. The pin-changing action here can include: inserting pin action and changing pin action.

[0053] Step S2: Control the tension detection device to detect the first tension data of the target line and collect the environmental data of the target line;

[0054] It should be noted that the type of tension detection device is not limited. It can be a hydraulic sensor installed on the cable to determine the tension through calculation, or a vibration meter to determine tension changes by monitoring the vibration frequency of the wire, or a laser vibration meter to determine the tension of the wire using the vibration frequency method. Specifically, the detection of the first tension data of the target line and the acquisition of environmental data of the target line are performed simultaneously with step S1. The order in which the first tension data and environmental data are detected is also not limited.

[0055] Step S3: When the first pin replacement action fails, determine the tension response model of the first pin replacement action based on the first tension data, environmental data, and the first control data.

[0056] Specifically, common situations where the first pin-changing action fails include: rust or deposits on the pin cannot be removed according to the first control data; the pin cannot be pulled out according to the pulling force specified in the first control data; and the pin cannot be inserted. In this process, since the first tension data and environmental data have been collected during the execution of the first pin-changing action, a tension response model can be determined using the first tension data, environmental data, and the first control data. The tension response model refers to a model describing the change in tension caused by all specific pin-changing action steps during the execution of the first pin-changing action. Environmental data may include at least one of the following: temperature data, icing data, and wind speed. Corresponding sensors are typically set up to collect the relevant data.

[0057] Step S4: Generate second control data based on the tension response model and the abnormal action that failed to execute, and execute the second pin replacement action based on the second control data.

[0058] It should be noted that the first pin replacement action often includes multiple sub-actions, and the failure of the first pin replacement action means that one of the sub-actions cannot be completed. Therefore, the abnormal action of execution failure here refers to the specific sub-action that failed when the first pin replacement action fails. Second control data is generated based on the tension response model and the abnormal action. In the second control data, adaptive control of the pin replacement operation on the high-voltage line is achieved by adjusting the control data.

[0059] In this embodiment, a first pin-changing action is performed on the target line using first control data, and first tension data and environmental data are detected. In fact, this identifies the environmental conditions during the pin-changing operation. When the first pin-changing action fails, a tension response model for the first pin-changing action is determined based on the first tension data, environmental data, and the first control data. Compared to traditional robots that are only controlled according to preset logic, this method can effectively identify the impact of each action on the cable during the current pin-changing operation. Second control data is generated based on the tension response model and the failed abnormal action, and a second pin-changing action is performed based on the second control data. This effectively avoids cable breakage caused by the robot pulling or inserting pins, and effectively improves the robot's scene adaptability.

[0060] Furthermore, based on the first embodiment, a second embodiment of the high-voltage line plug operation control method of the present invention is proposed. In this embodiment, reference is made to... Figure 3 The step of determining the tension response model of the first changing pin action based on the first tension data, environmental data, and the first control data includes:

[0061] Step S31: Group the first tension data into multiple second tension data based on the environmental data and the first control data;

[0062] Specifically, since the tension data is continuously collected, and since cable tension changes dynamically due to external environmental factors, in this embodiment, the first tension data is divided into multiple second tension data. Specifically, a first time node is determined based on the environmental data, a second time node is determined based on the first control data, and the first tension data is grouped into multiple second tension data based on the first and second time nodes.

[0063] Step S32: Determine the corresponding environmental features based on the environmental data, filter each second tension data according to the environmental features to obtain multiple third tension data, each third tension data corresponding to one second tension data;

[0064] The environmental data and features here need to correspond to each of the grouped second tension data points; that is, each first tension data point corresponds to a set of environmental features, including characteristics such as wind frequency, intensity, and average wind speed, and may also include temperature features. In this embodiment, the third tension data is obtained by filtering each second tension data point according to the environmental features. In other embodiments, the influence of environmental data on the second tension data can be determined, and the second tension data can be adjusted according to the influence to obtain the third tension data. The purpose of step S32 is to ensure that the tension change in the obtained third tension data is caused by the robot performing the pin-changing action.

[0065] Step S33: Determine the tension response model based on multiple third tension data and the first control data.

[0066] In this embodiment, it should be noted that since the second time node is determined based on the first control data during the grouping process, the tension change in the third tension data is caused by the robot's operation. The tension response model is determined by matching multiple third tension data points with the first control data.

[0067] In this embodiment, the first tension data is grouped into multiple second tension data based on the environmental data and the first control data. Corresponding environmental features are determined based on the environmental data, and each second tension data is filtered according to the environmental features to obtain multiple third tension data. A tension response model is then determined based on the multiple third tension data and the first control data, reducing the tension variations caused by the environment and thus improving the accuracy of the tension model identification.

[0068] Furthermore, based on the first or second embodiment, a third embodiment of the high-voltage line plug operation control method of the present invention is proposed. In this embodiment, reference is made to... Figure 4 The first control data includes: surface treatment control data, pre-pull-out control data, pin pull-out control data, and pin insertion control data. Each of the surface treatment control data, pre-pull-out control data, pin pull-out control data, and pin insertion control data corresponds to a control data type. The step of determining the tension response model based on multiple third tension data and the first control data includes:

[0069] Step S331: Determine the control data type corresponding to each of the third tension data based on the first control data;

[0070] In this embodiment, since the second time node is determined by the first control data, and the first tension data is grouped into multiple second tension data based on the first and second time nodes, each third tension data can essentially correspond to only one type of control data. However, since the second time node is determined based on environmental data, the control data type may correspond to one or more third tension data.

[0071] Step S332: Extract the tension features of each of the third tension data, and associate the tension features with the corresponding control data type to obtain multiple association relationships;

[0072] In this embodiment, when one control data type corresponds to more than one third tension data, in reality, through the association relationship, the number of tension features corresponding to one control data type is more than one. In this case, it is necessary to merge multiple tension features, for example, by calculating the average value of the tension features corresponding to one control data type. Of course, in other embodiments, the more than one tension feature corresponding to one control data type can be merged according to other data merging methods.

[0073] Step S333: Determine the action response vector based on the multiple association relationships and the control data type;

[0074] Specifically, tension features are determined based on the control data type and the correlation relationship, and the tension feature data are merged into a vector to obtain the action response vector. Each element in the action response vector corresponds to a tension feature of the control data type. For example, the first element of the action response vector stores the tension change frequency corresponding to the surface treatment of the target line, the second element stores the tension change amplitude corresponding to the surface treatment of the target line, and the third element stores the average tension corresponding to the surface treatment of the target line.

[0075] Step S334: Determine the tension response model based on the action response vector;

[0076] Optionally, the tension response model is determined by comparing the action response vector with the vector obtained by simulating the first pin change action of the target line using the first control data of the stored model.

[0077] Wherein, the surface control data is data for performing surface processing on the target circuit, the pre-pull control data is data for performing a test on the tightness of the pins in the target circuit, the pin pull control data is data for performing a pull-out of the pins in the target circuit, and the pin insertion control data is data for performing a process of inserting pins into the target circuit.

[0078] In addition, the surface treatment control data can be the operating data of the rust removal scraper, the pre-pulling control data can be the specific force control data, the pin pulling control data can be the pin pulling force, and the pin insertion control data can be the pin insertion pressure, etc. In this embodiment, the data here is not limited.

[0079] In this embodiment, the control data type corresponding to each of the third tension data is determined based on the first control data; the tension features of each of the third tension data are extracted and associated with the corresponding control data type to obtain multiple association relationships; the action response vector is determined based on the multiple association relationships and the control data type, thereby determining the tension response model.

[0080] Furthermore, based on any of the above embodiments, a fourth embodiment of the high-voltage line pin operation control method of the present invention is proposed, wherein the step of determining the tension response model based on the action response vector includes:

[0081] Calculate the first similarity between the action response vector and multiple preset response vectors;

[0082] The tension response model is determined based on the first similarity among multiple cable dynamics models;

[0083] The preset response vector corresponds one-to-one with the cable dynamics model.

[0084] In this embodiment, the physical characteristics, connection methods, and working environment parameters of the high-voltage line cables differ across multiple cable dynamics models. Furthermore, differences also exist in the coupling effect between the robot and the cable. Common design differences include vibration mode differences; for example, the dynamic characteristics of a cable covered in ice and snow differ from those of an uncovered cable. The cable dynamics model generates the preset response vector by simulating the robot executing the first control data. By calculating the first similarity between the action response vector and multiple preset response vectors, the tension response model can be determined among the multiple cable dynamics models.

[0085] In this embodiment, by calculating the first similarity between the action response vector and multiple preset response vectors, the tension response model is determined based on the first similarity in multiple cable dynamic models. In fact, the corresponding tension response model can be matched, thereby improving the accuracy of the subsequent determination of the second control data based on the tension response model.

[0086] Furthermore, based on any one of the first to third embodiments described above, a fifth embodiment of the high-voltage line plug operation control method of the present invention is proposed, wherein before the step of determining the tension response model based on the action response vector, the method further includes:

[0087] The tension response model is determined from multiple cable response models based on the action response vector;

[0088] The cable response model is a model trained on a preset deep learning model based on the operating data of the pin robot prior to the current moment.

[0089] In this embodiment, the operational data includes historical control data and corresponding historical tension data and historical environmental data recorded before the current moment. The historical control data and historical environmental data are used as input to the model, and the historical tension data is used as the model's output to construct a dataset. This dataset includes a training set and a test set. A preset deep learning model is trained using the training set, and the trained model is tested using the test set. The model that passes the test is used as the cable response model. It should be noted that the data in a dataset comes from data recorded on the same type or the same target line. The first control data and environmental data are input, the model's output is received, and the tension response model is determined based on the model's output.

[0090] Furthermore, based on any of the above embodiments, a sixth embodiment of the high-voltage line plug operation control method of the present invention is proposed, wherein the step of determining the second control data according to multiple alternative control data and the tension response model includes:

[0091] The execution process of the alternative control data is simulated in the tension response model, and the corresponding simulated tension is calculated to obtain multiple simulated tensions;

[0092] The second control data is determined from among the multiple alternative control data based on the simulated tension.

[0093] It should be noted that the target line has a maximum permissible tension. After obtaining the simulated tension, it needs to be compared with the stated maximum permissible tension. This maximum permissible tension is not a static data point; it is generally affected by environmental factors such as temperature, icing, wind speed, corrosion, and humidity. Therefore, the calculation method for the maximum permissible tension is not restricted. The stated maximum permissible tension needs to be comprehensively reduced under different environmental conditions.

[0094] The execution process of the alternative control data is simulated in the tension response model, and the corresponding simulated tension is calculated to obtain multiple simulated tensions. Based on the simulated tensions, the second control data is determined from the multiple alternative control data, thereby ensuring that the robot will not use excessive force under the control of the second control data, which would cause the target cable to break.

[0095] Furthermore, this invention also proposes a robot, which includes: a memory, a processor, and a high-voltage line pin operation control program stored in the memory and executable on the processor. The high-voltage line pin operation control program is configured to implement the steps of the embodiment of the high-voltage line pin operation control method described above. Figure 4 The diagram shows a robot in operation, which exerts additional tension on the cables during its work.

[0096] Furthermore, this invention also proposes a storage medium storing a high-voltage line pin operation control program, which, when executed by a processor, implements the steps of any of the embodiments of the high-voltage line pin operation control method described above.

[0097] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0098] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0099] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0100] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for controlling the operation of high-voltage line plugs, characterized in that, The high-voltage line pin operation control method, applied to pin-operating robots, includes the following steps: The first pin-changing action is performed on the target line according to the first control data. The first pin-changing action includes: a pin insertion action and a pin removal action. The control tension detection device detects the first tension data of the target line and collects the environmental data of the target line; When the first pin replacement action fails, the tension response model of the first pin replacement action is determined based on the first tension data, environmental data, and the first control data. The second control data is generated based on the tension response model and the abnormal action that failed to execute, and the second pin replacement action is executed based on the second control data; The step of determining the tension response model of the first changing pin action based on the first tension data, environmental data, and the first control data includes: Based on the environmental data and the first control data, the first tension data is grouped into multiple second tension data; Based on the environmental data, the corresponding environmental characteristics are determined, and each second tension data is filtered according to the environmental characteristics to obtain multiple third tension data, wherein each third tension data corresponds to one second tension data. The tension response model is determined based on multiple third tension data and the first control data; The first control data includes: surface treatment control data, pre-pull-out control data, pin pull-out control data, and pin insertion control data. Each of the surface treatment control data, pre-pull-out control data, pin pull-out control data, and pin insertion control data corresponds to a control data type. The step of determining the tension response model based on multiple third tension data and the first control data includes: The control data type corresponding to each of the third tension data is determined based on the first control data; Extract the tension features of each of the third tension data, and associate the tension features with the corresponding control data type to obtain multiple association relationships; The action response vector is determined based on the multiple relationships and the control data types described above; The tension response model is determined based on the action response vector; Wherein, the surface treatment control data is data for performing surface treatment on the target circuit, the pre-pull control data is data for performing a test on the tightness of the pins of the target circuit, the pin pull control data is data for performing a pull-out of the pins of the target circuit, and the pin insertion control data is data for performing a process of inserting pins into the target circuit. The step of determining the tension response model based on the motion response vector includes: Calculate the first similarity between the action response vector and multiple preset response vectors; The tension response model is determined based on the first similarity among multiple cable dynamics models; The preset response vector corresponds one-to-one with the cable dynamics model.

2. The high-voltage line pin operation control method as described in claim 1, characterized in that, Before the step of determining the tension response model based on the action response vector, the method further includes: The tension response model is determined from multiple cable response models based on the action response vector; The cable response model is a model trained on a preset deep learning model based on the operating data of the pin robot prior to the current moment.

3. The high-voltage line pin operation control method as described in claim 1, characterized in that, The step of generating second control data based on the tension response model and the failed abnormal action includes: Based on the abnormal action, determine multiple alternative control data; The second control data is determined based on multiple alternative control data and the tension response model.

4. The high-voltage line pin operation control method as described in claim 3, characterized in that, The step of determining the second control data based on multiple alternative control data and the tension response model includes: The execution process of the alternative control data is simulated in the tension response model, and the corresponding simulated tension is calculated to obtain multiple simulated tensions; The second control data is determined from among the multiple alternative control data based on the simulated tension.

5. The high-voltage line pin operation control method according to any one of claims 1 to 4, characterized in that, The environmental data includes at least one of the following: temperature data, icing data, and wind speed.

6. A robot, characterized in that, The robot includes: a memory, a processor, and a high-voltage line pin operation control program stored in the memory and executable on the processor, the high-voltage line pin operation control program being configured to implement the steps of the high-voltage line pin operation control method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium stores a high-voltage line pin operation control program, which, when executed by a processor, implements the steps of the high-voltage line pin operation control method as described in any one of claims 1 to 4.

Images