A method suitable for substation tubular bus clamp live machine robot double-arm collaborative operation

Abstract

The application discloses a kind of methods suitable for substation tube bus clamp live machine robot double-arm collaborative work, it is related to electric power system automation technical field. Including: the electromagnetic change threshold is compared with dynamic threshold, obtains electromagnetic environment change parameter and early warning signal;Through the early warning signal, the control parameter of the robot and the sensitivity of sensor are adjusted;Through the electromagnetic environment change parameter, the strong electromagnetic field area is determined, and the inspection path of robot is planned;According to the inspection path, tube bus is connected / detached in the target position Drainage line operation.The application determines the electromagnetic change threshold by monitoring the electromagnetic environment change in the substation in real time, and compares it with the dynamic threshold, generates different levels of early warning signals, so that the operator can be reminded in time or the robot parameters are automatically adjusted, and equipment failure or safety accident caused by strong electromagnetic field is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system automation, in particular to a double-arm cooperative operation method suitable for live-line robot of bus clamp in a substation. BACKGROUND

[0002] With the construction and development of smart grid, the automation and intelligence level of substations is continuously improved. Especially in high-voltage substations, the demand for live-line operation is increasing. The traditional live-line operation method mainly relies on manual work, which has great safety risks and labor costs. Therefore, the double-arm cooperative operation robot is widely used in live-line operation in substations because it can simulate human hands working cooperatively.

[0003] The Chinese invention patent with publication number CN109648303A discloses a bus fittings screw locking and unlocking device and method of a live-line operation robot, which comprises an insulated aerial platform truck, a working arm of the aerial platform truck erected on the aerial platform truck, a work platform provided on the working arm, a three-dimensional scanner, a driven mechanical arm, a locking / unlocking electric screw mechanism, a driving mechanical arm and an equipotential rod provided on the work platform, a driving mechanical arm binocular camera provided on the front part of the driving mechanical arm, a driven mechanical arm binocular camera provided on the front part of the driven mechanical arm, and the locking / unlocking electric screw mechanism installed at the end of the driving mechanical arm. The labor intensity of the locking and unlocking operation of the bus fittings screw of the power transformation equipment is reduced, the efficiency and automation level are improved, the personal safety of the operators is ensured, the number of robots required is small, and the influence of electromagnetic interference on information transmission is reduced.

[0004] The above-mentioned and similar methods have the problem that during the live-line operation by the robot, the robot is usually equipped with a multi-degree-of-freedom mechanical arm, a sensing system and a control system, and there is usually a strong electromagnetic field inside the substation, so that during the live-line operation, the electronic equipment and sensors of the robot are disturbed, affecting the normal work. SUMMARY

[0005] The present application aims to provide a double-arm cooperative operation method suitable for live-line robot of bus clamp in a substation to solve the problems in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solution: a double-arm cooperative operation method suitable for live-line robot of bus clamp in a substation, comprising:

[0007] establishing an environment monitoring module to monitor the electromagnetic environment changes in the substation, determine the electromagnetic change threshold value, compare the electromagnetic change threshold value with the dynamic threshold value, obtain the electromagnetic environment change parameter and the early warning signal;

[0008] Determine operating parameters: through the early warning signal, adjust the control parameters of the robot and the sensitivity of the sensor;

[0009] Set up a patrol avoidance strategy: through the electromagnetic environment change parameter, determine the strong electromagnetic field area in the working environment of the substation, and according to the strong electromagnetic field area and the operating parameter, plan the patrol path of the robot, including:

[0010] Compare the electromagnetic environment change parameter with the electromagnetic field intensity threshold value, and according to the comparison result, divide the working area corresponding to the electromagnetic environment change parameter into normal area, slight interference area, moderate interference area and strong interference area;

[0011] According to the division result of the working area, determine the interference level corresponding to each working area, and obtain the cost input corresponding to each working area, and according to the cost input, determine the patrol path, the calculation formula of the cost input is:

[0012]

[0013] Wherein: V(i,j) is the cost input from the starting point to the grid in the i row and j column, C(i,j) is the cost from the last step to the grid in the i row and j column, V(i-1,j) is the cost input from the starting point to the grid in the i-1 row and j column, V(i,j-1) is the cost input from the starting point to the grid in the i row and j-1 column, min(V(i-1,j),V(i,j-1)) is the minimum cost of the upper position and the left position to the current position, d is the fixed distance cost, η is the weight of electromagnetic field intensity, R(i,j) is the electromagnetic field intensity level of the grid in the i row and j column position;

[0014] Double arm cooperative work: according to the patrol path, the robot reaches the target position, and carries out the pipe busbar / dismounting drainage line work at the target position.

[0015] Further, obtain the electromagnetic environment change parameter and the early warning signal, including:

[0016] Build an environment monitoring system: set up sensor nodes in the interior of the substation and obtain sensor data;

[0017] Determine the dynamic threshold of electromagnetic field: through the sensor data, obtain the electromagnetic field intensity prediction value, and according to the electromagnetic field intensity prediction value and the smoothing result of different time windows, determine the electromagnetic dynamic threshold corresponding to each time;

[0018] generating a warning signal: comparing the real-time acquired electromagnetic field intensity data with the electromagnetic dynamic threshold, when the electromagnetic field intensity data is different from the electromagnetic dynamic threshold, determining the range size of the electromagnetic field intensity data exceeding the electromagnetic dynamic threshold, specifically:

[0019] when the electromagnetic field intensity data exceeds the electromagnetic dynamic threshold by 10%, a warning signal of slight interference is generated;

[0020] when the electromagnetic field intensity data exceeds the electromagnetic dynamic threshold by 30%, a warning signal of moderate interference is generated;

[0021] when the electromagnetic field intensity data exceeds the electromagnetic dynamic threshold by 50%, a warning signal of serious interference is generated;

[0022] acquiring electromagnetic environment change parameters: through the sensor data, the change size of the electromagnetic field intensity data between two adjacent time points is acquired, and according to the monitoring time, the change rate of the electromagnetic field intensity data within the monitoring time is determined.

[0023] Further, determining the electromagnetic dynamic threshold corresponding to each time point comprises:

[0024] M1: acquiring the average value of the electromagnetic field intensity data according to the electromagnetic field intensity data in the sensor data, the average value of the electromagnetic field intensity data is the initial dynamic threshold size;

[0025] M2: setting a machine learning model, and taking the electromagnetic field intensity data as the input of the machine learning model, and outputting the predicted value of the electromagnetic field intensity data;

[0026] M3: according to the initial dynamic threshold and the predicted value of the electromagnetic field intensity data, acquiring the short-time smoothing result and the long-time smoothing result corresponding to each time point, and fusing the short-time smoothing result and the long-time smoothing result to obtain the dynamic threshold corresponding to each time point, specifically:

[0027]

[0028] wherein: Y t is the dynamic threshold size corresponding to the t time point, ω1 is the weight corresponding to the short-time smoothing result, is the short-time smoothing result corresponding to the t time point, ω2 is the weight corresponding to the long-time smoothing result, is the long-time smoothing result corresponding to the t time point.

[0029] Further, the smoothing result corresponding to each time point is specifically:

[0030]

[0031] wherein: y t is the smoothing result corresponding to the t time, and a is a smoothing factor, is the electromagnetic field intensity prediction value corresponding to the t time, y t-1 is the smoothing result corresponding to the t-1 time.

[0032] Further, the smoothing factors corresponding to the smoothing results of different times satisfy the following relationship, specifically:

[0033] The size of the smoothing factor corresponding to the short-time smoothing result > the size of the smoothing factor corresponding to the long-time smoothing result.

[0034] Further, adjusting the control parameters of the robot and the sensitivity of the sensor, including:

[0035] Adjusting the inspection speed and frequency of the robot: according to the level of the early warning signal, increasing the inspection speed and frequency of the robot, specifically:

[0036] When the level of the early warning signal is slight interference, the inspection speed and frequency of the robot remain unchanged;

[0037] When the level of the early warning signal is moderate interference, the inspection speed of the robot is reduced by 30%, and the inspection frequency is increased by 10%;

[0038] When the level of the early warning signal is severe interference, the inspection speed of the robot is reduced by 50%, and the inspection frequency is increased by 20%;

[0039] Adjusting the sensitivity of the sensor: according to the level of the early warning signal, increasing the sensitivity of the sensor, specifically:

[0040] When the level of the early warning signal is slight interference, the sensitivity of the sensor remains unchanged;

[0041] When the level of the early warning signal is moderate interference, the sensitivity of the sensor is increased by 10%;

[0042] When the level of the early warning signal is severe interference, the sensitivity of the sensor is increased by 20%.

[0043] Further, according to the cost input, the inspection path is determined, including:

[0044] N1: The inspection area in the transformer substation is divided into grids, and the coordinate position corresponding to each inspection area is determined, and according to the division result of the working area, the interference level corresponding to each inspection area is determined;

[0045] N2: According to the coordinate position and the interference level, the cost input corresponding to each coordinate position is obtained;

[0046] N3: According to the target position of the robot, the cost input of all previous positions corresponding to the target position is obtained, and the minimum cost input is determined from all the cost inputs of the previous positions, and the position corresponding to the minimum cost input is the inspection position on the inspection path, and the step N3 is repeated until the position corresponding to the minimum cost input is the starting position of the robot.

[0047] Further, the electromagnetic field intensity threshold is set to 100 muT, 200 muT and 300 muT.

[0048] Further, the pipe busbar connecting / drawing wire operation at the target position comprises:

[0049] Environment modeling: the position of the pipe busbar in the substation is modeled in three dimensions by laser radar, and the parking position and the lifting height position of the lifting operation vehicle are determined according to the three-dimensional modeling;

[0050] Connecting / drawing wire operation: the robot is transported to the pipe busbar position by the parking position and the lifting height position of the lifting operation vehicle, the main arm of the robot is hung on the trolley and operated at the same potential, the slave arm of the robot is visually assisted, and the running state of the main arm and the slave arm of the robot is determined according to the classification of the connecting / drawing operation, specifically:

[0051] When connecting / drawing operation is performed:

[0052] The slave arm fixes the drainage plate, the main arm grinds the drainage plate, the main arm grabs the bolt tool, the slave arm grabs the nut tool, and the bolt tool and the nut tool are perforated and tightened;

[0053] When the drawing operation is performed:

[0054] The main arm takes the bolt tool, the slave arm takes the nut tool, and the bolt tool and the nut tool are taken out.

[0055] Compared with the prior art, the beneficial effects of the present application are:

[0056] Firstly, the environment monitoring module is established to monitor the electromagnetic environment change in the substation in real time, the electromagnetic change threshold is determined and compared with the dynamic threshold, the electromagnetic environment change parameter and the early warning signal are obtained, and different levels of early warning signals are generated according to the change of the electromagnetic field intensity, so that the operator can be reminded in time or the robot parameters can be automatically adjusted, thereby avoiding equipment failure or safety accidents caused by strong electromagnetic field;

[0057] Secondly, the sensitivity of the sensor is dynamically adjusted according to the level of the early warning signal, so that the environmental data can be accurately acquired under different interference conditions, and the inspection speed and frequency of the robot can be adjusted according to the level of the early warning signal, thereby further ensuring the operation safety in the strong electromagnetic field area.

[0058] Thirdly, the strong electromagnetic field area is determined through the electromagnetic environment change parameter, and the inspection path of the robot is planned according to the area and the operation parameter, so that the robot is prevented from running in the high-risk area, and the optimal path is selected by calculating the cost input of each position in the path planning, thereby ensuring the safety and effectiveness of the inspection task.

[0059] Fourthly, the robot cooperates with the main arm and the slave arm, the main arm is responsible for the main operation, and the slave arm provides visual assistance and auxiliary operation, thereby improving the coordination and flexibility of the operation, and the robot can continuously and efficiently complete the operation task without being affected by fatigue and environmental factors, thereby improving the operation efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 It is a flowchart of the double-arm cooperative operation method of the application;

[0061] Figure 2 It is a flowchart of acquiring the early warning signal of the application;

[0062] Figure 3 It is a flowchart of determining the inspection path of the application;

[0063] Figure 4 It is a grid distribution diagram corresponding to the area to be inspected in the substation of the application;

[0064] Figure 5 It is an electromagnetic field intensity distribution diagram corresponding to each area of the application;

[0065] Figure 6 It is an interference level distribution diagram corresponding to each area of the application;

[0066] Figure 7 It is an inspection path diagram corresponding to each area of the application;

[0067] Figure 8 It is a position distribution diagram between the robot and the lifting operation vehicle of the application. DETAILED DESCRIPTION

[0068] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0069] With the rapid development of modern industry and the improvement of people's living standards, the power users' demand for power supply reliability is also increasing. In order to reduce the power outage time and even realize the uninterrupted operation, the power supply companies in various places accelerate the application and promotion of live working robots in substations. However, since the robot is usually equipped with a multi-degree-of-freedom mechanical arm, a sensing system and a control system, and there is usually a strong electromagnetic field in the interior of the substation, during the live working process, the electronic equipment and sensors of the robot will be disturbed, affecting its normal work. The technical solutions provided in the present application determine the strong electromagnetic field area in the substation by establishing an environment monitoring module, avoid the strong electromagnetic field area in advance when planning the inspection path of the robot, and adjust the control parameters of the robot and the sensitivity of the sensors according to the early warning signal, thereby avoiding equipment failure or safety accidents caused by strong electromagnetic field.

[0070] Reference Figures 1-3 The embodiment provides a double-arm cooperative working method suitable for a live working robot of a bus clamp in a substation, which comprises the following steps:

[0071] Step S1: Establishing an environment monitoring module. That is, by monitoring the electromagnetic environment change in the substation, the electromagnetic change threshold is determined. At the same time, the determined electromagnetic change threshold and the dynamic threshold are compared to obtain the electromagnetic environment change parameter and the early warning signal. Specifically as follows:

[0072] Step S1.1: Building an environment monitoring system. That is, by deploying multiple sensor nodes at different positions in the substation, specifically, setting sensor nodes at key equipment (such as transformers, circuit breakers and busbars), incoming and outgoing line areas, control rooms and operating rooms, outdoor areas (such as around the walls of the substation) to form a grid monitoring network, so as to effectively monitor the electromagnetic environment change in the substation. Further, by means of electromagnetic field intensity sensors, such as fluxgate magnetometers and Hall effect sensors, the electromagnetic field intensity in the substation is monitored. By means of temperature sensors, such as thermocouples and thermistors, the environmental temperature is detected. By means of humidity sensors, such as capacitive humidity sensors and resistance humidity sensors, the environmental humidity is monitored. By means of air pressure sensors, such as piezoresistive air pressure sensors and capacitive air pressure sensors, the environmental air pressure is monitored.

[0073] In the process of specific implementation, sensors are deployed near key equipment such as transformers, circuit breakers and busbars to monitor the electromagnetic field intensity, temperature, humidity and air pressure around the key equipment. Similarly, sensors are deployed in the incoming and outgoing line areas of the substation to monitor the environmental parameters of the incoming and outgoing line areas. Similarly, sensors are deployed in the control room and operating room to ensure that the environmental conditions of the area meet the operating requirements. Similarly, sensors are deployed around the walls of the substation to monitor the impact of the external environment on the substation.

[0074] Step S1.2: Determine the dynamic threshold of the electromagnetic field. That is, continuously collect electromagnetic field intensity, temperature, humidity and air pressure data in the substation to obtain a signal data set of the substation. At the same time, the obtained signal data set is preprocessed, including but not limited to data cleaning and denoising. Further, the dynamic threshold of the electromagnetic field is obtained through the preprocessed signal data set. Specifically as follows:

[0075] Step M1: Through the environmental monitoring system built in step S1.1, the intensity data of the electromagnetic field is obtained, and the size of the initial dynamic threshold is determined according to the intensity data of the electromagnetic field. That is, the average value of the obtained electromagnetic field intensity data is the initial size of the dynamic threshold.

[0076] In the process of specific implementation, the electromagnetic field intensity data is set as shown in the following table, specifically:

[0077] Time t＝1 t＝2 t＝3 t＝4 t＝5 Electromagnetic field strength 100 105 110 115 120

[0078] According to the above data, the average value of the electromagnetic field intensity data is 110, that is, the initial size of the dynamic threshold is set to 100.

[0079] Step M2: Through the machine learning model and the electromagnetic field intensity data obtained in step M1, the prediction value of the electromagnetic field intensity data obtained through the machine learning model is obtained. In this embodiment, the machine learning model is set to an LSTM model, that is, the electromagnetic field intensity data obtained in step M1 is taken as the input of the LSTM model, and the corresponding electromagnetic field intensity prediction value is output.

[0080] In the process of specific implementation, according to the electromagnetic field intensity data in step M1, the electromagnetic field intensity prediction value output by the LSTM model is obtained as shown in the following table, specifically:

[0081] Time / t t＝1 t＝2 t＝3 t＝4 t＝5 Electromagnetic field strength / x t ]] 102 107 112 117 122

[0082] Step M3: Obtain the smoothing results of different time windows, fuse the smoothing results of different time windows, and dynamically obtain the size of the dynamic threshold corresponding to each moment according to the electromagnetic field intensity prediction value obtained in step M2. In the embodiment, the calculation formula of the dynamic threshold corresponding to each moment is specifically:

[0083]

[0084] wherein Y t is the size of the dynamic threshold corresponding to the t-th moment, ω1 is the weight corresponding to the short-time smoothing result, is the short-time smoothing result corresponding to the t-th moment, ω2 is the weight corresponding to the long-time smoothing result, is the long-time smoothing result corresponding to the t-th moment.

[0085] In the embodiment, the calculation formula of the short-time smoothing result corresponding to the t-th moment is specifically:

[0086]

[0087] wherein: is the short-time smoothing result corresponding to the t-th moment, α d is the short-time smoothing factor, is the electromagnetic field intensity prediction value corresponding to the t-th moment, is the short-time smoothing result corresponding to the t-1-th moment.

[0088] Further, the calculation formula of the long-time smoothing result corresponding to the t-th moment is specifically:

[0089]

[0090] wherein: is the long-time smoothing result corresponding to the t-th moment, α l is the long-time smoothing factor, is the electromagnetic field intensity prediction value corresponding to the t-th moment, is the long-time smoothing result corresponding to the t-1-th moment.

[0091] It is worth noting that the size of the short-time smoothing factor α d is greater than the size of the long-time smoothing factor α l . Meanwhile, in the embodiment, the size of the short-time smoothing factor α d is generally set to 0.1 or 0.2, and the size of the long-time smoothing factor α l is generally set to 0.05 or 0.1.

[0092] In the process of specific implementation, the size of the short-time smoothing factor a d is set to 0.1, the size of the long-time smoothing factor a l is set to 0.05, the size of the weight ω1 corresponding to the short-time smoothing result is set to 0.7, and the size of the weight ω2 corresponding to the long-time smoothing result is set to 0.3.

[0093] That is, according to the initial size of the dynamic threshold value obtained in the above step M1, which is 100, the future time electromagnetic field intensity prediction value obtained in the above step M2, the short-time smoothing results corresponding to the t1th time to the t5th time are respectively:

[0094] t1th time:

[0095] t2th time:

[0096] t3th time:

[0097] t4th time:

[0098] t5th time:

[0099] Similarly, the long-time smoothing results corresponding to the t1th time to the t5th time are respectively:

[0100] t1th time:

[0101] t2th time:

[0102] t3th time:

[0103] t4th time:

[0104] t5th time:

[0105] That is, the dynamic threshold value sizes corresponding to the t1th time to the t5th time are respectively:

[0106] t1th time: Y1=0.7×100.2+0.3×100.1=100.17.

[0107] t2th time: Y2=0.7×100.88+0.3×100.245=100.6545.

[0108] t3th time: Y3=0.7×101.692+0.3×100.48275=101.187175.

[0109] At t4 moment: Y4 = 0.7 * 102.6228 + 0.3 * 100.7636 125 = 101.7808576.

[0110] At t5 moment: Y5 = 0.7 * 103.66052 + 0.3 * 101.0754 319 = 102.4339176.

[0111] Step S1.3: generating a warning signal. That is, comparing the real-time monitored electromagnetic field intensity data with the electromagnetic dynamic threshold size determined in step M3 corresponding to different moments, when the real-time monitored electromagnetic field intensity data is different from the electromagnetic dynamic threshold size, it is determined that the electromagnetic field intensity data exceeds the range of the electromagnetic dynamic threshold size.

[0112] Specifically, when the electromagnetic field intensity data exceeds 10% of the electromagnetic dynamic threshold size, a warning signal of slight interference is generated, and at this time the robot can continue to run.

[0113] When the electromagnetic field intensity data exceeds 30% of the electromagnetic dynamic threshold size, a warning signal of medium interference is generated, and at this time the control parameters of the robot need to be adjusted.

[0114] When the electromagnetic field intensity data exceeds 50% of the electromagnetic dynamic threshold size, a warning signal of serious interference is generated, and at this time the robot stops running.

[0115] Step S1.4: obtaining electromagnetic environment change parameters. That is, through the environment monitoring system built in step S1.1, the sensor data obtained can be obtained, that is, the real-time monitored electromagnetic field intensity, temperature, humidity and air pressure data can be obtained. That is, by calculating the change rate of the electromagnetic field intensity corresponding to each moment, the interference degree of the electromagnetic field to the running of the robot at each moment during the running of the robot can be determined. Further, by obtaining the cumulative change of the electromagnetic field intensity, the interference situation in a long time can be determined.

[0116] In the process of specific implementation, the data in the following table can be obtained, specifically:

[0117] Time Electromagnetic field strength Temperature Humidity Barometric pressure t＝1 100 20 50 1013 t＝2 105 22 52 1012 t＝3 110 21 51 1011 t＝4 115 23 53 1010 t＝5 120 24 54 1009

[0118] According to the data in the above table and the dynamic threshold size obtained in the above process, it can be known that:

[0119] At t1 moment: the real-time monitored electromagnetic field intensity is 100, and the dynamic threshold size is 100.17, so the exceeding range between them is 0.0017.

[0120] That is, the electromagnetic field intensity at the t1 moment does not trigger the generation of the early warning signal.

[0121] At t2 moment: the real-time monitored electromagnetic field intensity is 105, and the dynamic threshold size is 100.6545, so the exceeding range between them at this time is 0.0432.

[0122] That is, the electromagnetic field intensity at the t2 moment does not trigger the generation of the early warning signal.

[0123] At t3 moment: the real-time monitored electromagnetic field intensity is 110, and the dynamic threshold size is 101.187175, so the exceeding range between them at this time is 0.0871.

[0124] That is, the electromagnetic field intensity at the t3 moment does not trigger the generation of the early warning signal.

[0125] At t4 moment: the real-time monitored electromagnetic field intensity is 115, and the dynamic threshold size is 101.7808576, so the exceeding range between them at this time is 0.1299.

[0126] That is, the electromagnetic field intensity at the t4 moment exceeds the range, so the generation of the early warning signal of slight interference is triggered at this time.

[0127] At t5 moment: the real-time monitored electromagnetic field intensity is 120, and the dynamic threshold size is 102.4339176, so the exceeding range between them at this time is 0.1715.

[0128] That is, the electromagnetic field intensity at the t5 moment exceeds the range, so the generation of the early warning signal of slight interference is triggered at this time.

[0129] Further, according to the data in the above table, the size of the electromagnetic environment change parameter is the change rate of the electromagnetic field intensity in the above table, and the result is 20.

[0130] Step S2: determine the operating parameter. That is, according to the level of the early warning signal generated in step S1.3, adjust the control parameters of the robot and the sensitivity of the sensor. Specifically as follows:

[0131] Step S2.1: adjust the inspection speed and frequency of the robot. That is, according to the level of the early warning signal, increase the inspection speed and frequency of the robot. Specifically:

[0132] When the level of the early warning signal is slight interference, the inspection speed and frequency of the robot remain unchanged.

[0133] When the level of the early warning signal is medium interference, the inspection speed of the robot is reduced to 70% of the current speed, and the inspection frequency of the robot is increased by 10%.

[0134] When the warning signal level is severe interference, the robot's inspection speed is reduced to 50% of the current speed, and the robot's inspection frequency is increased by 20%.

[0135] Step S2.2: Adjust sensor sensitivity. This involves adjusting the sensor sensitivity according to the level of the warning signal. Specifically:

[0136] When the warning signal level is minor interference, the sensor sensitivity remains unchanged.

[0137] When the warning signal level is medium interference, the sensor sensitivity increases by 10%.

[0138] When the warning signal level is severe interference, the sensor sensitivity increases by 20%.

[0139] During the specific implementation process, the data in the table below can be obtained, specifically:

[0140] Time Electromagnetic field strength Dynamic threshold Speed Inspection frequency Sensor sensitivity t＝1 100 100.17 1.0 m / s 1 time / hour 1.0 t＝2 105 100.6545 1.0 m / s 1 time / hour 1.0 t＝3 110 101.187175 1.0 m / s 1 time / hour 1.0 t＝4 115 101.7808576 1.0 m / s 1 time / hour 1.0 t＝5 120 102.4339176 1.0 m / s 1 time / hour 1.0

[0141] According to the data in the table above:

[0142] If no warning signal is triggered at time t1, the robot's inspection speed and frequency remain unchanged, and the sensor sensitivity remains unchanged.

[0143] If no warning signal is triggered at time t2, the robot's inspection speed and frequency remain unchanged, and the sensor sensitivity remains unchanged.

[0144] If no warning signal is triggered at time t3, the robot's inspection speed and frequency remain unchanged, and the sensor sensitivity remains unchanged.

[0145] If a warning signal for a slight disturbance is triggered at time t4, the robot's inspection speed and frequency will remain unchanged, and the sensor sensitivity will remain unchanged.

[0146] If a warning signal for a slight disturbance is triggered at time t5, the robot's inspection speed and frequency will remain unchanged, and the sensor sensitivity will remain unchanged.

[0147] Step S3: Set up an inspection avoidance strategy. This involves identifying strong electromagnetic field areas within the substation's operating environment by analyzing electromagnetic environment change parameters. Based on these areas and the operating parameters determined in Step S2, the robot's inspection path is planned. This effectively prevents the robot from operating in high-risk areas, ensuring the safety and effectiveness of the inspection task. Details are as follows:

[0148] Step S3.1: comparing the electromagnetic environment change parameter obtained in step S1.4 with a preset electromagnetic field intensity threshold, and dividing the working area corresponding to the electromagnetic environment change parameter according to the comparison result. It is worth noting that the preset electromagnetic field intensity threshold is generally set to 100 μT, 200 μT and 300 μT. That is, according to the different sizes of the electromagnetic field intensity threshold, the obtained electromagnetic environment change parameter is divided into different ranges, which are as follows:

[0149] Normal area: electromagnetic environment change parameter < 100 μT.

[0150] Mild interference area: 100 μT ≤ electromagnetic environment change parameter < 200 μT.

[0151] Moderate interference area: 200 μT ≤ electromagnetic environment change parameter < 300 μT.

[0152] Strong interference area: electromagnetic environment change parameter ≥ 300 μT.

[0153] Step S3.2: adjusting the initial inspection path according to the interference area determined in step S3.1 to avoid the determined interference area, to ensure the safety and effectiveness of the robot inspection work. Specifically as follows:

[0154] Step N1: according to the plan of the substation, the inspection area in the substation is divided into grid, and the coordinate position corresponding to each inspection area is determined.

[0155] Further, according to the division range of the electromagnetic environment change parameter determined in step S3.1, it is corresponding to each inspection area, so as to determine the interference level corresponding to each inspection area. Specifically, the interference level of normal area is 0, the interference level of mild interference area is 1, the interference level of moderate interference area is 2, and the interference level of strong interference area is 3.

[0156] Step N2: according to the coordinate position and interference level corresponding to each inspection area determined in step N1, the cost input corresponding to each position is determined. In this embodiment, the calculation formula of the cost input is as follows:

[0157]

[0158] V(i, j) = C(i, j) + min(V(i - 1, j), V(i, j - 1)) + d + η * R(i, j), wherein: V(i, j) is a cost input from the starting point to the i-th row and j-th column in the grid, C(i, j) is a cost from the previous step to the i-th row and j-th column in the grid, V(i - 1, j) is a cost input from the starting point to the i - 1-th row and j-th column in the grid, V(i, j - 1) is a cost input from the starting point to the i-th row and j - 1-th column in the grid, min(V(i - 1, j), V(i, j - 1)) is the minimum cost of the upper position and the left position to the current position, d is a fixed distance cost, η is a weight of the electromagnetic field strength, and R(i, j) is an electromagnetic field strength level of the i-th row and j-th column in the grid.

[0159] Step N3: According to the initial position of the robot starting and the target position to be inspected, the target position is traced back to the initial position. Specifically, according to the cost input corresponding to each position obtained in step N2, starting from the target position, all cost inputs at the adjacent previous positions are determined step by step, and the minimum cost input is determined from all cost inputs at the previous positions. The previous position corresponding to the minimum cost input is the inspection position in the inspection process. At the same time, the process is repeated until it is traced back to the initial position. That is, in the process of backtracking from the target position, the connection line of all previous positions obtained is the inspection path of the robot from the initial position to the target position.

[0160] In the process of specific implementation, reference is made to Figures 4-7 , wherein Figure 4 is a grid distribution diagram after grid division of the area to be inspected in the transformer substation, Figure 5 is a distribution diagram of electromagnetic field strength corresponding to each area, Figure 6 is a distribution diagram of interference level corresponding to each area, Figure 7 is an inspection path diagram determined for each area; according to the data in Figures 4-6 , it can be known (taking the data position in Figure 4 as an example, which is specifically described as follows):

[0161] The cost input corresponding to the first row is:

[0162] V(1, 1) = 0.

[0163] V(1, 2) = V(1, 1) + 1 + 10 * 1 = 11.

[0164] V(1, 3) = V(1, 2) + 1 + 10 * 3 = 42.

[0165] V(1, 4) = V(1, 3) + 1 + 10 * 1 = 53.

[0166] The cost input corresponding to the second row is:

[0167] V(2, 1) = V(1, 1) + 1 + 10 * 3 = 31.

[0168] V(2, 2) = min(V(2, 1), V(1, 2)) + 1 + 10 * 0 = 11 + 1 + 0 = 12.

[0169] V(2, 3) = min(V(2, 2), V(1, 3)) + 1 + 10 * 0 = 12 + 1 + 0 = 13.

[0170] V(2, 4) = min(V(2, 3), V(1, 4)) + 1 + 10 * 1 = 13 + 1 + 10 = 24.

[0171] The cost input corresponding to the third row is:

[0172] V(3, 1) = V(2, 1) + 1 + 10 * 3 = 61.

[0173] V(3, 2) = min(V(3, 1), V(2, 2)) + 1 + 10 * 0 = 12 + 1 + 0 = 13.

[0174] V(3, 3) = min(V(3, 2), V(2, 3)) + 1 + 10 * 0 = 13 + 1 + 0 = 14.

[0175] V(3, 4) = min(V(3, 3), V(2, 4)) + 1 + 10 * 2 = 14 + 1 + 20 = 35.

[0176] The cost input corresponding to the fourth row is:

[0177] V(4, 1) = V(3, 1) + 1 + 10 * 1 = 73.

[0178] V(4, 2) = min(V(4, 1), V(3, 2)) + 1 + 10 * 1 = 13 + 1 + 10 = 24.

[0179] V(4, 3) = min(V(4, 2), V(3, 3)) + 1 + 10 * 3 = 14 + 1 + 30 = 45.

[0180] V(4, 4) = min(V(4, 3), V(3, 4)) + 1 + 10 * 0 = 35 + 1 + 0 = 36.

[0181] In this embodiment, the target position to be inspected is set as (4, 4), and the initial position of starting is set as (1, 1), so according to the cost input obtained above, it can be known that:

[0182] Starting from the cost input V(4, 4) corresponding to the target position, the cost inputs in all the previous adjacent positions are determined one by one. Specifically:

[0183] The size of the cost input V(4, 4) is 36, and all of its previous adjacent positions are position (3, 4) and position (4, 3). In this embodiment, the size of the cost input V(3, 4) is 35, and the size of the cost input V(4, 3) is 45. That is, the previous adjacent position of position (4, 4) selects position (3, 4).

[0184] Further, the size of the cost input V(3, 4) is 35, and all of its previous adjacent positions are position (3, 3) and position (2, 4). In this embodiment, the size of the cost input V(2, 4) is 24, and the size of the cost input V(3, 3) is 14. That is, the previous adjacent position of position (3, 4) selects position (3, 3).

[0185] Further, the size of the cost input V(3, 3) is 14, and all of its previous adjacent positions are position (3, 2) and position (2, 3). In this embodiment, the size of the cost input V(2, 3) is 13, and the size of the cost input V(3, 2) is 13. That is, the previous adjacent position of position (3, 4) selects position (2, 3) or position (3, 2).

[0186] Notably, at this time, the inspection path can be further determined by the size of the previous adjacent position of position (2, 3) and the previous adjacent position of position (3, 2). Specifically, position (2, 3) has all of its previous adjacent positions as position (2, 2) and position (1, 3). In this embodiment, the size of the cost input V(2, 2) is 12, and the size of the cost input V(1, 3) is 42. That is, the previous adjacent position of position (2, 3) selects position (2, 2).

[0187] Similarly, position (3, 2) has all of its previous adjacent positions as position (3, 1) and position (2, 2). In this embodiment, the size of the cost input V(2, 2) is 12, and the size of the cost input V(3, 1) is 62. That is, the previous adjacent position of position (3, 2) selects position (2, 2).

[0188] That is, the cost of reaching position (3, 2) and position (2, 3) from position (2, 2) is the same, so position (3, 2) or position (2, 3) can be selected here.

[0189] Further, the size of the cost input V(2, 2) is 12, and all of its previous adjacent positions are position (1, 2) and position (2, 1). In this embodiment, the size of the cost input V(1, 2) is 11, and the size of the cost input V(2, 1) is 31. That is, the previous adjacent position of position (2, 2) selects position (1, 2).

[0190] Further, the cost input V(1,2) has a size of 11, and all of its previous adjacent positions are the position (1,1), i.e., the initial position of the start. Thus, the position obtained according to the above comparison can obtain the required inspection path, that is, the inspection path determined in the embodiment is:

[0191] (1,1)→(1,2)→(2,2)→(3,2) / (2,3)→(3,3)→(3,4)→(4,4).

[0192] That is, the inspection path corresponds to the area:

[0193] A→B→F→G / J→K→L→P.

[0194] Step S4: dual-arm cooperative operation. That is, according to the inspection path determined in step N3, the robot reaches the target position from the starting position and performs the pipe busbar cable mounting / dismounting operation at the target position. The specific process is as follows:

[0195] Step S4.1: environment modeling. That is, the surrounding environment of the pipe busbar in the substation is modeled in three dimensions by a laser radar, and the parking position of the lifting work vehicle is determined according to the three-dimensional modeling result. It is worth noting that the robot is arranged on the upper part of the lifting work vehicle, as shown in Figure 8 is placed.

[0196] That is, according to the position of the pipe busbar in the substation, the parking position of the lifting work vehicle is determined, and the lifting height position of the lifting work vehicle is determined by scanning the pipe busbar three-phase line with a laser radar. At the same time, safety fences are established for the remaining two phases to ensure a safe distance for the robot to perform the cable operation.

[0197] Step S4.2: cable mounting / dismounting operation. That is, the main arm of the robot is controlled to hang the trolley and equalize the potential, and at the same time, the slave arm of the robot performs visual assistance, i.e., during the hanging of the trolley and the equalization of the potential by the main arm, the slave arm performs visual observation to ensure the accuracy of the operation of the main arm.

[0198] Further, when the cable mounting / dismounting operation is performed, after the hanging of the trolley and the equalization of the potential are completed, the slave arm grasps the clamping device to fix the cable, to ensure the stability of the cable. At the same time, the main arm grasps the polishing device to polish the rust and stains on the cable, to ensure the cleanliness of the contact surface. Then the main arm grasps the bolt tool and the slave arm grasps the nut tool to perforate and tighten the bolt and nut, so that the cable mounting / dismounting operation is completed. It is worth noting that when the cable dismounting operation is performed, after the hanging of the trolley and the equalization of the potential are completed, the main arm takes the bolt tool and the slave arm takes the nut tool, and then the bolt and nut are sequentially removed, so that the cable dismounting operation is completed.

[0199] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the embodiments disclosed except insofar as recited in the claims.

Claims

1. A method suitable for a substation bus bar clamp live machine robot dual-arm collaborative work, characterized in that, The method comprises the following steps: establishing an environment monitoring module to monitor the electromagnetic environment changes in the substation, determine the electromagnetic change threshold, compare the electromagnetic change threshold with the dynamic threshold, obtain the electromagnetic environment change parameter and the early warning signal; determining the operation parameter: through the early warning signal, adjust the control parameter of the robot and the sensitivity of the sensor; setting the inspection avoidance strategy: through the electromagnetic environment change parameter, determine the strong electromagnetic field area in the working environment of the substation, and plan the inspection path of the robot according to the strong electromagnetic field area and the operation parameter, which comprises the following steps: compare the electromagnetic environment change parameter with the electromagnetic field intensity threshold, and according to the comparison result, divide the working area corresponding to the electromagnetic environment change parameter into normal area, slight interference area, moderate interference area and strong interference area; determine the interference level corresponding to each working area according to the division result of the working area, and obtain the cost input corresponding to each working area, and determine the inspection path according to the cost input, the calculation formula of the cost input is as follows: wherein: V(i,j) is the cost input from the starting point to the i-th row and j-th column of the grid, C(i,j) is the cost from the previous step to the i-th row and j-th column of the grid, V(i-1,j) is the cost input from the starting point to the i-1-th row and j-th column of the grid, V(i,j-1) is the cost input from the starting point to the i-th row and j-1-th column of the grid, min(V(i-1,j),V(i,j-1)) is the minimum cost of the upper position and the left position to the current position, d is the fixed distance cost, η is the weight of the electromagnetic field intensity, and R(i,j) is the electromagnetic field intensity level of the i-th row and j-th column position in the grid; double-arm cooperative work: according to the inspection path, the robot reaches the target position and performs the pipe busbar / line connecting and disconnecting work at the target position.

2. The method for the coordinated operation of the dual arms of the live-line robot suitable for the bus clamp of the substation according to claim 1, characterized in that, obtaining the electromagnetic environment change parameter and the early warning signal, which comprises the following steps: building an environment monitoring system: setting a sensor node in the substation and obtaining sensor data; determining the dynamic threshold of the electromagnetic field: through the sensor data, obtaining the electromagnetic field intensity prediction value, and according to the electromagnetic field intensity prediction value and the smoothing result of different time windows, determining the electromagnetic dynamic threshold corresponding to each time; generating the early warning signal: comparing the real-time obtained electromagnetic field intensity data with the electromagnetic dynamic threshold, when the electromagnetic field intensity data is different from the electromagnetic dynamic threshold, determining the range size of the electromagnetic field intensity data exceeding the electromagnetic dynamic threshold, which is as follows: when the electromagnetic field intensity data exceeds the electromagnetic dynamic threshold by 10%, a slight interference early warning signal is generated; when the electromagnetic field intensity data exceeds the electromagnetic dynamic threshold by 30%, a moderate interference early warning signal is generated; when the electromagnetic field intensity data exceeds the electromagnetic dynamic threshold by 50%, a serious interference early warning signal is generated; Obtaining electromagnetic environment change parameters: through the sensor data, the change size of electromagnetic field intensity data between two adjacent time points is obtained, and the change rate of electromagnetic field intensity data within the monitoring time is determined according to the monitoring time.

3. The method of claim 2, wherein the method is applied to a substation tubular bus clamp charging robot dual-arm collaborative work, characterized in that, The electromagnetic dynamic threshold corresponding to each time point is determined, including: M1: obtaining the average value of electromagnetic field intensity data according to the electromagnetic field intensity data in the sensor data, which is the initial dynamic threshold size; M2: setting a machine learning model, and inputting the electromagnetic field intensity data into the machine learning model to output the predicted value of the electromagnetic field intensity data; M3: according to the initial dynamic threshold and the predicted value of the electromagnetic field intensity data, the short-time smoothing result and the long-time smoothing result corresponding to each time point are obtained, and the short-time smoothing result and the long-time smoothing result are fused to obtain the dynamic threshold corresponding to each time point, specifically: Wherein: Y t is the dynamic threshold size corresponding to the t time, ω1 is the weight corresponding to the short time smoothing result, is the short time smoothing result corresponding to the t time, ω2 is the weight corresponding to the long time smoothing result, is the long time smoothing result corresponding to the t time.

4. The method of claim 3, wherein the method is applied to a substation tubular bus clamp charging robot dual-arm collaborative work, characterized in that, The smoothing result corresponding to each time point is specifically: Wherein: y t is the smoothing result corresponding to the t time, and a is a smoothing factor, is the electromagnetic field intensity prediction value corresponding to the t time, y t-1 is the smoothing result corresponding to the t-1 time.

5. The method for the double-arm collaborative work of the live-line robot and the bus clamp of the substation according to claim 3 or 4, characterized in that, The smoothing factors corresponding to the smoothing results at different times satisfy the following relationship, specifically: The size of the smoothing factor corresponding to the short-time smoothing result is greater than the size of the smoothing factor corresponding to the long-time smoothing result.

6. The method for the coordinated operation of the dual arms of the live-line robot suitable for the tubular bus clamp of the substation according to claim 1, characterized in that, Adjusting the control parameters of the robot and the sensitivity of the sensor includes: Adjusting the inspection speed and frequency of the robot: according to the level of the early warning signal, increasing the inspection speed and frequency of the robot, specifically: When the level of the early warning signal is slight interference, the inspection speed and frequency of the robot remain unchanged; When the level of the early warning signal is moderate interference, the inspection speed of the robot is reduced by 30%, and the inspection frequency is increased by 10%; When the level of the early warning signal is severe interference, the inspection speed of the robot is reduced by 50%, and the inspection frequency is increased by 20%; Adjusting the sensitivity of the sensor: according to the level of the early warning signal, increasing the sensitivity of the sensor, specifically: When the level of the early warning signal is slight interference, the sensitivity of the sensor remains unchanged; When the level of the early warning signal is moderate interference, the sensitivity of the sensor is increased by 10%; When the level of the early warning signal is severe interference, the sensitivity of the sensor is increased by 20%.

7. The method of claim 1, wherein the method is applied to a substation tubular bus clamp live machine robot dual-arm collaborative work, characterized in that, According to the cost input, the inspection path is determined, including: N1: dividing the inspection area in the transformer substation into grids, and determining the coordinate position corresponding to each inspection area, and determining the interference level corresponding to each inspection area according to the division result of the working area; N2: according to the coordinate position and the interference level, the cost input corresponding to each coordinate position is obtained; N3: according to the target position of the robot, the cost input of all previous positions corresponding to the target position is obtained, and the minimum cost input is determined from all the cost inputs of the previous positions, and the position corresponding to the minimum cost input is the inspection position on the inspection path, and step N3 is repeated until the position corresponding to the minimum cost input is the starting position of the robot.

8. The method of claim 1, wherein the method is applied to a substation tubular bus clamp live machine robot dual-arm collaborative work, characterized in that, The electromagnetic field intensity threshold is set to 100 μT, 200 μT and 300 μT.

9. The method of claim 1, wherein the method is applied to a substation tubular bus clamp live machine robot dual-arm collaborative work, characterized in that, The pipe bus connecting / detaching drainage line operation at the target position comprises the following steps: Environment modeling: three-dimensional modeling of the position of the pipe bus in the substation is performed by laser radar, and the parking position and the lifting height position of the lifting operation vehicle are determined according to the three-dimensional modeling; Connecting / detaching drainage line operation: the robot is transported to the pipe bus position through the parking position and the lifting height position of the lifting operation vehicle, the main arm of the robot is hung with a trolley and operated at the same potential, the slave arm of the robot is visually assisted, and the running state of the main arm and the slave arm of the robot is determined according to the classification of the connecting / detaching operation, specifically: When the connecting / detaching operation is performed: The slave arm fixes the drainage plate, the main arm grinds the drainage plate, the main arm grabs the bolt tool, the slave arm grabs the nut tool, and the bolt tool and the nut tool are perforated and tightened; When the connecting / detaching operation is performed: The main arm takes the bolt tool, the slave arm takes the nut tool, and the bolt tool and the nut tool are removed.

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