A truck crane high-voltage close-to-live safety early warning method, system and device

Abstract

The application provides a kind of automobile crane high pressure near electric safety early warning method, system and device, belong to engineering machinery safety protection technical field. Including: the electric field signal of high voltage line radiation is collected, the distance is deduced based on electric field intensity attenuation model, and the azimuth of charged body is calculated by MUSIC algorithm;According to the azimuth control laser radar carries out high density scanning within the set angle, obtains the three-dimensional point cloud data of high voltage line;Time synchronization and coordinate registration are carried out to electromagnetic sensor array and laser radar data, and the three-dimensional space model of high voltage line is established after fusion, and the shortest distance between it and the boom is calculated;Voltage grade is identified and basic safety distance is calculated, and after being corrected combined with environmental factors, dynamic safety threshold is obtained by multiplying safety coefficient;According to the real-time distance between the boom and the charged body, trigger hierarchical warning and control action.The application solves the measurement deviation problem of single sensor in complex electromagnetic environment, variable illumination and meteorological working condition.

Description

TECHNICAL FIELD

[0001] The application relates to a high-voltage proximity-to-live safety early warning method, system and device for a truck crane, and belongs to the technical field of safety protection of engineering machinery. BACKGROUND

[0002] With the continuous advancement of urbanization process and the vigorous development of infrastructure construction in China, the number of various large-scale engineering construction projects has increased rapidly, and construction sites are increasingly dense and close to existing power facilities. As a key heavy lifting equipment, truck cranes play an important role in construction activities in the vicinity of high-voltage transmission lines due to their flexibility, wide operating range and other advantages. However, when such equipment performs lifting, rotating and other operations in complex and variable construction environments, its metal boom, sling and other components are extremely vulnerable to invasion into the safety distance range of high-voltage live lines due to improper operation or spatial judgment errors, posing a serious safety threat.

[0003] When a truck crane operates near a live line, a proximity-to-live or line-encounter accident will not only cause expensive equipment damage and lead to a large-scale construction interruption, but also cause disastrous consequences, including but not limited to: high-voltage arc discharge causing a fire, equipment insulation breakdown leading to a short-circuit trip, and even causing on-site operating personnel to be electrocuted, posing a major risk to the stable and reliable operation of the power grid and the safety of people's lives and property. Therefore, developing a high-reliability and high-precision proximity-to-live operation safety early warning system for truck cranes to achieve reliable proximity-to-live early warning has become a key technical problem to be solved to ensure the safety of power facilities and improve the level of construction safety.

[0004] Currently, there are some related technologies for proximity-to-live early warning of truck cranes. Commonly used methods include an early warning method based on an electromagnetic sensor, which determines the distance from a live line by sensing the change in the surrounding magnetic field strength. However, this method is greatly affected by the electromagnetic environment of the engineering machinery, has low ranging accuracy, and has a high false alarm rate in complex power grid environments, making it difficult to meet the high-precision requirements in actual engineering. In addition, ultrasonic ranging technology has also been applied to proximity-to-live distance detection, but its measurement results are significantly affected by factors such as environmental temperature and humidity, ranging angle, etc., resulting in large measurement errors and unable to provide stable and reliable early warning information.

[0005] There is also a proximity-to-live early warning method based on a laser radar, which can improve the ranging accuracy and reaction speed to a certain extent, but the single laser radar solution is prone to missing point cloud data in adverse weather conditions such as rain and fog, resulting in inaccurate distance information output and affecting the early warning effect. At the same time, most existing proximity-to-live early warning systems use fixed threshold early warning and do not consider the differences in safety distances corresponding to different voltage levels, which can easily result in insufficient protection or excessive early warning, and cannot well adapt to complex and variable proximity-to-live operation conditions.

[0006] In summary, the existing near-electricity early warning technology of the automobile crane has many deficiencies, and it is difficult to meet the requirements of high precision, high reliability and strong adaptability of safety warning on the construction site. Therefore, a more advanced and reliable near-electricity operation safety warning system and method are urgently needed, which has great practical significance and application value for improving the intrinsic safety level of the automobile crane in the area close to the live line, ensuring the safe and stable operation of the power grid and the safety of the construction personnel. SUMMARY

[0007] The purpose of the present application is to provide a high-voltage near-electricity safety warning method, system and device for automobile crane, which effectively solves the measurement deviation problem of single sensor in complex electromagnetic environment, variable light and weather conditions, and realizes the purpose of all-weather, real-time dynamic high-precision near-electricity safety warning of high-voltage live body during the operation of automobile crane.

[0008] To achieve the above purpose, the present application realizes the following technical solutions:

[0009] A high-voltage near-electricity safety warning method for automobile crane, comprising the following steps:

[0010] Real-time acquisition of electric field signals radiated by high-voltage lines through an electromagnetic sensor array, distance is back calculated based on an electric field intensity attenuation model, and the azimuth angle of the live body is calculated through the MUSIC algorithm;

[0011] Control the laser radar to perform high-density scanning within a set angle according to the azimuth angle of the live body, and obtain three-dimensional point cloud data of the high-voltage line;

[0012] Time synchronization and coordinate registration of electromagnetic sensor array and laser radar data, fusion to establish a three-dimensional space model of the high-voltage line, and calculation of the real-time shortest distance between the high-voltage line and the boom;

[0013] Identify the voltage level and calculate the basic safety distance, multiply the safety factor after combining with the environmental factors to obtain the dynamic safety threshold;

[0014] According to the dynamic safety threshold and the real-time shortest distance between the boom and the live body, trigger the graded warning and control action.

[0015] Preferably, the electric field intensity attenuation model is as follows:

[0016] ,

[0017] ,

[0018] Wherein, E represents the measured electric field intensity of the electromagnetic sensor array, is the dielectric constant, represents the identified voltage level of the high-voltage line, represents the linear distance between the sensor and the charged body, represents the environmental attenuation factor, is the propagation path length of the electromagnetic wave in the medium, is the angle between the line connecting the laser radar and the charged body and the horizontal plane.

[0019] Preferably, the specific method for calculating the azimuth angle of the charged body by the MUSIC algorithm is as follows:

[0020] The MUSIC algorithm is used to perform eigenvalue decomposition on the covariance matrix of the electric field signal received by the electromagnetic sensor array, the first K large eigenvalues form a signal subspace, and the remaining eigenvalues form a noise subspace. The spatial spectrum function is used to search for the azimuth angle of the charged body:

[0021] ,

[0022] wherein, is the array steering vector, is the azimuth angle of the charged body, is the spatial spectrum function in the MUSIC algorithm, noise subspace, is the conjugate transpose of the noise subspace matrix, is the conjugate transpose of the array steering vector.

[0023] Preferably, the time synchronization and coordinate registration of the electromagnetic sensor array and the laser radar data specifically includes:

[0024] The time stamps of the electromagnetic sensor array data and the multi-beam laser radar data are aligned by linear interpolation;

[0025] The electromagnetic positioning points and the laser point clouds are unified to a coordinate system with the boom root as the origin by using the ICP registration algorithm;

[0026] According to the correction formula, the point cloud offset caused by the rotation of the boom is eliminated, and the correction formula is as follows:

[0027] ,

[0028] ,

[0029] wherein, is the corrected point cloud coordinate, is the rotation correction matrix, is the original collected point cloud coordinate, is the linear velocity of the boom movement, is the time interval for the laser radar to complete a frame of point cloud collection, is the angular velocity of the boom movement.

[0030] The specific scheme for aligning the electromagnetic positioning points and the laser point cloud to the coordinate system with the boom root as the origin by using the ICP registration algorithm is as follows:

[0031] The electromagnetic sensor array data coordinates and the lidar data coordinates are unified to the coordinate system with the boom root as the origin by using the conversion matrix.

[0032] The sum of squared Euclidean distances between the electromagnetic positioning points in the electromagnetic sensor array data and the laser point cloud in the lidar data is minimized:

[0033] ,

[0034] wherein, is a rotation matrix, is a translation vector, is an electromagnetic positioning point, is a laser point cloud point, is the total number of electromagnetic positioning point-laser point cloud point matching pairs participating in registration.

[0035] The dynamic safety threshold is preferably calculated as follows:

[0036] ,

[0037] ,

[0038] ,

[0039] ,

[0040] ,

[0041] wherein, , , and are compensation coefficients, is the dust concentration output by the environmental perception module, represents the identified high-voltage line voltage level, is the deviation of the environmental temperature from the standard temperature, is the relative humidity, is the environmental attenuation factor, is the dynamic safety coefficient.

[0042] The voltage level identification preferably uses a convolutional neural network, the input is the mel spectrum of the electric field intensity, and the output is the voltage level classification result; the convolutional neural network includes 5 convolutional layers and 2 fully connected layers and is trained based on measured spectrum samples in the 10kV-1000kV voltage range.

[0043] ​Preferably, the tiered early warning includes:

[0044] Level 1 warning: When the distance is 1.5 times the dynamic safety threshold, an audio-visual alert is triggered;

[0045] Level 2 warning: When the distance is between the dynamic safety threshold and 1.5 times the dynamic safety threshold, the boom movement speed is limited;

[0046] Level 3 warning: When the distance is less than the dynamic safety threshold, emergency braking is executed.

[0047] A high-voltage proximity safety early warning system for truck cranes includes:

[0048] Distributed electromagnetic sensing array: Consists of flexible triaxial electromagnetic sensors arranged in each segment of the boom, used to collect electric field signals radiated by high-voltage lines;

[0049] Multi-beam lidar: Installed on the rotating gimbal outside the main boom, it is used to collect point cloud data of high-voltage lines. It includes a dual-axis scanning mechanism and a dynamic focusing module. The dynamic focusing module adjusts the point cloud density according to the azimuth angle of the charged body.

[0050] Environmental sensing module: Includes temperature, humidity, air pressure and rain / fog sensors to monitor environmental parameters in real time;

[0051] Central Processing Unit: Includes a built-in charged body positioning engine, spatial mapping module, dynamic threshold calculator, and voltage level identification module. It achieves charged body positioning, coordinate mapping, and safety threshold calculation through multi-physics data fusion. The charged body positioning engine inversely calculates the distance to the charged body based on an electric field attenuation model and calculates the azimuth angle of the charged body using the MUSIC algorithm. The spatial mapping module is based on the azimuth angle of the charged body... Controlling multi-beam lidar in Enhanced scanning of the region; alignment of timestamps between electromagnetic sensor array data and multi-beam lidar data using linear interpolation; unification of electromagnetic positioning points and laser point clouds to a coordinate system with the boom root as the origin using the ICP algorithm; elimination of point cloud offset caused by boom rotation according to the correction formula; the dynamic threshold calculator calculates the safety threshold by constructing a three-dimensional safety distance model. The voltage level identification module identifies voltage levels based on a convolutional neural network.

[0052] A novel grid-based renewable energy transient reactive power support strategy and setting device includes a processor and a memory storing program instructions. The processor is configured to execute the high-voltage proximity safety early warning method for truck cranes when running the program instructions.

[0053] The advantages of this invention are:

[0054] Through the original multi-physics space-time synchronous fusion method, the advantages of electromagnetic and laser sensors are complementary, forming an efficient closed-loop detection process, which significantly improves the recognition success rate of high-voltage thin wires, and fundamentally solves the positioning problem of small targets in complex environments.

[0055] A dynamic threshold calculation system based on deep learning and environment modeling is proposed, which realizes the accurate and intelligent judgment of safety distance. This technology performs particularly well in rain, fog and other extremely harsh environments, and controls the warning error within the industry standard, greatly improving the safety and environmental adaptability of equipment operation.

[0056] By introducing motion parameters and constructing a real-time compensation model, the measurement distortion caused by the motion of the equipment itself is effectively eliminated, so that the system maintains high-precision ranging in dynamic working conditions with continuous motion of the boom, ensuring the accuracy and stability of the whole process measurement. BRIEF DESCRIPTION OF DRAWINGS

[0057] The accompanying drawings are used to provide a further understanding of the present application, and form a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation on the present application.

[0058] Fig. 1 The present application is a method flowchart.

[0059] Fig. 2 The present application is a fusion electromagnetic sensing and laser ranging automobile crane near electric operation safety warning system structure schematic diagram.

[0060] Fig. 3 The present application is a fusion electromagnetic sensing and laser ranging automobile crane near electric operation safety warning system operation flowchart.

[0061] Wherein, 1 is an electromagnetic sensor array, and 2 is a laser radar. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0063] Embodiment 1

[0064] As shown in Figs. 1-3 A high-voltage near-electric safety warning method for automobile cranes, comprising the following steps:

[0065] S1: Real-time acquisition of electric field signals of high-voltage line radiation by electromagnetic sensor array, inverse distance based on electric field intensity attenuation model, and calculation of charged body azimuth angle by MUSIC algorithm;

[0066] S2: Control of laser radar to perform high-density scanning within a set angle according to the charged body azimuth angle, and acquisition of three-dimensional point cloud data of the high-voltage line;

[0067] S3: Time synchronization and coordinate registration of electromagnetic sensor array and laser radar data, fusion to establish a three-dimensional space model of the high-voltage line, and calculation of the real-time shortest distance between the high-voltage line and the boom;

[0068] S4: Identification of voltage level and calculation of basic safety distance, multiplication by safety factor after correction combined with environmental factors to obtain dynamic safety threshold;

[0069] S5: Triggering of graded warning and control action according to the dynamic safety threshold and the real-time shortest distance between the boom and the charged body.

[0070] As a refinement of the above embodiment, the electric field intensity attenuation model is as follows:

[0071] ,

[0072] ,

[0073] wherein, represents the measured electric field intensity of the electromagnetic sensor array, is the medium constant, represents the identified voltage level of the high-voltage line, represents the straight-line distance between the sensor and the charged body, represents the environmental attenuation factor, dynamically calculated by temperature and humidity sensor data, is the propagation path length of electromagnetic waves in the medium.

[0074] As a refinement of the above embodiment, the present application adopts Direction of Arrival (DOA) algorithm to determine the azimuth angle of the charged body, and for the first time applies Multiple Signal Classification (MUSIC) to crane proximity detection, and uses signal subspace orthogonality to improve anti-interference ability; the specific method is as follows:

[0075] S101: Feature decomposition of the covariance matrix of the electric field signals received by the electromagnetic sensor array using the MUSIC algorithm, the formula is as follows:

[0076] ,

[0077] wherein, is the number of elements of the electromagnetic sensor array, is the eigenvector matrix,​ is the conjugate transpose of the noise subspace matrix.

[0078] S102: arranging the eigenvalues in descending order The first K large eigenvalues constitute the signal subspace, and the remaining eigenvalues constitute the noise subspace. The orientation angle of the charged body is searched using a spatial spectrum function:

[0079] ,

[0080] wherein, is the array steering vector, is the orientation angle of the charged body, is the spatial spectrum function in the MUSIC algorithm, is the noise subspace matrix, is the conjugate transpose of the noise subspace matrix, is the conjugate transpose of the array steering vector, and K is the number of actual high-voltage charged bodies.

[0081] The value of K is not a fixed constant, but dynamically matches the number of actual high-voltage charged bodies in the current working environment: first, the number of signal sources is preliminarily judged through the direction and intensity characteristics of the electric field signal, then verified through the amplitude mutation point after the eigenvalue sorting, and finally the value of K is determined to ensure that the signal subspace can accurately correspond to all high-voltage charged bodies to be detected, providing an accurate basis for subsequent orientation angle calculation (searching for the peak value of the spatial spectrum function).

[0082] For example: there are 2 or more parallel high-voltage lines (such as double-circuit transmission lines) in the working environment, at this time K=2 (or more, depending on the number of lines), and the signal subspace is constituted by the first 2 (or more) large eigenvalues.

[0083] As a refinement of the above embodiment, the time synchronization and coordinate registration of the electromagnetic sensor array and the lidar data specifically includes:

[0084] S301: aligning the time stamps of the electromagnetic sensor array data and the multi-beam lidar data through linear interpolation;

[0085] Let the sampling time of the electromagnetic sensor array be , and the scanning frame time of the lidar be . Time alignment is achieved through linear interpolation: (the minimum scanning period of the lidar), the synchronization error is controlled within ±5ms through time stamp calibration, meeting the ranging accuracy requirement (error ≤0.01m) of the boom dynamic motion (maximum linear speed 1m / s), is the step variable of time interpolation, is the upper limit of the total number of interpolations. ​

[0086] S302: The ICP registration algorithm is used to unify the electromagnetic positioning points and the laser point cloud into a coordinate system with the root of the boom as the origin.

[0087] S3021: A transformation matrix is ​​used to unify the coordinates of the electromagnetic sensor array data and the lidar data to a coordinate system with the root of the boom as the origin.

[0088] Electromagnetic sensor array coordinate system : The origin of the electromagnetic sensor array coordinate system is set as the array center at the top of the boom. The X-axis of the electromagnetic sensor array coordinate system is set to point from the top of the boom. The Y-axis of the electromagnetic sensor array coordinate system is set to be perpendicular to... The horizontal lateral direction of the axis; The Z-axis of the electromagnetic sensor array coordinate system is set to vertically upward;

[0089] LiDAR coordinate system : The origin of the lidar coordinate system is set as the center of rotation for the lidar. The X-axis of the lidar coordinate system is set to be along the principal optical axis of the lidar. The Y-axis of the lidar coordinate system is set to a horizontal tangent perpendicular to the principal optical axis. The Z-axis of the lidar coordinate system is set vertically upward (towards...). (axis in the same direction)

[0090] Unified World Coordinate System : To unify the origin of the world coordinate system, it is set as the hinge point at the base of the boom. To unify the world coordinate system, the X-axis is set to point along the ground in the direction of operation; To unify the world coordinate system, the Y-axis is set to be perpendicular to... The horizontal lateral direction of the axis (along the ground); To unify the world coordinate system, the Z-axis is set vertically upward (and...). , (Same direction); unify the "height dimension" benchmark of all sensors to ensure that electromagnetic signals and laser point clouds can be accurately fused in three-dimensional space through coordinate transformation.

[0091] Transformation matrix:

[0092] Electromagnetic sensor array coordinate transformation to a unified world coordinate system: ,in It is a 4×4 transformation matrix, containing the boom length L and elevation angle. Rotation angle Parameters: .

[0093] S3022: Minimize the sum of squared Euclidean distances between the electromagnetic positioning points and the laser point cloud in the lidar data.

[0094] ICP registration algorithm:

[0095] Objective function: Minimize the sum of squared Euclidean distances between the electromagnetic positioning points and the laser point cloud :

[0096] ,

[0097] where, is a 3x3 rotation matrix, is a 3x1 translation vector, is an electromagnetic positioning point, is a laser point cloud point, is the nearest neighbor point in the laser point cloud, is the total number of scene-level point clouds obtained by the original scan of the lidar, is the total number of electromagnetic positioning point-laser point cloud point matching pairs participating in registration. The value of is determined by the number of electromagnetic positioning points and the number of laser point cloud points participating in registration at the same time (n points are selected on the electromagnetic side and n corresponding points are selected on the laser side to form n matching pairs), which is a "point pair number parameter" that associates electromagnetic data and laser point cloud data and supports registration optimization calculation.

[0098] Through iterative optimization (iteration times ≤20 times), the angle error of the registration error is less than or equal to 0.3° and the distance error is less than or equal to 0.1m.

[0099] S303: Eliminate the point cloud offset caused by the rotation of the boom according to the correction formula as follows:

[0100] ,

[0101] ,

[0102] where, is the corrected point cloud coordinate, is the rotation correction matrix, is the original collected point cloud coordinate, is the linear velocity of boom movement, is the time interval for the lidar to complete a frame of point cloud collection, is the angular velocity of boom movement. Experimental verification can reduce the dynamic ranging error from 0.5m to within 0.1m.

[0103] As a refinement of the above embodiment, the dynamic safety threshold The calculation is as follows:

[0104] S401: Basic safety distance Calculation;

[0105] Based on the voltage reference distance of IEC61472 standard, the environmental parameter compensation is introduced:

[0106] ,

[0107] Where, , , And is the compensation coefficient, which is obtained by fitting 100,000 sets of experimental data using the least squares method, is the dust concentration output by the environmental perception module, represents the identified high-voltage line voltage grade, is the deviation of the environmental temperature from the standard temperature (25℃), is the relative humidity.

[0108] Voltage term : Based on the electric field attenuation characteristics, the 0.7 power fitting conforms to the nonlinear relationship between the air breakdown critical distance and the voltage;

[0109] Humidity term : For every 10% increase in humidity, the air breakdown field strength decreases by 5%, so the safety distance needs to be increased by 0.2m;

[0110] Temperature term : For every 10℃ increase in temperature, the air insulation strength decreases by 3%, corresponding to a distance compensation of 0.1m.

[0111] S402: Environmental attenuation factor Correction

[0112] For the electric field propagation attenuation in extreme weather such as rain and fog, a correction coefficient is introduced:

[0113] ,

[0114] The actual safety distance after correction: .

[0115] Experimental verification: when the humidity is 95% and the dust concentration is 20mg / m³, =1.8, the safety distance of 500kV line is corrected from 6.8m to 12.24m, with an error of ≤5% compared with the actual breakdown distance.

[0116] S403: Dynamic safety coefficient

[0117] Adaptive adjustment according to working conditions:

[0118] ,

[0119] The dynamic working condition adaptation unit generates a final safety threshold: ; At the same time, the PPO deep reinforcement learning algorithm is adopted, with the goal of "no false alarm, low false alarm", based on 1000+ historical accident cases and 500,000 sets of simulation data to train the model, realize the real-time self-optimization of the threshold, and reduce the error to below 2.3% in the city dense power grid scene.

[0120] As a refinement of the above embodiment, the voltage level identification module adopts a convolutional neural network (CNN), with the input being the electric field intensity spectrum feature and the output being the voltage level classification result. The model training data includes measured electric field samples under different voltages, distances and environmental conditions. The CNN model adopts a 5-layer convolution + 2-layer fully connected structure, with the input being the Mel-spectrogram of the electric field signal, and the training data including 120,000 sets of measured samples from 10kV to 1000kV. When the signal-to-noise ratio SNR=10dB, the recognition accuracy is 98.7%, which is better than the traditional FFT feature (85.2%).

[0121] As a refinement of the above embodiment, the hierarchical early warning includes:

[0122] First-level warning: when the distance is 1.5 times , trigger an audible and visual prompt;

[0123] Second-level warning: when the distance is between and 1.5 times , limit the speed of the boom movement;

[0124] Third-level warning: when the distance is less than , execute emergency braking.

[0125] It should be noted that the present embodiment has the following technical effects:

[0126] Spacetime synchronous multi-physical field fusion method

[0127] The closed-loop fusion process of "electromagnetic azimuth guidance-laser focusing scanning-dynamic registration correction" is created, and through the combination of MUSIC algorithm and ICP iteration, the positioning problem of high-voltage thin wires (diameter 15-30mm) in complex environments is solved. Compared with the traditional laser single sensor scheme, the recognition success rate is improved from 72% to 99.3%.

[0128] Adaptive safety threshold calculation method

[0129] A dynamic threshold system based on voltage spectrum characteristics (CNN recognition) and environmental attenuation model is constructed to realize the full-link quantitative calculation of "voltage level → reference distance → environmental correction → working condition adaptation". Under extreme conditions such as rain and fog (humidity 95%), the warning error is ≤5%, which is better than the industry standard (≤15%).

[0130] Motion distortion real-time compensation method

[0131] The boom motion parameters (angular velocity, linear velocity) are introduced into the point cloud correction model, and the ranging deviation during dynamic operation is eliminated through coupling calculation of rotation matrix and translation vector, so that the distance measurement accuracy under the motion state of the boom is maintained within 0.1 m.

[0132] Example 2

[0133] As shown in Fig. 2 , a high-voltage near-electricity safety warning system for an automobile crane includes:

[0134] Distributed electromagnetic sensor array: composed of flexible three-axis electromagnetic sensors arranged at each segment of the boom, used to collect electric field signals radiated by high-voltage lines;

[0135] Multi-line laser radar: installed on the outer rotating pan-tilt of the main boom, used to collect high-voltage line point cloud data, including a two-axis scanning mechanism (horizontal rotation range ±180°, pitch angle adjustment range -30°~+60°) and a dynamic focusing module, which adjusts the point cloud density according to the azimuth angle of the live body;

[0136] Environment perception module: contains temperature and humidity, air pressure and rain and fog sensors, which monitor environmental parameters in real time;

[0137] Central processing unit: built-in live body positioning engine, space mapping module, dynamic threshold calculator and voltage level recognition module, which realizes live body positioning, coordinate mapping and safety threshold calculation through multi-physical field data fusion.

[0138] The live body positioning engine reverses the live body distance based on the electric field intensity attenuation model, and calculates the azimuth angle of the live body through the MUSIC algorithm.

[0139] The space mapping module controls the multi-line laser radar to enhance scanning in the degree area based on the azimuth angle of the live body ; aligns the time stamps of the electromagnetic sensor array data and the multi-line laser radar data through linear interpolation; uses the ICP algorithm to unify the electromagnetic positioning points and the laser point cloud to the coordinate system with the boom root as the origin, with a registration error ≤0.3°; eliminates the point cloud offset caused by boom rotation according to the correction formula;

[0140] The dynamic threshold calculator calculates a safety threshold by constructing a three-dimensional safety distance model The voltage level identification module identifies the voltage level based on a convolutional neural network.

[0141] Example 3

[0142] This example takes a 35-ton truck crane near a 220kV high-voltage line as a scene to verify the practical application effect of the application. The operating environment parameters are: temperature 30℃, relative humidity 85%, dust concentration 10mg / m³, no rain and fog. The deployment and running process of each module of the system is as follows:

[0143] I. System deployment details

[0144] Distributed electromagnetic sensor array: 1 flexible three-axis electric field sensor is installed at each of the 2nd, 3rd and 4th segments of the boom (10m, 15m and 20m from the boom root), with a sampling frequency of 1kHz, real-time collection of spatial electric field intensity and gradient data. The sensor measurement range is 0~5000V / m, with an accuracy of ±2%.

[0145] Multi-line laser radar: installed on the outer rotating pan-tilt of the main boom, the double-axis scanning mechanism has a horizontal rotation range of ±180° and a pitch angle adjustment range of -30°~+60°. The default scanning frequency of the dynamic focusing module is 10Hz, and the point cloud density is 50 points / m²; the scanning frequency in high-risk areas can be increased to 40Hz, and the point cloud density is increased to 200 points / m².

[0146] Environmental perception module: integrated with a temperature and humidity sensor (measurement accuracy ±2%RH, ±0.5℃) and a dust sensor (measurement range 0~50mg / m³), with a data output frequency of 10Hz, real-time uploading of environmental parameters to the central processing unit.

[0147] Central processing unit: industrial-grade embedded computer (2.8GHz main frequency, 16GB memory) is used, with built-in live body positioning engine, spatial mapping module, dynamic threshold calculator and voltage level identification module (CNN model is 5-layer convolution + 2-layer fully connected structure, input electric field signal mel spectrum).

[0148] II. Running process

[0149] Step 1: Live body orientation detection

[0150] The electromagnetic sensor array collects the electric field signals radiated by the high-voltage line in real time, and the measured electric field intensity E of the three sensors is 800V / m, 950V / m and 1050V / m respectively.

[0151] Distance backstepping: calculate the distance through the electric field intensity attenuation model, where the medium constant =0.85 (air), environmental attenuation factor ( calculated by RH = 85%, = 10 mg / m³).

[0152] Azimuth calculation: the charged body positioning engine starts the MUSIC algorithm to perform eigenvalue decomposition on the received signal covariance matrix of the 3 array elements, to obtain the signal subspace and the noise subspace , by searching the peak value of the spatial spectrum function, the azimuth of the charged body is determined = 45°.

[0153] Step 2: Laser radar focused scanning

[0154] The spatial mapping module sends the azimuth = 45° to the laser radar dynamic focusing module, which controls it to perform high-density scanning within a 30°~60° (45°±15°) cone angle at a frequency of 40Hz, the point cloud density is increased to 200 points / m², and the three-dimensional point cloud data of the high-voltage line in this area is obtained.

[0155] Step 3: Multi-data fusion modeling

[0156] Time synchronization: through linear interpolation, the time stamps of the electromagnetic sensor array (1kHz) and the laser radar (40Hz) are aligned, the synchronization error is controlled within ±4ms, which meets the ranging accuracy requirement (error ≤0.01m) of the boom dynamic motion (linear speed 0.8m / s).

[0157] Coordinate registration: the ICP algorithm is used to unify the electromagnetic positioning points ( , 45°) and the laser point cloud to the world coordinate system with the boom root as the origin, after 18 iterations, the registration angle error is 0.25°, and the distance error is 0.07m.

[0158] Motion distortion correction: the boom rotates at 0.3 rad / s, the scanning period = 0.1s, the rotation matrix is substituted into the correction formula to eliminate the point cloud offset caused by the boom rotation.

[0159] Three-dimensional modeling: after fusion, the high-voltage line is determined as a single line, the spatial coordinates ( ) and the shortest straight line distance from the boom are preliminarily calculated as 6.2m.

[0160] Step 4: Dynamic safety threshold calculation

[0161] Voltage level identification: the CNN inputs the mel spectrum features of the electric field signal, and outputs the identification result as 220kV (identification accuracy 99.1%).

[0162] Basic safety distance: according to the formula ,

[0163] wherein the compensation coefficient , , , , , , , , calculated .

[0164] Environmental correction: environmental attenuation factor , corrected actual safety distance .

[0165] Safety threshold: take the working condition safety coefficient (lifting operation), the final safety threshold .

[0166] Step 5: hierarchical early warning trigger

[0167] First level warning: when the distance between the boom and the live body is 14.04m (1.5x9.36m), trigger the first level warning: yellow indicator light, 1kHz buzzer starts, prompt "approaching the live body safety range".

[0168] Second level warning: when the distance is reduced to 10.0m ( ), trigger the second level warning: red indicator light, 2kHz buzzer starts, the system sends PWM signal through CAN bus, limits the boom rotation speed from 0.8rad / s to 0.3rad / s.

[0169] Third level warning: when the operation error causes the distance to be reduced to 6.0m ( ), trigger the third level warning: red and green indicator lights alternate flashing, continuous buzzing, central processing unit outputs instructions to control the hydraulic system overflow valve unloading, execute emergency braking, braking distance 0.4m (≤0.5m).

[0170] Three, implementation effect verification

[0171] In this embodiment, the system recognition success rate of 220kV high voltage line is 99.3%, the dynamic ranging accuracy is maintained within 0.08m, the early warning error under rain and fog (humidity 95%) and other extreme environments is ≤4.8%, all are better than the industry standard, which verifies the effectiveness of the invention under complex working conditions.

[0172] The embodiment of the present disclosure further provides an automobile crane high-voltage near-electricity safety early warning device, comprising a processor and a memory. Optionally, the device can further comprise a communication interface and a bus. The processor, the communication interface and the memory can complete mutual communication through the bus. The communication interface can be used for information transmission. The processor can call the logical instructions in the memory to execute the automobile crane high-voltage near-electricity safety early warning method of the above-mentioned embodiment.

[0173] In addition, the logical instructions in the memory described above can be realized in the form of a software functional unit and sold or used as an independent product, and can be stored in a computer readable storage medium.

[0174] The memory, as a computer readable storage medium, can be used to store software programs, computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor executes the program instructions / modules stored in the memory, thereby executing functional applications and data processing, i.e. realizing the automobile crane high-voltage near-electricity safety early warning method in the above-mentioned embodiment.

[0175] The memory can comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required by a function; the data storage area can store data created according to the use of the terminal device, etc. In addition, the memory can comprise a high-speed random access memory and can further comprise a non-volatile memory.

[0176] Finally, it should be noted that: the above only describes the preferred embodiments of the present application and is not used to limit the present application, although the present application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced equivalently. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for high-voltage proximity safety early warning of a truck crane, characterized in that, Includes the following steps: The electric field signal radiated by the high-voltage line is collected in real time by an electromagnetic sensor array. The distance is inferred based on the electric field intensity attenuation model, and the azimuth of the charged body is calculated by the MUSIC algorithm. The lidar is controlled to perform high-density scanning within a set angle based on the azimuth angle of the charged body to obtain the three-dimensional point cloud data of the high-voltage line. The electromagnetic sensor array and lidar data are synchronized in time and registered in coordinates. After fusion, a three-dimensional spatial model of the high-voltage line is established, and the real-time shortest distance between it and the boom is calculated. Identify the voltage level and calculate the basic safety distance, then multiply the result by a safety factor after adjusting for environmental factors to obtain the dynamic safety threshold. Based on the dynamic safety threshold and the real-time shortest distance between the boom and the live conductor, graded early warning and control actions are triggered. The electric field intensity attenuation model is as follows: ， ， in, This represents the measured electric field strength of the electromagnetic sensing array. The dielectric constant is This indicates the identified high-voltage line voltage level. This represents the straight-line distance between the sensor and the charged object. Indicates the environmental degradation factor. The path length of an electromagnetic wave in a medium. The angle between the line connecting the lidar and the charged body and the horizontal plane; The specific method for calculating the azimuth angle of a charged body using the MUSIC algorithm is as follows: The MUSIC algorithm is used to perform eigenvalue decomposition on the covariance matrix of the electric field signal received by the electromagnetic sensor array. The first K largest eigenvalues ​​form the signal subspace, and the remaining eigenvalues ​​form the noise subspace. The azimuth angle of the charged body is then searched using the spatial spectrum function. ， in, For array guiding vector, The azimuth angle of the charged body. For the spatial spectrum function in the MUSIC algorithm, Noise subspace For the conjugate transpose of the noise subspace matrix, This is the conjugate transpose of the array guiding vector; The specific steps for time synchronization and coordinate registration of electromagnetic sensor array and lidar data include: The timestamps of electromagnetic sensor array data and multi-beam lidar data are aligned using linear interpolation. The ICP registration algorithm is used to unify the electromagnetic positioning points and the laser point cloud into a coordinate system with the root of the boom as the origin. The point cloud offset caused by boom rotation is eliminated according to the correction formula, which is as follows: ， ， in, The corrected point cloud coordinates, For rotation correction matrix, The coordinates of the original point cloud data. The linear velocity of the boom motion, The time interval for the lidar to complete one frame of point cloud acquisition. This represents the angular velocity of the boom.

2. The high-voltage proximity safety early warning method for truck cranes according to claim 1, characterized in that, The specific scheme for unifying the electromagnetic positioning points and laser point clouds to a coordinate system with the base of the boom as the origin using the ICP registration algorithm is as follows: A transformation matrix is ​​used to unify the coordinates of the electromagnetic sensor array data and the lidar data into a coordinate system with the root of the boom as the origin. Minimize the sum of squared Euclidean distances between electromagnetic positioning points in the electromagnetic sensor array data and laser point clouds in the lidar data: ， in, Let be a rotation matrix. It is a translation vector. For electromagnetic positioning points, For laser point cloud points, The total number of electromagnetic positioning point-laser point cloud matching pairs participating in the registration.

3. The high-voltage proximity safety early warning method for truck cranes according to claim 1, characterized in that, The dynamic security threshold The calculation method is as follows: ， ， ， ， ， in, , , and For compensation coefficient, The dust concentration output by the environmental sensing module. This indicates the identified high-voltage line voltage level. The deviation between ambient temperature and standard temperature. Relative humidity, As an environmental degradation factor, This is the dynamic safety factor.

4. The high-voltage proximity safety early warning method for truck cranes according to claim 3, characterized in that, The voltage level identification uses a convolutional neural network, with the input being the electric field strength Mel spectrum and the output being the voltage level classification result. The convolutional neural network contains 5 convolutional layers and 2 fully connected layers, and is trained based on measured spectrum samples in the voltage range of 10kV–1000kV.

5. The high-voltage proximity safety early warning method for truck cranes according to claim 1, characterized in that, The tiered early warning system includes: Level 1 warning: When the distance is 1.5 times the dynamic safety threshold, an audio-visual alert is triggered; Level 2 warning: When the distance is between the dynamic safety threshold and 1.5 times the dynamic safety threshold, the boom movement speed is limited; Level 3 warning: When the distance is less than the dynamic safety threshold, emergency braking is executed.

6. A high-voltage proximity safety early warning system for truck cranes, characterized in that, The method for high-voltage proximity safety warning of a truck crane as described in any one of claims 1-5 includes: Distributed electromagnetic sensing array: Consists of flexible triaxial electromagnetic sensors arranged in each segment of the boom, used to collect electric field signals radiated by high-voltage lines; Multi-beam lidar: Installed on the rotating gimbal outside the main boom, it is used to collect point cloud data of high-voltage lines. It includes a dual-axis scanning mechanism and a dynamic focusing module. The dynamic focusing module adjusts the point cloud density according to the azimuth angle of the charged body. Environmental sensing module: Includes temperature, humidity, air pressure and rain / fog sensors to monitor environmental parameters in real time; Central Processing Unit: Includes a built-in charged body positioning engine, spatial mapping module, dynamic threshold calculator, and voltage level identification module. It achieves charged body positioning, coordinate mapping, and safety threshold calculation through multi-physics data fusion. The charged body positioning engine inversely calculates the distance to the charged body based on an electric field attenuation model and calculates the azimuth angle of the charged body using the MUSIC algorithm. The spatial mapping module is based on the azimuth angle of the charged body... Controlling multi-beam lidar in Enhanced scanning of the region; alignment of timestamps between electromagnetic sensor array data and multi-beam lidar data using linear interpolation; unification of electromagnetic positioning points and laser point clouds to a coordinate system with the boom root as the origin using the ICP algorithm; elimination of point cloud offset caused by boom rotation according to the correction formula; the dynamic threshold calculator calculates the safety threshold by constructing a three-dimensional safety distance model. The voltage level identification module identifies voltage levels based on a convolutional neural network.

7. A high-voltage proximity safety early warning device for a truck crane, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to execute the high-voltage proximity safety warning method for truck cranes as described in any one of claims 1-5 when running the program instructions.

Images