Robot and insulation protection and safety control method thereof

Abstract

The invention discloses a robot and an insulation protection and safety control method thereof, and relates to the technical field of industrial robot safety protection, and the method comprises the steps: detecting the electromagnetic interference intensity in real time, outputting an electric field intensity signal, and carrying out the short-time processing and long-time processing of the electric field intensity signal, so as to obtain a pulse event label and a background fluctuation standard deviation; determining a baseline threshold value according to the background fluctuation standard deviation, and taking a difference value between the electric field intensity signal measurement value and the baseline threshold value as a field intensity deviation; and entering a switching preparation state when the switching preparation condition and the fuzzy decision condition at least meet one of the switching preparation condition and the fuzzy decision condition, and triggering communication mode switching when the electric field intensity signal is greater than an intensity threshold value. The environment electric field intensity can be sensed in real time, communication mode switching is completed in milliseconds, wireless radio frequency is switched to laser communication to replace a traditional wireless link, it is ensured that stable transmission is still kept under the extreme condition that the electric field intensity is larger than or equal to 30 kV / m, and the error rate of the communication link is lower than 10 <-6 >.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of safety protection of industrial robots, and particularly relates to a robot and an insulation protection and safety control method thereof. BACKGROUND

[0002] With the development of industrial automation technology, robots as flexible mobile special robots have been widely applied to high-risk operation scenes such as power inspection and equipment maintenance. In high-voltage equipment dense areas such as thermal power plants, the operation environment needs to withstand 10kV-500kV strong electric field radiation and instantaneous induced voltage, and the electronic system faces serious electromagnetic interference (EMI) threats. Adopting robots to replace manual inspection can significantly reduce the safety risk of personnel exposed to high-voltage equipment, but the adaptability defects of the prior art in the high-voltage electromagnetic environment have become the core bottleneck restricting its reliable application.

[0003] In the related art, communication is mainly performed through a control link of Wi-Fi communication. However, the packet loss rate of this communication mode exceeds 45% in a strong electromagnetic environment, which cannot guarantee the reliable transmission of inspection data and real-time control instructions, and seriously affects the timeliness and accuracy of remote operation. In addition, the electromagnetic shielding effectiveness (30 dB vs 60 dB), instantaneous voltage resistance (15 kV breakdown probability 37%), and fault survival ability (damage rate 89%) do not meet the requirements of the power industry standards. SUMMARY

[0004] The application provides a robot and an insulation protection and safety control method thereof, which can solve the technical problem of low communication reliability in a strong electromagnetic environment in the prior art.

[0005] In a first aspect, the application provides a robot insulation protection and safety control method, which comprises: Real-time detection of electromagnetic interference intensity, output of an electric field intensity signal, and long-time processing and short-time processing of the electric field intensity signal to obtain a background fluctuation standard deviation and a pulse event label; Determination of a baseline threshold according to the background fluctuation standard deviation, and use of the difference between the electric field intensity signal measurement value and the baseline threshold as a field strength deviation; When at least one of the switching preparation condition and the ambiguous decision condition is met, entering a switching preparation state, and triggering communication mode switching when the electric field intensity signal is greater than the intensity threshold; The switching preparation condition is that the duration of the field strength deviation being greater than the deviation threshold exceeds the time threshold, and the pulse event label is a non-transient label, or the pulse event label is a transient label and the spectral characteristics meet the preset condition; The ambiguous decision condition is that the risk intensity value output by the fuzzy decision exceeds the risk threshold.

[0006] In combination with the first aspect, in an implementation, when the pulse event label is a non-transient label, the field strength deviation, the duration, and the non-transient label are taken as inputs for fuzzy decision, and a risk intensity value is outputted; When the pulse event label is a transient label, the field strength deviation, the duration, and the spectral feature are taken as inputs for fuzzy decision, and a risk intensity value is outputted.

[0007] In combination with the first aspect, in an implementation, the electric field intensity signal is subjected to long-time processing and short-time processing respectively to obtain a background fluctuation standard deviation and a pulse event label, specifically including: The electric field intensity signal is subjected to slow channel processing to perform background estimation, and a background fluctuation standard deviation is obtained; The electric field intensity signal is subjected to fast channel processing to perform pulse detection, and a pulse event label is outputted.

[0008] In combination with the first aspect, in an implementation, when entering the switching preparation state, further including: The electric field intensity signal is subjected to fuzzy PID control, and the fuzzy PID output is compared with the intensity threshold value.

[0009] In combination with the first aspect, in an implementation, the method further includes: When detecting that the leakage current exceeds the current threshold value, multi-source data acquisition is triggered, and the acquired multi-source data is fused to form a multi-layer grid map, and an electromagnetic interference intensity factor is calculated for each grid cell; Based on the electromagnetic interference intensity factor, path planning is performed on the robot, if the sum of the electromagnetic interference intensity factors of the first preset grid cells on the planned path exceeds the interference threshold value, the switching preparation state is entered, and a local bit error rate curve is obtained according to the planned path, which is used for motion speed and attitude adjustment.

[0010] In combination with the first aspect, in an implementation, based on the electromagnetic interference intensity factor, path planning is performed on the robot, specifically including: An improved A* algorithm is adopted, and a total cost function is defined based on the electromagnetic interference intensity factor The path of the robot is planned; The electromagnetic interference intensity factor is:

[0011] The total cost function is:

[0012] wherein, is a normalized field strength, for an empirical model-based link error rate estimation, for sensor measurement uncertainty; g(n) is the cost from the starting point to the current grid; h(n) is a heuristic function; ω is a weight coefficient.

[0013] In combination with the first aspect, in an implementation, the method further includes: The robot is provided with a composite insulation protective shell, and the composite insulation protective shell comprises, from inside to outside, a base material layer, a reinforced intermediate layer, and a nano coating layer.

[0014] In a second aspect, the application provides a robot, which comprises an electromagnetic shielding dynamic response system, and the electromagnetic shielding dynamic response system comprises: a strength detection unit configured to detect electromagnetic interference strength in real time and output an electric field strength signal; a risk modeling unit configured to perform long-time processing and short-time processing on the electric field strength signal to obtain a background fluctuation standard deviation and a pulse event label, and determine a baseline threshold value according to the background fluctuation standard deviation, and use the difference between the electric field strength signal measurement value and the baseline threshold value as a field strength deviation; a communication adaptive switching unit configured to enter a switching preparation state when at least one of a switching preparation condition and an ambiguous decision condition is met, and trigger communication mode switching when the electric field strength signal is greater than a strength threshold value; the switching preparation condition is that the duration for which the field strength deviation is greater than a deviation threshold value exceeds a time threshold value, and the pulse event label is a non-transient label, or the pulse event label is a transient label and a spectral feature meets a preset condition; the ambiguous decision condition is that a risk intensity value output by the fuzzy decision exceeds a risk threshold value.

[0015] In combination with the second aspect, in an implementation, the robot further comprises a fault self-withdrawal system, and the fault self-withdrawal system comprises: a fault detection unit configured to detect a leakage current; a path planning and execution unit configured to trigger multi-source data acquisition when the detected leakage current exceeds a current threshold value, fuse the acquired multi-source data to form a multi-layer grid map, and calculate an electromagnetic interference strength factor for each grid unit; the path planning and execution unit is further configured to plan a path for the robot based on the electromagnetic interference strength factor, enter a switching preparation state if the sum of the electromagnetic interference strength factors of the first preset number of grid units on the planned path exceeds an interference threshold value, and obtain a local error rate curve according to the planned path, which is used for motion speed and attitude adjustment.

[0016] In combination with the second aspect, in an embodiment, the robot further comprises: The composite insulation protective shell comprises, from inside to outside, a base material layer, a reinforced intermediate layer, and a nano coating layer.

[0017] The technical scheme provided by the application has the beneficial effects of: By detecting the electromagnetic interference strength in real time, outputting the electric field strength signal, and performing long-time processing and short-time processing on the electric field strength signal to obtain the background fluctuation standard deviation and the pulse event label, the baseline threshold is determined according to the background fluctuation standard deviation, and the difference between the electric field strength signal measurement value and the baseline threshold is taken as the field strength deviation. When the switching preparation condition and the ambiguous decision condition at least meet one of them, the switching preparation state is entered, and when the electric field strength signal is greater than the intensity threshold, the communication mode switching is triggered, wherein the switching preparation condition is that the duration of the field strength deviation greater than the deviation threshold exceeds the time threshold, and the pulse event label is a non-transient label, or the pulse event label is a transient label and the spectral feature meets the preset condition. The ambiguous decision condition is that the risk intensity value output by the ambiguous decision exceeds the risk threshold.

[0018] The application can perceive the environmental electric field strength in real time and complete the communication mode switching in milliseconds. By switching from wireless radio frequency to laser communication to replace the traditional wireless link, stable transmission is ensured under extreme conditions of electric field strength ≥ 30 kV / m, and the communication link error rate is less than 10 -6 , effectively avoiding the technical problem of low communication reliability in a strong electromagnetic environment. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The figure is a flowchart of an embodiment of the robot insulation protection and safety control method of the application; Figure 2 The figure is a dynamic response flowchart of the electromagnetic shielding of the embodiment of the application; Figure 3 The figure is a schematic diagram of the architecture of an embodiment of the robot of the application; Figure 4 The figure is a schematic diagram of the architecture of another embodiment of the robot of the application; DETAILED DESCRIPTION In order to enable those skilled in the art to better understand the application scheme, the technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0020] In a first aspect, embodiments of the present application provide a robot insulation protection and safety control method. The robot can be a robot dog.

[0021] In an embodiment, referring to Figure 1 , Figure 1 FIG. 1 is a flowchart of an embodiment of the robot insulation protection and safety control method of the present application. The robot insulation protection and safety control method includes: S1. Real-time detection of electromagnetic interference intensity, output of an electric field intensity signal, and long-time and short-time processing of the electric field intensity signal to obtain a background fluctuation standard deviation and a pulse event label; S2. Determination of a baseline threshold according to the background fluctuation standard deviation, and use of a difference between the electric field intensity signal measurement value and the baseline threshold as a field strength deviation; S3. When at least one of a switching preparation condition and an ambiguous decision condition is met, entering a switching preparation state, and triggering communication mode switching to switch from a current communication mode to a higher frequency communication mode when the electric field intensity signal is greater than an intensity threshold.

[0022] The switching preparation condition is that the duration for which the field strength deviation is greater than a deviation threshold exceeds a time threshold, and the pulse event label is a non-transient label, or the pulse event label is a transient label and a spectrum feature meets a preset condition. The ambiguous decision condition is that a risk intensity value output by the fuzzy decision exceeds a risk threshold.

[0023] It can be understood that when the duration for which the field strength deviation is greater than the deviation threshold exceeds the time threshold, and the pulse event label is a non-transient label, the switching preparation state is entered.

[0024] When the duration for which the field strength deviation is greater than the deviation threshold exceeds the time threshold, the pulse event label is a transient label, and the spectrum feature meets the preset condition, the switching preparation state is entered.

[0025] In addition, when the risk intensity value output by the fuzzy decision exceeds the risk threshold, the switching preparation state is also entered.

[0026] In this embodiment, the electromagnetic interference intensity is detected in real time, and an electric field intensity signal is output. This signal undergoes both long-term and short-term processing to obtain the background fluctuation standard deviation and pulse event labels. A baseline threshold is then determined based on the background fluctuation standard deviation, and the difference between the measured electric field intensity signal and the baseline threshold is used as the field strength deviation. When at least one of the switching preparation condition and the fuzzy decision condition is met, the system enters a switching preparation state. When the electric field intensity signal exceeds the intensity threshold, a communication mode switch is triggered. This allows for real-time sensing of the ambient electric field intensity and completion of the communication mode switch within milliseconds. By switching from radio frequency to laser communication to replace the traditional wireless link, stable transmission is maintained even under extreme conditions with an electric field intensity ≥30kV / m, and the communication link bit error rate is below 10%. -6 This effectively avoids the technical problem of low communication reliability in strong electromagnetic environments.

[0027] Based on the above embodiments, in this embodiment, when the above-mentioned pulse event label is a non-transient label, fuzzy decision-making is performed using field strength deviation, duration and non-transient label as inputs, and the risk intensity value is output.

[0028] When the above-mentioned pulse event label is a transient label, fuzzy decision-making is performed using field strength deviation, duration and spectral characteristics as inputs, and the risk intensity value is output.

[0029] In this embodiment, a risk intensity value is output through rule-based fuzzy decision-making. Specifically, a fuzzy membership function (a value between 0 and 1) is assigned to each of the three conditional parameters in the handover preparation conditions (field strength deviation, duration, and non-transient label or spectral characteristics). Then, fuzzy inference is performed to obtain a comprehensive risk intensity index. ;when When the threshold (e.g., 0.7) is reached, the switching preparation can be triggered directly even if the switching preparation conditions are not fully met, thus avoiding being blocked by strict logic in boundary cases and improving the triggering accuracy.

[0030] Furthermore, in one embodiment, in step S1 above, the electric field intensity signal is subjected to long-time processing and short-time processing respectively to obtain the background fluctuation standard deviation and pulse event label, specifically including: The electric field intensity signal is processed by a slow channel to estimate the background and obtain the standard deviation of the background fluctuation; and the electric field intensity signal is processed by a fast channel to detect the pulse and output the pulse event label.

[0031] Furthermore, in one embodiment, step S3 above, when entering the switching preparation state, further includes: The electric field strength signal is subjected to fuzzy PID control, and the fuzzy PID output is compared with the intensity threshold.

[0032] Optionally, the intensity threshold is a preset safety threshold based on EMC (Electromagnetic Compatibility) standards, link tests and engineering optimization.

[0033] Preferably, the intensity threshold is 30 kV / m.

[0034] In this embodiment, the deviation adjustment and filtering of the electric field intensity signal are realized by fuzzy PID control of the electric field intensity signal, so that the detection is more robust.

[0035] In some embodiments, when insulation failure or electromagnetic interference occurs, there is a lack of fast power-off protection and autonomous evacuation coordination mechanism. In such cases, the equipment damage rate is as high as 89%, significantly increasing the operation and maintenance cost and the risk of operation interruption.

[0036] Further, in this embodiment, the method further includes a fault self-evacuation step, specifically including: When the leakage current is detected to exceed the current threshold, multi-source data acquisition is triggered, and the acquired multi-source data is fused to form a multi-layer grid map, and an electromagnetic interference intensity factor is calculated for each grid cell.

[0037] Then, based on the electromagnetic interference intensity factor, the robot is path planned, and if the sum of the electromagnetic interference intensity factors of the first preset grid cells on the planned path exceeds the interference threshold, the switching preparation state is entered, and a local bit error rate curve is obtained according to the planned path for motion speed and attitude adjustment.

[0038] Further, in an embodiment, based on the electromagnetic interference intensity factor, the robot is path planned, specifically including: An improved A* algorithm is used to define a total cost function based on the electromagnetic interference intensity factor The robot is path planned.

[0039] The electromagnetic interference intensity factor is:

[0040] The total cost function is:

[0041] wherein, is the normalized field strength, is the link bit error rate estimation based on the empirical model, is the sensor measurement uncertainty; g(n) is the cost from the starting point to the current grid; h(n)is a heuristic function; ω is a weight coefficient.

[0042] In this embodiment, when planning the path, the improved A* algorithm by introducing the electromagnetic interference intensity factor can avoid high interference areas while ensuring the shortest path, and forms a linkage with the communication pre-switch mechanism, thereby realizing autonomous and safe evacuation in a fault state.

[0043] In some embodiments, the robot dog mainly uses a single polytetrafluoroethylene (PTFE) insulated shell, and the material structure is highly homogeneous and lacks nano-enhanced modification design. According to the high voltage test standard GB / T 16927.1-2011, the breakdown probability of such design under the action of 15kV or more instantaneous voltage is more than 37%, which cannot meet the insulation requirements of the sudden voltage fluctuation scene in thermal power plants, and the insulation protection system is weak. In addition, the electromagnetic shielding scheme mainly uses fixed metal mesh structure, and the attenuation amount of electromagnetic radiation in the frequency range of 1MHz-1GHz is only 20-30dB, which is significantly different from the ≥60dB shielding effectiveness specified in DL / T 1573-2016 "Electromagnetic Compatibility Requirements for Electric Power Robots", which leads to the internal electronic components being easily disturbed by electromagnetic coupling. Therefore, the electromagnetic compatibility performance is insufficient.

[0044] Further, in this embodiment, the above method further includes: A composite insulation protective shell is provided for the robot, and the composite insulation protective shell comprises, from inside to outside, a base material layer, a reinforced intermediate layer, and a nano coating layer.

[0045] Preferably, the composite insulation protective shell is made of a composite insulation material. The base material layer provides mechanical support, the reinforced intermediate layer improves impact resistance and insulation strength, and the nano coating layer forms a dense insulation screen to form a three-layer synergistic structure.

[0046] In this embodiment, the composite insulation protective shell adopts a three-layer gradient structure design, the core of which is to build a multi-layer composite system with high insulation and anti-electromagnetic interference ability through interface process and interlayer parameter matching. The above-mentioned substrate layer adopts FR-4 epoxy resin glass cloth substrate (dielectric strength ≥ 40 kV / mm, heat distortion temperature 130℃), which provides basic insulation environment while ensuring structural support. The reinforced intermediate layer embeds basalt fiber three-dimensional interwoven network (tensile strength ≥ 3800 MPa, temperature resistance range -260℃ ~ 650℃) with optimized braid angle and density control, which forms an electric field homogenization zone at the interlayer interface, effectively suppresses local electric field distortion, and maintains insulation stability when subjected to mechanical impact or thermal cycle load. The surface layer nanocoating adopts Al2O3-TiO2 composite nanocoating (particle size 50-80nm, thickness 30-80μm) with enhanced interface bonding force by plasma pretreatment, which makes the dielectric constant present a gradient change from 8.0 to 5.5 from inside to outside through optimization of thickness and particle size ratio, and layer-by-layer regulation of Al2O3 and TiO2 volume fraction, and further reduces the local electric field concentration effect by means of the "D=εE continuity" principle to homogenize the interface electric field distribution. At the same time, the polarization effect of nanoparticles accelerates the dissipation of surface charge, and shows better dielectric loss control performance than conventional ceramic coatings under high-frequency electromagnetic radiation.

[0047] Preferably, the above-mentioned nanocoating is an Al2O3-TiO2 composite nanoparticle system, the mass ratio of Al2O3 to TiO2 is 3:2, the particle size range is 50-80nm, and the coating thickness is 30-80μm.

[0048] The method of this embodiment specifically includes: First, a composite insulation protective shell is set.

[0049] In this embodiment, the FR-4 epoxy glass cloth substrate surface is treated by oxygen plasma (80-120 W, 30-60 s) to improve the surface energy to ≥52 mN / m and remove the weak interface layer; basalt fibers are modified by KH-550 silane coupling agent (1.5-2.0 wt%, pH 4.5-5.0 hydrolysis for 20-30 min) and then dried at 110°C for 30 min to enhance the interface bonding between the fibers and the resin; the resin system of the substrate layer adopts bisphenol A epoxy + CTBN 5 phr toughening + nano-SiO2 1.0 wt% dispersion, and is formed by VARTM vacuum infiltration (–0.08 ~ –0.095 MPa, 80°C x 1 h + 120°C x 2 h curing) to realize the integrated co-curing of the substrate layer and the reinforced intermediate layer. The nano-coating part adopts a double dispersant stabilized slurry (PVP 0.3 wt%, BYK-111 0.5 wt%), which is sprayed in multiple passes and then flash evaporated at 90°C for 6-8 min / pass to form a layered structure, with a total thickness of 30-80 μm, and then densified at 120°C x 40 min + 365 nm ultraviolet 2 J / cm 2 curing, and finally treated by fluorosilane 0.5-1.0 wt% to reduce moisture absorption and seepage channels. Through this process, the composite coating surface has a pinhole density of <0.1 per cm 2 , the adhesion reaches the 5B level of the hundred-grade test, and exhibits excellent moisture resistance and electromagnetic shielding performance.

[0050] Through this gradient design and process matching, the interface effect between the three layers is significantly enhanced, and the overall insulation resistance can be improved to 10 14 Ω·cm, and the long-term stability remains better than that of conventional insulation boards in a humid and high-temperature alternating environment. Its insulation breakdown field strength satisfies the formula

[0051] wherein Eb is the breakdown field strength (kV / mm); Vb is the breakdown voltage (kV); and d is the insulation layer thickness (mm).

[0052] Experiments show that the breakdown field strength of the composite insulation shell is about 2.3 times higher than that of a single layer of FR-4, and the shielding effectiveness SE is ≥85 dB in the 10 MHz-1 GHz frequency band, and the surface charge decay time is significantly shortened. In complex working conditions such as strong electromagnetic interference, thermal cycling and mechanical impact, the composite system exhibits comprehensive performance of high insulation (≥45 kV / mm), high shielding (≥85 dB) and high interface reliability, which is significantly better than ordinary three-layer insulation structures.

[0053] Secondly, the dynamic response control of electromagnetic shielding.

[0054] In this embodiment, the electromagnetic shielding dynamic response control aims to ensure the control and data link continuity of the robot in a strong electromagnetic interference environment. The design goals include: real-time and regional perception of environmental electromagnetic intensity; predictive switching before the radio frequency link begins to degrade; avoiding false triggering caused by transient pulses; ensuring that key control messages are not lost and the delay is controllable (total switching delay ≤ 20 ms) during the switching process; and providing a safe laser link establishment and failure fallback mechanism.

[0055] Specifically, in this embodiment, a 16-way distributed electric field sensor (mixed Hall type and electric field probe) is used, with a single-point detection range of 1 kV / m-100 kV / m and a sampling rate of 1 kHz. The sensors are arranged equidistantly around the body, supporting spatial interpolation to obtain the local electric field distribution (two-dimensional grid mapping), and outputting the time series E_i(t).

[0056] In this embodiment, a low-noise amplification and anti-saturation protection circuit is preposed in front of each sensor in hardware, and a 14-16 bit ADC is used to preserve small fluctuations.

[0057] Further, the output time series enters the risk modeling unit, which performs double-channel processing on the electric field intensity time series E_i(t), respectively completing pulse detection in the fast channel, background noise estimation and adaptive threshold calculation in the slow channel, and combining small window discrete wavelet and FFT energy statistics for transient discrimination, based on short-time FFT or wavelet energy statistics for spectral proportion judgment, and based on small window discrete wavelet or envelope statistics to identify high-amplitude pulse events and label them as 'transient', and also generate electromagnetic interference risk factors and interference levels. The output of the risk modeling unit serves as the input of the communication adaptive switching unit and the fuzzy PID control logic, realizing predictive switching and parameter linkage adjustment.

[0058] Among them, the double-channel processing for each E_i(t) is as follows: the fast channel is short-time envelope detection + low-delay first-order filtering (τ≈1-2 ms) for pulse detection; the slow channel is a sliding window mean (window 20-50 ms) for background estimation and adaptive threshold calculation. Small window discrete wavelet or envelope statistics (such as 1-5 ms scale wavelet packet energy) are used to identify high-amplitude pulse events and label them as 'transient' to suppress false triggering. Based on the weighted average and directionality estimation of multiple sensor readings, the local strongest interference direction and intensity distribution are obtained, which are very important when deciding whether the robot needs to change its posture / avoidance or adjust the beam alignment direction.

[0059] The decision entry is the field strength deviation e(t)=Emeasured(t)-E th , where E this the baseline threshold. The baseline threshold is obtained by adaptive thresholding in this embodiment:

[0060] wherein, is the scene baseline, which is the long-term average field strength baseline of the scene, usually obtained by initial measurement sampling (e.g. sliding average for a few seconds) after the robot enters the scene; is the background fluctuation standard deviation estimated by the slow channel; is the robust factor (typically 2-4) to avoid background noise amplification false triggering.

[0061] The determination passes the "and" logic of three parallel conditions: e(t) > 0; e(t) continuously exceeds the threshold value (the default value of this embodiment is 5 ms) on the fast channel; non-transient, or transient but the spectral characteristics meet the preset conditions to distinguish between one-time pulse and sustained rise. Among them, when transient, accompanied by low-frequency energy rise or spectral proportion change (judged by short-time FFT / wavelet), it is determined that the spectral characteristics meet the preset conditions.

[0062] Specifically, the low-frequency energy refers to the energy component of the signal in a lower frequency band (e.g. 0-200 Hz or 0-500 Hz), mainly from background electric field slow change or continuous interference. Compared with transient pulse (high frequency dominant, very short duration), low-frequency energy rise is a key indicator to distinguish "persistent interference".

[0063] In the fast channel, the input signal is wavelet transformed, and the energy spectrum is taken. In the set low-frequency bandwidth , the low-frequency energy is calculated:

[0064] Compare the current low-frequency energy with the sliding baseline of the previous window:

[0065] If (wherein is the threshold coefficient, is the low-frequency energy fluctuation standard deviation), it is determined that the low-frequency energy rises.

[0066] Further, after the fast channel outputs the short-time spectrum, the low-frequency proportion can be calculated and compared with the set threshold; the spectral proportion change refers to whether the low-frequency energy proportion in the total energy significantly increases, when the low-frequency energy proportion increases ≥10-20% relative to the baseline, it is determined that the "spectral proportion change".

[0067] Optionally, in the fast channel, the input signal is wavelet transformed, and the energy spectrum is taken. .

[0068] Low frequency energy according to the above calculation , the proportion calculation: .

[0069] Get the historical baseline proportion under normal circumstances , calculate the difference: If , it means that the spectrum proportion changes.

[0070] When the switching preparation condition is met, or the risk intensity value of the regularized fuzzy decision output exceeds the risk threshold value, the switching preparation state is entered, so as to ensure rapid response and inhibit false triggering.

[0071] Adopt fuzzy PID controller to calculate the electric field intensity deviation in real time:

[0072] When the electric field intensity detected through the fuzzy PID exceeds the intensity threshold value (30kV / m), trigger the communication mode to switch from wireless radio frequency (2.4GHz, transmission power 20dBm) to laser communication module (wavelength 808nm, beam divergence angle 0.5mrad, receiving sensitivity-45dBm), the switching response time is ≤20ms, ensuring the communication continuity under strong electromagnetic interference. The communication error rate calculation formula is:

[0073] Where: BER is the error rate; E b is the energy per bit; N0 is the noise power spectral density.

[0074] In this embodiment, the BER is less than 10 -9 under high electric field conditions, which is much better than Wi-Fi (>10 -4 ).

[0075] As Figure 2 shown, the above electromagnetic shielding dynamic response process specifically includes: A1. Real-time monitoring of electric field intensity; A2. Whether the electric field intensity exceeds the intensity threshold value, if yes, turn to A3, otherwise turn to A.

[0076] A3. Trigger electromagnetic shielding response; A4. Start high-frequency communication mode; A5. Close low-frequency communication mode; A6. Activate the controllable shielding function unit in the electromagnetic shielding dynamic response system; A7. Continuously monitor the electric field intensity; A8. Determine whether the electric field intensity returns to normal, if yes, turn to A9, otherwise turn to A7.

[0077] A9. Resuming low frequency communication mode; A10. Turning off the controllable shielding function unit.

[0078] Finally, the fault self-withdrawal function is realized through the fault self-withdrawal mechanism.

[0079] In this embodiment, the path planning is extended to a real-time, communication-aware and safety-constrained closed-loop system. The overall process includes: fault triggering - rapid environment perception and risk mapping - real-time costed path planning - predictive communication presetting and parallel alignment - controlled execution and online re-planning - fallback / fault-tolerant processing.

[0080] Specifically, when the fault detection unit reports a leakage current > 5 mA or other dangerous events, the fault self-withdrawal system switches to the emergency power supply within 0.3 s and enters the withdrawal process. The data acquisition triggered in parallel includes: 16-channel electric field array (E_i), IMU and odometry, vision and depth sensors, terrain grid and current communication link quality indicators (RSSI, BER estimate, packet loss rate). These information are fused into a multi-layer grid map (occupancy + EMI risk + comm quality + terrain cost) within a short time window (e.g. last 100-200 ms).

[0081] Define the electromagnetic interference intensity factor of each grid cell:

[0082] where is the normalized field strength, is the link error rate estimate based on empirical model, is the sensor measurement uncertainty (variance); function Weighted or nonlinear mapping can be used to reflect the stepwise nature of risk.

[0083] In this embodiment, by introducing three types of factors: electric field strength normalization indicator, link BER estimate and sensor uncertainty, and reflecting the stepwise characteristics of risk through weighted or nonlinear mapping, the path planning and communication switching are served.

[0084] Optionally, the above function Linear weighting, exponential, Sigmoid or fuzzy membership function, etc. can be used.

[0085] In this embodiment, the improved A* algorithm is started, and the total cost function is defined on the grid cell n:

[0086] Where g(n) is the cost from the starting point to the current grid cell; h(n) is the heuristic function (predicting the remaining cost). ω = 0.7: Electromagnetic interference intensity factor; ω = 0.7: weighting coefficient.

[0087] In this embodiment, the path planning result is not output in isolation, but is shared with the communication units (including radio frequency communication and laser communication) and triggers preset actions. If the cumulative value of the first N_segments (e.g., 2–5) of the planned path is... If the interference threshold is exceeded, the planner of the path planning and execution unit issues a "communication pre-switching" command, and the communication unit performs laser link preheating and coarse alignment in parallel. This mechanism ensures that link switching preparation is completed before the robot enters a high-risk section, avoiding momentary disconnection due to path selection. The planner also sends the expected local BER curve to the controller of the path planning and execution unit for motion speed / attitude adjustment.

[0088] The planner, along the path, incorporates the electromagnetic interference intensity factor of each grid cell. The predicted bit error rate is calculated through the mapping relationship: , Energy per bit; Noise power spectral density. By stitching together the BER values ​​of several future segments, a local BER curve is obtained.

[0089] Define the local BER curve within the time window Rate of change within:

[0090] Set two thresholds: rapid deterioration threshold. and gradual change threshold .like Then determine the rapidly deteriorating state, if If the change is gradual, it is considered to be in a state of slow change; if it is in between, it is considered to be in an intermediate state.

[0091] The controller combines the results of real-time and predicted local BER curves to perform fuzzy PID adjustment on motion speed v and attitude θ: When the curve deteriorates rapidly, the speed v is reduced and the attitude adjustment amplitude Δθ is increased to prioritize communication stability; when the curve changes smoothly, small fine-tuning is maintained to avoid unnecessary energy consumption and speed loss; when the curve is in an intermediate state, medium-intensity adjustment is performed to ensure balance.

[0092] Specifically, the controller receives the path and local BER curve issued by the planner, and uses fuzzy-PID to online adjust the motion speed v and the attitude (orientation θ, tilt): when the current segment EMI increases, the fuzzy rule is used to reduce v and adjust the attitude to improve the antenna or optical pointing stability, where the antenna and the optical correspond to the antenna for wireless radio frequency communication and the optical link for laser communication, respectively. In this embodiment, when the wireless radio frequency link is used, the antenna pointing is adjusted; when the laser link is used, the optical pointing is adjusted; if increases and then the fuzzy output reduces , increases and makes to avoid the overshoot caused by integration and give time for communication switching. The interface between the controller and the communication unit's communication switcher is a four-step protocol of "pre-switch trigger-confirmation-execution", which ensures a compromise between speed and pointing. The actuator uses a dual-redundant drive motor (rotation speed synchronization error < 2%, torque ≥ 8 N·m), which ensures that even if a single channel fails, it can still complete the evacuation, with an average evacuation time ≤ 15 seconds and a success rate ≥ 99.9%.

[0093] The current segment EMI refers to the electromagnetic interference intensity factor in the local interval of the robot path composed of a plurality of consecutive grids, and the weighted average of 2-5 grids is usually taken to avoid excessive sensitivity caused by single grid fluctuation. That is, if the weighted average of the current segment electromagnetic interference intensity factor is greater than the weighted average of the last segment electromagnetic interference intensity factor, the motion speed is reduced, and the attitude is adjusted. If the electromagnetic interference intensity factor of the current grid unit is greater than the electromagnetic interference intensity factor of the last grid unit, and the first-order derivative of the electromagnetic interference intensity factor of the current grid unit with respect to time is greater than 0, the PID parameter is reduced, and the motion speed is adjusted by fuzzy-PID.

[0094] Optionally, the specific adjustment proportion is obtained online by fuzzy control rules, and the mapping table maps input quantities such as EMI intensity, EMI increment, speed error, and attitude deviation to output quantities such as speed reduction factor and PID parameter correction amount.

[0095] The method of this embodiment has the following significant advantages: 1. The insulation protection performance is significantly enhanced: through the three-layer structure design of the base material layer, the reinforced intermediate layer, and the nano coating layer, the overall insulation resistance is increased to 10 14 Ω·cm, and the breakdown field strength is increased by 2.3 times compared with a single material system. The electromagnetic shielding effectiveness in the 10 MHz-1GHz frequency band reaches ≥85 dB, meeting and exceeding the requirements of industry standards such as DL / T 1573-2016, significantly reducing the risk of insulation breakdown and arc discharge in a high-voltage environment.

[0096] 2. Electromagnetic interference suppression and communication stability improvement: Adopting electromagnetic shielding dynamic response, it can sense the environmental electric field intensity in real time and complete communication mode switching within milliseconds; by replacing traditional wireless links with laser communication, it ensures stable transmission even under extreme conditions of electric field intensity ≥30 kV / m, with a communication link error rate below 10 -9 , effectively avoiding the defect of Wi-Fi packet loss rate exceeding 45% in high-voltage electromagnetic environments.

[0097] 3. Intelligent adaptive control to improve anti-interference ability: Introducing fuzzy PID control logic, when the electric field intensity after fuzzy PID exceeds the threshold value, the robot can automatically adjust the communication and operation strategy to realize the "monitoring-judgment-response" closed-loop protection. This adaptive mechanism not only improves the adaptability of the system to complex working conditions, but also significantly reduces the probability of control failure caused by interference.

[0098] 4. Enhanced fault safety evacuation capability: Through the cooperative design of fault detection unit and emergency power supply, combined with improved A* algorithm path planning and redundant actuators, it can complete emergency evacuation within ≤15 seconds, with a success rate ≥99.9%. Even in 500 kV ultra-high voltage scenarios, it can ensure the safe exit of the device in case of insulation failure or communication interruption, avoiding damage caused by high-voltage arc breakdown.

[0099] The mode of the embodiment is suitable for robot dog insulation protection and safety control in high-voltage electromagnetic environments. Through the organic combination of composite insulation shell, electromagnetic shielding dynamic response, and intelligent fault self-evacuation mechanism, the robot dog realizes stable operation in strong electromagnetic interference environments such as thermal power plants, substations, and ultra-high voltage transmission facilities.

[0100] In a second aspect, the embodiments of the present application also provide a robot.

[0101] In an embodiment, referring to Figure 3 , Figure 3 is the architecture schematic diagram of an embodiment of the robot of the present application. The robot includes an electromagnetic shielding dynamic response system, which includes a strength detection unit, a risk modeling unit, and a communication adaptive switching unit.

[0102] The strength detection unit is used for real-time detection of electromagnetic interference strength, and outputs an electric field intensity signal.

[0103] The risk modeling unit is used for long-term processing and short-term processing of the electric field intensity signal to obtain a background fluctuation standard deviation and a pulse event label; and determining a baseline threshold value according to the background fluctuation standard deviation, and taking the difference between the electric field intensity signal measurement value and the baseline threshold value as the field strength deviation.

[0104] The communication adaptive switching unit is configured to enter a switching preparation state when at least one of a switching preparation condition and an ambiguous decision condition is met, and trigger a communication mode switching when the electric field intensity signal is greater than the intensity threshold.

[0105] The switching preparation condition is that the field strength deviation is greater than the deviation threshold for a duration exceeding the time threshold, and the pulse event label is a non-transient label, or the pulse event label is a transient label and the spectral feature meets a preset condition; and the ambiguous decision condition is that a risk intensity value output by the ambiguous decision exceeds a risk threshold.

[0106] Further, in an embodiment, the robot further comprises a fault self-withdrawal system, which comprises a fault detection unit and a path planning and execution unit.

[0107] The fault detection unit is configured to detect a leakage current.

[0108] The path planning and execution unit is configured to trigger multi-source data acquisition when the leakage current is detected to exceed a current threshold, fuse the acquired multi-source data to form a multi-layer grid map, and calculate an electromagnetic interference intensity factor for each grid cell. The path planning and execution unit is further configured to plan a path for the robot based on the electromagnetic interference intensity factor, enter a switching preparation state if a sum of electromagnetic interference intensity factors of a preset number of grid cells on the planned path exceeds an interference threshold, and obtain a local bit error rate curve according to the planned path for motion speed and attitude adjustment.

[0109] Further, in an embodiment, the robot further comprises: A composite insulation protective shell, which comprises, from inside to outside, a base material layer, a reinforced intermediate layer, and a nano coating layer.

[0110] Further, in an embodiment, the communication adaptive switching unit is further configured to: When the pulse event label is a non-transient label, perform fuzzy decision with the field strength deviation, the duration, and the non-transient label as inputs, and output a risk intensity value; When the pulse event label is a transient label, perform fuzzy decision with the field strength deviation, the duration, and the spectral feature as inputs, and output a risk intensity value.

[0111] Further, in an embodiment, the risk modeling unit is further configured to: Perform slow channel processing on the electric field intensity signal to estimate a background and obtain a background fluctuation standard deviation; Perform fast channel processing on the electric field intensity signal to detect a pulse and output a pulse event label.

[0112] Furthermore, in one embodiment, the electromagnetic shielding dynamic response system of this embodiment also includes a fuzzy PID control unit, which is used to: perform fuzzy PID control on the electric field strength signal to obtain a fuzzy PID output.

[0113] The aforementioned adaptive communication switching unit is also used to compare the fuzzy PID output with the intensity threshold.

[0114] Furthermore, in one embodiment, the path planning and execution unit is also used for: An improved A* algorithm is adopted, and the total cost function is defined based on the electromagnetic interference intensity factor. Perform path planning for the robot; The above electromagnetic interference intensity factor for:

[0115] The above total cost function for:

[0116] in, To normalize the field strength, For link bit error rate estimation based on empirical models, To address the uncertainty in sensor measurements; g(n) The cost from the starting point to the current grid; h(n) ω is the heuristic function; ω is the weighting coefficient.

[0117] like Figure 4 As shown, in this embodiment, the robot includes an electromagnetic shielding dynamic response system, a fault self-evacuation system, and a composite insulating protective shell for physical protection.

[0118] The electromagnetic shielding dynamic response system includes an intensity detection unit, a risk modeling unit, a fuzzy PID control unit, a communication adaptive switching unit, and a communication unit, which predictively switches the communication link and adjusts the motion control parameters in conjunction with the real-time sensing of electromagnetic interference intensity.

[0119] The aforementioned fault self-evacuation system includes a fault detection unit, an emergency power supply unit, and a path planning and execution unit.

[0120] The aforementioned composite insulating protective shell includes a substrate layer, a reinforcing intermediate layer, and a nano-coating.

[0121] Furthermore, the aforementioned intensity detection unit can be an electric field sensor array. This electric field sensor array is an 8-16 channel electric field sensor array with a sampling frequency ≥1kHz and a detection range covering 10V / m-100kV / m.

[0122] The fuzzy PID control unit can be a fuzzy PID controller. The fuzzy PID control logic is based on the electromagnetic interference intensity, the link error rate and the attitude stability input variables. When the electric field intensity exceeds 5 kV / m or the link error rate exceeds the corresponding threshold, the linkage adjustment of the motion speed and the communication mode is triggered to slow down the interference effect. When the electric field intensity further exceeds 30 kV / m, the forced switching of the communication mode (wireless radio frequency → laser link) is triggered to ensure the continuity of control and data transmission.

[0123] The communication unit includes a radio frequency communication module, a laser communication module, and a communication switcher. The laser communication unit adopts a semiconductor laser transceiver with a wavelength of 808 nm, a transmission rate of ≥100 Mbps, and an error rate of <10 -6 .

[0124] The emergency power supply unit contains a 24V / 50Ah lithium-sulfur battery pack as an emergency power supply, which supports ≥30 minutes of emergency operation, and the evacuation success rate is ≥99.9%.

[0125] The breakdown strength of the composite insulating shell is ≥45 kV / mm, and the electromagnetic shielding effectiveness in the 10MHz-1GHz frequency band is ≥85dB.

[0126] The robot of the embodiment constructs the overall architecture of the multi-layer protection system, realizes the all-around protection of the robot in the high-voltage electromagnetic environment through the collaborative design of the composite insulating protective shell, the electromagnetic shielding dynamic response mechanism and the fault self-evacuation mechanism. The system adopts a three-level linkage logic of “prevention-monitoring-emergency”, forming a closed-loop protection network from physical isolation to intelligent response.

[0127] The core design logic of the multi-layer protection system: based on material innovation, dynamic response as the core, intelligent evacuation as the guarantee, through the composite insulating protective shell to block the electromagnetic penetration path, the electromagnetic shielding dynamic response to adjust the anti-interference strategy in real time, and the fault self-evacuation mechanism to realize active risk avoidance, the three synergistically improve the survival ability in extreme environments.

[0128] To verify the feasibility and environmental adaptability of the robotic dog, three types of embodiments are designed for different voltage level power operation scenes, corresponding to 10kV power distribution environment, 110kV substation and 500kV ultra-high voltage environment respectively. Through differentiated material configuration and system parameter optimization, precise adaptation to complex electromagnetic environments is realized.

[0129] Embodiment 1: Take the robotic dog adapted to 10kV power distribution environment as an example.

[0130] Application scenario: mainly for 10kV outdoor distribution area in urban distribution network, ring network cabinet inspection and other conventional live working scenes. In this environment, the power frequency electric field strength is usually lower than 5kV / m, and the electromagnetic interference is mainly conducted coupling.

[0131] Material and system configuration: 3mm thick epoxy resin-based composite material is used as the main body insulation substrate, and the surface is coated with 15wt% nano zinc oxide (ZnO) doped silicone rubber coating. The interface polarization effect of nano particles improves the dielectric loss tangent (tanδ<0.02). The sensor array adopts an 8-channel basic configuration, including electric field sensors (measurement range 0-10kV / m), temperature sensors (-40~125℃) and vibration sensors (0-500Hz), and the communication unit uses a single laser channel (wavelength 808nm) to realize data backhaul.

[0132] Experimental verification: In the power frequency withstand test platform simulating the 10kV distribution environment, the robot dog continuously runs for 3000 hours, experiences 200 times of operating overvoltage impact (up to 1.2 times the rated voltage), and does not appear insulation breakdown, surface creepage or partial discharge phenomenon (partial discharge amount <5pC), sensor data acquisition error rate <0.5%, communication link error rate <1×10 -6 .

[0133] The insulation life model adopts Weibull distribution:

[0134] Where: P(t): failure probability at time t; η: characteristic life; β: shape parameter.

[0135] Experimental fitting results: η=5200h, β=2.1, indicating that the failure rate increases slowly with time and the reliability is high.

[0136] Example 2, taking a robot dog adapted to 110kV substation as an example.

[0137] Application scenario: for 110kV substation circuit breaker room, transformer area and other strong electromagnetic environment, there are high frequency pulse interference (1MHz-1GHz) and transient electromagnetic field (maximum field strength 50kV / m) in this scene, which requires to strengthen electromagnetic shielding and multi-source data fusion capability.

[0138] Material and system configuration: Add a 0.2 mm thick nickel-based alloy wire mesh shielding layer (shielding effectiveness SE > 60 dB @ 1 GHz) to the base substrate to form a "insulation substrate-shielding layer-protection coating" three-layer composite structure. The sensor array is expanded to 12 channels, with the addition of 2 ultra-high frequency (UHF) partial discharge sensors (300 MHz-3 GHz) and 2 radio frequency interference (RFI) monitoring channels, and the communication unit is upgraded to a dual-laser redundant channel, enabling automatic switching between primary and backup links (switching time < 10 ms). This configuration corresponds to the electromagnetic shielding dynamic response system in the technical solution, focusing on verifying EMI perception and adaptive communication switching capabilities.

[0139] Experimental verification: In the 110 kV substation field test, the robot dog remained stable during the bus short circuit fault simulation (short circuit current 31.5 kA / 2s), with a shielding effectiveness of 65 dB @ 500 MHz and a partial discharge signal recognition accuracy of > 98%. The link survival rate of the dual-laser communication under strong electromagnetic pulse interference was improved to 99.9%, reducing the data packet loss rate by 82% compared to the single-channel configuration.

[0140] Shielding effectiveness formula:

[0141] Test results: E unshielded = 1.0 V / m, E shielded = 0.00056 V / m, SE ≈ 65 dB calculated, meeting the standard requirements.

[0142] Example 3: Robot dog in 500 kV ultra-high voltage environment.

[0143] Application scenario: For ultra-high voltage converter station valve hall, high voltage reactor and other extreme environments, there are ultra-high frequency electromagnetic radiation (300 MHz-10 GHz), space charge accumulation and ±800 kV DC strong electric field (field strength up to 200 kV / m), which requires strict requirements for insulating material corona resistance and safety control response speed.

[0144] Material and system configuration: Three-layer gradient insulation structure: inner layer 4 mm thick polyimide (PI) film (breakdown field strength > 300 kV / mm), middle layer basalt fiber reinforced epoxy resin (tensile strength > 400 MPa), outer layer fluororubber modified polytetrafluoroethylene (PTFE) coating (volume resistivity > 10 16Ω·cm). The integrated dual-CPU redundant architecture is configured with an independent electromagnetic pulse (EMP) protection module, and based on an embedded improved A* path planning algorithm, an EMI risk factor is introduced into the cost function to realize integrated control of obstacle avoidance and electromagnetic protection, and the emergency evacuation response time is compressed to <50 ms. The corresponding technical solution in the fault self-evacuation mechanism system.

[0145] Experimental verification: In the DC withstand voltage test at the 500 kV ultra-high voltage test base, the system withstands 1.1 times the rated voltage (880 kV) for 1 hour without flashover, and the corona inception voltage is increased to 25 kV (40% higher than traditional materials). In 100 times of sudden failure simulation (including insulation failure warning, communication interruption, etc.), the evacuation success rate is 100%, the average evacuation time is 3.2 seconds, which meets the specification requirement of "near zone operation safety distance ≥ 5 meters" of ultra-high voltage equipment.

[0146] Corona inception voltage formula (Peek empirical formula):

[0147] Where: V c is the corona inception voltage; g0 is the critical electric field strength of air; r is the radius of the conductor; D is the distance between the conductors; m is the surface condition coefficient.

[0148] After being modified with a nano coating, the value of m is increased by about 18%, significantly improving the corona inception voltage.

[0149] Through comparison of key indicators of three types of embodiments with existing technologies (see Table 1), the environmental adaptability and performance improvement of the present scheme can be intuitively reflected.

[0150] Table 1 Comparison of technical effects

[0151] Experiments show that the robot dog of the present embodiment can continuously run for 3000 hours without insulation breakdown in a 10 kV power distribution environment; and can withstand 1.1 times the rated voltage for 1 hour without flashover phenomenon in a ±800 kV ultra-high voltage environment. Compared with existing technologies, the insulation failure probability is reduced by two orders of magnitude, and the single maintenance period is extended by 3-4 times, greatly improving the running stability and economy of the robot dog.

[0152] The robot dog of the present embodiment is suitable for different voltage level working environments (10 kV, 110 kV, 500 kV), and provides different system configurations respectively, which can realize differentiated design and precise adaptation, covering various application scenarios from urban power distribution network to ultra-high voltage converter station, and has wide engineering promotion value.

[0153] The functions of each module in the robot correspond to each step in the robot insulation protection and safety control method embodiment, and the functions and implementation processes will not be described here.

[0154] It should be noted that the above-mentioned sequence numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0155] The terms "comprise" and "have" and any variations thereof in the specification and claims of the present application and the above drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device. The terms "first", "second" and "third" and the like descriptions are used to distinguish different objects, and do not represent the order or limit the types of "first", "second" and "third".

[0156] In the description of the embodiments of the present application, "exemplary", "for example" or "for instance" is used to represent as an example, illustration or description. Any embodiment or design scheme described as "exemplary", "for example" or "for instance" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. In fact, the words "exemplary", "for example" or "for instance" are intended to present the relevant concept in a specific way.

[0157] In the description of the embodiments of the present application, unless otherwise specified, " / " represents the meaning of or, for example, A / B can represent A or B; "and / or" in the text only describes the relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can represent: A alone, A and B together, and B alone, in addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0158] In some of the processes described in the embodiments of the present application, a plurality of operations or steps are included in a specific order, but it should be understood that these operations or steps can be executed or executed in parallel without the order in which they appear in the embodiments of the present application, and the serial number of the operation is only used to distinguish each different operation, and the serial number itself does not represent any execution order. In addition, these processes can include more or fewer operations, and these operations or steps can be executed in sequence or in parallel, and these operations or steps can be combined.

[0159] Those skilled in the art can clearly understand the above-mentioned embodiment method can be realized by means of software and the necessary general hardware platform, of course, can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a plurality of instructions for making a terminal device execute the method described in each embodiment of the present application.

[0160] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for robot insulation protection and safety control, characterized in that, The method comprises: Real-time detection of electromagnetic interference intensity, output of an electric field intensity signal, and long-time processing and short-time processing of the electric field intensity signal to obtain a background fluctuation standard deviation and a pulse event label; Determination of a baseline threshold according to the background fluctuation standard deviation, and taking the difference between the electric field intensity signal measurement value and the baseline threshold as a field intensity deviation; When at least one of a switching preparation condition and an ambiguous decision condition is met, entering a switching preparation state, and triggering communication mode switching when the electric field intensity signal is greater than an intensity threshold; The switching preparation condition is that the duration for which the field intensity deviation is greater than a deviation threshold exceeds a time threshold, and the pulse event label is a non-transient label, or the pulse event label is a transient label and the spectral feature meets a preset condition; The ambiguous decision condition is that a risk intensity value output by ambiguous decision exceeds a risk threshold.

2. The robot insulation protection and safety control method of claim 1, wherein: When the pulse event label is a non-transient label, performing ambiguous decision with the field intensity deviation, the duration, and the non-transient label as inputs, and outputting a risk intensity value; When the pulse event label is a transient label, performing ambiguous decision with the field intensity deviation, the duration, and the spectral feature as inputs, and outputting a risk intensity value.

3. The method of robotic isolation guarding and safety control of claim 1, wherein, The long-time processing and short-time processing of the electric field intensity signal to obtain the background fluctuation standard deviation and the pulse event label specifically comprises: Slow channel processing of the electric field intensity signal to obtain a background fluctuation standard deviation; Fast channel processing of the electric field intensity signal to perform pulse detection and output a pulse event label.

4. The method of robotic isolation guarding and safety control of claim 1, wherein, When entering the switching preparation state, further comprising: Fuzzy PID control of the electric field intensity signal, and comparison of the fuzzy PID output with the intensity threshold.

5. The method of robotic isolation guarding and safety control of claim 1, wherein, The method further comprises: When a leakage current exceeding a current threshold is detected, triggering multi-source data acquisition, fusing the acquired multi-source data to form a multi-layer grid map, and calculating an electromagnetic interference intensity factor for each grid cell; Based on the electromagnetic interference intensity factor, path planning for the robot, if the sum of the electromagnetic interference intensity factors of the first preset number of grid cells on the planned path exceeds an interference threshold, entering the switching preparation state, and obtaining a local bit error rate curve according to the planned path, for motion speed and attitude adjustment.

6. The method of robotic isolation guarding and safety control of claim 5, wherein, Based on the electromagnetic interference intensity factor, path planning for the robot specifically comprises: An improved A* algorithm is adopted to define a total cost function based on electromagnetic interference intensity factor Path planning is performed for the robot The electromagnetic interference strength factor is: The total cost function is: wherein, is the normalized field strength, is the link error rate estimate based on the empirical model, is the sensor measurement uncertainty; g(n) is the cost from the start point to the current grid; h(n) is the heuristic function; ω is the weight coefficient.

7. The method of robotic isolation guard and safety control of claim 1, wherein, The method further comprises: Providing the robot with a composite insulation protection shell, the composite insulation protection shell comprising, from the inside out, a base material layer, a reinforced intermediate layer, and a nano coating layer.

8. A robot, characterized in that The robot comprises an electromagnetic shielding dynamic response system, the electromagnetic shielding dynamic response system comprising: An intensity detection unit for real-time detection of electromagnetic interference intensity, output of an electric field intensity signal; A risk modeling unit for long-time processing and short-time processing of the electric field intensity signal to obtain a background fluctuation standard deviation and a pulse event label, and determination of a baseline threshold according to the background fluctuation standard deviation, and taking the difference between the electric field intensity signal measurement value and the baseline threshold as a field intensity deviation; The communication adaptive switching unit is used for entering a switching preparation state when at least one of a switching preparation condition and an ambiguous decision condition is met, and triggering a communication mode switching when the electric field intensity signal is greater than an intensity threshold value; The switching preparation condition is that a duration of the field intensity deviation being greater than a deviation threshold value exceeds a time threshold value, and the pulse event label is a non-transient label, or the pulse event label is a transient label and a spectrum feature meets a preset condition; The ambiguous decision condition is that a risk intensity value output by the ambiguous decision exceeds a risk threshold value.

9. The robot of claim 8, wherein, The robot further comprises a fault self-withdrawal system, and the fault self-withdrawal system comprises: A fault detection unit is used for detecting a leakage current; A path planning and execution unit is used for triggering multi-source data acquisition when the detected leakage current exceeds a current threshold value, fusing the acquired multi-source data to form a multi-layer grid map, and calculating an electromagnetic interference intensity factor for each grid cell; The path planning and execution unit is further used for path planning of the robot based on the electromagnetic interference intensity factor, entering a switching preparation state if a sum of electromagnetic interference intensity factors of a preset number of grid cells on the planned path exceeds an interference threshold value, and acquiring a local bit error rate curve according to the planned path for motion speed and attitude adjustment.

10. The robot of claim 8, wherein, The robot further comprises: A composite insulation protective shell, which comprises, from inside to outside, a base material layer, a reinforced intermediate layer and a nano coating layer.

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