Anti-collision and anti-electricity device and system for truck cranes in power grid construction scenarios

Abstract

The present invention relates to the technical field of power grid construction, specifically to an anti-collision and anti-approaching device and system for a truck crane in a power grid construction scenario, the system comprising: an environmental data acquisition module, an electric field mapping and analysis module, a motion trajectory comparison module, a control parameter matching module, an abnormal behavior recording module, and a risk response control module. In the present invention, by collecting environmental data in real time and drawing an electric field distribution model, an early warning of the approaching risk of high-voltage wires is provided, so that crane operators can quickly respond to environmental changes and reduce misoperation, adjust the operation path by comparing ideal and actual motion trajectories, reduce collisions and improve the accuracy and response speed of operations in construction scenarios, and use monitoring data for dynamic analysis to timely identify and prevent abnormal behaviors, reduce the risk of accidents caused by operational errors and environmental factors, enhance the safety of crane use in a complex power grid construction environment, and ensure the safety of workers and equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid construction, and in particular to an anti-collision and anti-electricity device and system for a truck crane in a power grid construction scenario. Background Art

[0002] The field of power grid construction technology focuses on the design, construction, maintenance and optimization of power grids, and the layout and implementation of transmission lines, substations and distribution networks. Through the physical construction of power grids, combined with modern information technology and automation technology, it aims to improve the reliability, efficiency and ability of power grids to cope with complex working environments, taking into account geographical, environmental, economic and social factors, ensuring the stability and sustainability of the power system, and using intelligent sensors and automatic control to improve the safety performance of construction and maintenance operations, cope with complex and changing construction environments, and ensure the accuracy and efficiency of operations.

[0003] Among them, the anti-collision and anti-close-to-electricity system for truck cranes in the power grid construction scenario focuses on enhancing the operational safety of truck cranes used in the power grid construction process. Through IoT sensors, it monitors the operating status and surrounding environment of the crane in real time, identifies and prevents the risks of collision and close contact with high-voltage wires, provides immediate warning signals to operators and automatically adjusts the operation of the crane when necessary, reduces the possibility of construction accidents, and ensures the safety of personnel and equipment. It aims to improve the efficiency and safety of power grid construction in complex construction environments with narrow spaces and full of high-voltage wires.

[0004] Traditional truck crane anti-collision and anti-electrical proximity systems rely on basic sensors and simple logical controls, and lack the ability to analyze environmental data and respond in real time. In complex construction environments, it is difficult to provide real-time early warning information and accurate risk assessments, increasing the risk of misoperation and accidents, causing the crane to approach or touch high-voltage power lines without knowing it, and being unable to dynamically adjust the crane's operating parameters to match instantly changing environmental conditions, affecting the application effect in risky environments. Deficiencies in data integration and processing lead to alarm delays and false alarms, affecting the progress and quality of power grid construction projects and reducing construction safety and efficiency. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose an anti-collision and anti-electricity device and system for a truck crane in a power grid construction scenario.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme. The anti-collision and anti-electricity system of the truck crane in the power grid construction scenario includes:

[0007] The environmental data acquisition module is based on IoT environmental sensors. Temperature and humidity sensors and electric field sensors are deployed at multiple structural nodes of the crane to monitor and record data in real time and generate real-time environmental monitoring data.

[0008] The electric field mapping analysis module analyzes the electric field strength data based on the real-time environmental monitoring data, draws an electric field strength distribution map around the crane, and generates an electric field distribution model;

[0009] The motion trajectory comparison module analyzes the input command of the crane based on the electric field distribution model, combines the IoT position sensor, simulates the ideal motion trajectory and compares it with the actual motion trajectory, and generates a trajectory deviation monitoring record;

[0010] The control parameter matching module analyzes the motion deviation according to the trajectory deviation monitoring record, adjusts the crane drive parameters to match the ideal motion trajectory, and generates parameter optimization results;

[0011] The abnormal behavior recording module monitors the speed, acceleration, and direction change data of the crane based on the parameter optimization results, identifies abnormal behavior changes, and generates abnormal operation monitoring results;

[0012] The risk response control module analyzes and identifies collision and near-electric risks based on the abnormal operation monitoring results and the real-time electric field distribution, sends warning information and adjusts the crane start and stop parameters to generate anti-collision and anti-near-electric control parameters.

[0013] As a further solution of the present invention, the real-time environmental monitoring data includes temperature data, humidity information, and electric field strength values ​​around the crane; the electric field distribution model includes electric field strength levels, distribution area boundaries, and risk area identification results; the trajectory deviation monitoring records include deviation values, deviation directions, and deviation durations between actual motion trajectories and ideal motion trajectories; the parameter optimization results include speed vector adjustment results, steering angle adjustment records, and drive power output setting parameters; the abnormal operation monitoring results include abnormal acceleration event information, abnormal direction change events, and operation risk levels; the anti-collision and anti-near-electric control parameters include emergency stop thresholds, warning start time, and safety distance settings.

[0014] As a further solution of the present invention, the environmental data acquisition module includes:

[0015] The environmental sensor installation submodule is based on IoT environmental sensors. Considering coverage and monitoring efficiency, it installs temperature, humidity and electric field sensors at multiple key structural nodes of the crane and generates sensor deployment records.

[0016] The environmental data monitoring submodule monitors the environmental data around the crane in real time based on the sensor deployment record, records the changes in temperature, humidity and electric field strength, and generates an operating environment information record;

[0017] The environmental information analysis submodule analyzes the collected environmental data and electric field data based on the operating environment information records, performs data cleaning and optimizes the consistency of the data format, and generates real-time environmental monitoring data.

[0018] As a further solution of the present invention, the electric field mapping analysis module includes:

[0019] The electric field information analysis submodule receives the real-time environmental monitoring data, analyzes the electric field data, identifies data missing and processes the electric field data using data interpolation, optimizes data continuity and smoothness, and generates electric field data processing results;

[0020] The electric field distribution evaluation submodule analyzes the electric field distribution around the crane based on the electric field data processing result, draws a spatial distribution map of the electric field strength around the crane, and generates an electric field strength distribution map;

[0021] The spatial model building submodule constructs a distribution model of the electric field around the crane based on the electric field intensity distribution map, analyzes the spatial variation and intensity level of the electric field, identifies the location and range of the risk area in combination with the operating area of ​​the crane, and generates an electric field distribution model.

[0022] As a further solution of the present invention, the motion trajectory comparison module includes:

[0023] The input instruction analysis submodule analyzes the operation instruction input by the crane based on the electric field distribution model, calculates the ideal motion trajectory of the crane, and generates the ideal motion trajectory prediction data;

[0024] The motion trajectory recording submodule monitors and records the actual motion trajectory of the crane in real time through a position sensor based on the ideal motion trajectory prediction data, and generates an actual trajectory monitoring record;

[0025] The deviation comparison and analysis submodule compares and analyzes the ideal and actual motion trajectories of the crane based on the actual trajectory monitoring record and adopts the least squares algorithm to evaluate the deviation of the actual motion trajectory, identify the deviation type and degree, and generate a trajectory deviation monitoring record.

[0026] As a further solution of the present invention, the least squares algorithm is according to the formula:

[0027]

[0028] Calculate the trajectory deviation, where is the total sum of squared deviations, is the total number of data points, is the index variable, For the The weight of the data point, For the The actual trajectory position of the data points, For the The ideal motion trajectory position of the data points is is the ideal motion trajectory function, is the parameter set of the ideal motion trajectory function, For the The time labels of the data points, For the The standard deviation of the measurement error for each data point.

[0029] As a further solution of the present invention, the control parameter matching module includes:

[0030] The motion deviation assessment submodule analyzes and identifies key factors affecting the crane trajectory deviation based on the trajectory deviation monitoring record, records the control parameters that need to be adjusted, and generates adjustment demand analysis results;

[0031] The input parameter adjustment submodule adjusts the control parameters of the speed vector and the steering angle of the crane based on the deviation evaluation result, matches the ideal motion trajectory and optimizes the deviation, and generates a control parameter adjustment result;

[0032] The parameter matching verification submodule monitors and calibrates the motion trajectory of the truck crane in real time according to the control parameter adjustment result, matches the ideal motion trajectory of the input parameters, and generates parameter optimization results.

[0033] As a further solution of the present invention, the abnormal behavior recording module includes:

[0034] The abnormal data collection submodule collects and records the speed, acceleration, and direction change data of the crane movement in real time based on the parameter optimization results, and generates movement data records;

[0035] The abnormal motion analysis submodule analyzes abnormal motion of the truck crane in real time based on the motion data record, including sudden acceleration and direction change, identifies operating errors and crane failures, and generates abnormal behavior identification records;

[0036] The monitoring result evaluation submodule calculates the risk scores of various abnormal behaviors based on the abnormal behavior identification records, evaluates the severity and frequency of abnormal behaviors, and classifies abnormal operation behaviors to generate abnormal operation monitoring results.

[0037] As a further solution of the present invention, the risk response control module includes:

[0038] The operation risk analysis submodule identifies the key risk points that lead to safety accidents based on the abnormal operation monitoring results and combines the real-time electric field distribution data, analyzes the collision and near-electric risks, and generates near-electric risk assessment results;

[0039] The risk notification sending submodule sends warning information to management personnel and operators according to the near-electric risk assessment result and the identified near-electric and collision risks, including the warning level and risk location, and generates a warning information sending record;

[0040] The operating parameter control submodule adjusts the operating parameters of the truck crane, including speed, height, and start / stop control, based on the early warning information transmission record, to match the safety operation requirements and generate anti-collision and anti-close-electricity control parameters.

[0041] The anti-collision and anti-electrical proximity device for a truck crane in a power grid construction scenario includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the module of the anti-collision and anti-electrical proximity system for the truck crane in the power grid construction scenario is implemented.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are:

[0043] In the present invention, by collecting environmental data in real time and drawing an electric field distribution model, the risk of approaching high-voltage wires is warned, so that crane operators can respond quickly to environmental changes and reduce misoperation, adjust the operation path by comparing ideal and actual motion trajectories, reduce collisions and improve the accuracy and response speed of operations in construction scenarios. By using monitoring data for dynamic analysis, abnormal behaviors can be identified and prevented in a timely manner, the risk of accidents caused by operational errors and environmental factors can be reduced, the safety of crane use in complex power grid construction environments can be enhanced, and the safety of workers and equipment can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a system flow chart of the present invention;

[0045] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0046] Figure 3 This is a flow chart of the environmental data acquisition module of the present invention;

[0047] Figure 4 This is a flow chart of the electric field mapping analysis module of the present invention;

[0048] Figure 5 It is a flow chart of the motion trajectory comparison module of the present invention;

[0049] Figure 6 It is a flow chart of the control parameter matching module of the present invention;

[0050] Figure 7 This is a flow chart of the abnormal behavior recording module of the present invention;

[0051] Figure 8 This is a flow chart of the risk response control module of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0053] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0054] See also Figure 1 to Figure 2 The anti-collision and anti-electricity system for truck cranes in power grid construction scenarios includes:

[0055] The environmental data acquisition module is based on IoT environmental sensors. Temperature and humidity sensors and electric field sensors are deployed at multiple structural nodes of the crane to monitor and record data in real time and generate real-time environmental monitoring data.

[0056] The electric field mapping analysis module analyzes the electric field strength data based on real-time environmental monitoring data, draws the electric field strength distribution map around the crane, and generates an electric field distribution model;

[0057] The motion trajectory comparison module analyzes the input instructions of the crane based on the electric field distribution model, combines the IoT position sensor, simulates the ideal motion trajectory and compares it with the actual motion trajectory to generate trajectory deviation monitoring records;

[0058] The control parameter matching module monitors and records trajectory deviations, analyzes motion deviations, adjusts the crane drive parameters to match the ideal motion trajectory, and generates parameter optimization results;

[0059] The abnormal behavior recording module monitors the speed, acceleration, and direction change data of the crane based on the parameter optimization results, identifies abnormal behavior changes, and generates abnormal operation monitoring results;

[0060] The risk response control module analyzes and identifies collision and near-electric risks based on the abnormal operation monitoring results and the real-time electric field distribution, sends warning information and adjusts the crane start and stop parameters to generate anti-collision and anti-near-electric control parameters.

[0061] Real-time environmental monitoring data include temperature data, humidity information, and electric field strength values ​​around the crane. The electric field distribution model includes the electric field strength level, distribution area boundaries, and risk area identification results. Trajectory deviation monitoring records include the deviation value, deviation direction, and deviation duration between the actual motion trajectory and the ideal motion trajectory. Parameter optimization results include speed vector adjustment results, steering angle adjustment records, and drive power output setting parameters. Abnormal operation monitoring results include abnormal acceleration event information, abnormal direction change events, and operation risk levels. Anti-collision and anti-proximity control parameters include emergency stop threshold, warning start time, and safety distance setting.

[0062] See also Figure 2 and Figure 3 , the environmental data acquisition module includes:

[0063] The environmental sensor installation submodule is based on the IoT environmental sensor. Considering the coverage and monitoring efficiency, the temperature, humidity and electric field sensors are installed at multiple key structural nodes of the crane. The specific process of generating sensor deployment records is as follows;

[0064] In the environmental sensor installation submodule, based on the IoT environmental sensors, the installation points of temperature, humidity and electric field sensors are determined for coverage and monitoring efficiency, and the optimal coverage is achieved. By analyzing the structural characteristics of the crane, structural nodes with uniform load distribution and frequent contact with the external environment are selected as installation points. The sensor configuration of each node is adjusted according to the environmental sensitivity to ensure the accuracy and timeliness of the monitoring data. The sensor installation procedure includes cleaning the surface of the node to ensure the cleanliness of the sensor contact surface to improve the sensitivity of the sensor. Non-destructive fixing technology is used to ensure the stability of the sensor. After the sensor is arranged, a functional test is performed to ensure that the data is transmitted and received correctly. The sensor deployment efficiency model is used, and the formula is: ,in, Sensor deployment efficiency is used to measure the spatial efficiency and signal coverage of sensor deployment. For the The signal strength of a sensor is used to indicate the signal sending ability of the sensor at its installation location. is the target total coverage volume, which is used to represent the overall spatial area that needs to be covered by the sensor. is the number of installed sensors, which is used to indicate the total number of sensors involved in monitoring. The index of the sensor, which is used to traverse multiple sensors during the calculation process and generate sensor deployment records.

[0065] The environmental data monitoring submodule monitors the environmental data around the crane in real time based on the sensor deployment record, records the changes in temperature, humidity and electric field strength, and generates the specific process of the operating environment information record as follows;

[0066] In the environmental data monitoring submodule, based on the sensor deployment record, the environmental data around the crane is monitored in real time, and the temperature, humidity and electric field strength data are collected. By establishing a data collection time series and synchronizing multiple sensor data to reduce information delays, the monitoring data is preliminarily screened through preset thresholds to exclude obviously abnormal data points to ensure data quality, including real-time data transmission protocols to ensure instant data upload and processing. The data quality assessment model is adopted, and the formula is: ,in, It is a data quality indicator used to measure the accuracy and reliability of monitoring data. For the The data of a measurement is used to indicate the environmental parameter value of a specific measurement. is the average value of the data, used to represent the average level of all measured data, as a comparison benchmark. is the total number of data points, which is used to indicate the total amount of data collected during the monitoring period. It is the index of the data point, which is used to traverse each data point during the calculation process and generate the operation environment information record.

[0067] The environmental information analysis submodule analyzes the collected environmental data and electric field data based on the operating environment information records, performs data cleaning and optimizes the consistency of the data format, and generates the specific process of real-time environmental monitoring data as follows;

[0068] In the environmental information analysis submodule, based on the operating environment information records, the collected environmental data and electric field data are analyzed, including cleaning the original data, eliminating abnormal data caused by equipment failure or external interference, standardizing the data format, ensuring the consistency of the format of multiple data sources, facilitating data fusion and analysis, and statistically analyzing the cleaned data to evaluate environmental change trends and predict potential risks. The data consistency optimization model is adopted, and the formula is: ,in, Optimizes the value for data consistency, which is used to measure the uniformity and format consistency between data. For the A data value representing a single data point after cleaning and formatting. It is the average value of all data, used to represent the center point of all data values, and is a commonly used parameter in statistical analysis. is the total number of data points after processing, used to indicate the number of valid data before analysis. It is the index of the data point, which is used to traverse multiple data points during the calculation process to generate real-time environmental monitoring data.

[0069] See also Figure 2 and Figure 4 , the electric field mapping analysis module includes:

[0070] The electric field information analysis submodule receives real-time environmental monitoring data, analyzes the electric field data, identifies missing data, and uses data interpolation to process the electric field data, optimizes data continuity and smoothness, and generates the specific process of electric field data processing results as follows;

[0071] In the electric field information analysis submodule, real-time environmental monitoring data is received, the electric field data is analyzed, missing points in the data set are identified, missing values ​​are filled by linear interpolation, the integrity and continuity of the data are maintained, and the moving average method is applied to the processed electric field data to reduce the impact of random fluctuations on the analysis results, including adjusting the interval between data points to ensure continuous recording of the electric field strength. The electric field data continuity optimization model is adopted, and the formula is: ,in, It is a continuity measure of electric field data, used to measure the smoothness and continuity of electric field intensity records. and are the electric field strength values ​​of two consecutive data points, which are used to represent the electric field strength at the target time point. and are the timestamps of these two data points, used to mark the specific time of the electric field strength measurement. is the total number of data points, used to indicate the number of electric field strength data points involved in the calculation. It is the index of the data point, which is used to traverse multiple data points in the formula calculation and generate the electric field data processing results.

[0072] The electric field distribution assessment submodule draws a spatial distribution map of the electric field strength around the crane by analyzing the electric field distribution around the crane based on the electric field data processing results. The specific process of generating the electric field strength distribution map is as follows:

[0073] In the electric field distribution assessment submodule, based on the electric field data processing results, the Kriging interpolation method is used to analyze the changes in the electric field strength around the crane, and the spatial distribution of the electric field strength is plotted. Through the generated data, a three-dimensional distribution map of the electric field strength around the crane is constructed to visualize the electric field changes and facilitate monitoring and analysis. The spatial distribution model assessment method is used, and the formula is: ,in, is the spatial distribution uniformity of the electric field strength, which is used to evaluate the uniformity of the electric field strength distribution in space. For the The electric field strength at each position is used to represent the electric field strength at the target spatial position. is the average value of the electric field strength at all measurement points, used to indicate the general level of electric field strength and as a comparison benchmark. is the total number of measurement points, which is used to indicate the number of data points included in the entire spatial model. It is the index of the location point, which is used to traverse the electric field strength data of multiple spatial locations in the formula calculation to generate the electric field strength distribution diagram.

[0074] The spatial model building submodule builds a distribution model of the electric field around the crane based on the electric field intensity distribution map, analyzes the spatial variation and intensity level of the electric field, identifies the location and range of the risk area in combination with the crane's operating area, and generates the electric field distribution model in the following specific steps:

[0075] In the spatial model construction submodule, an electric field distribution model is constructed based on the electric field intensity distribution map. The model considers the geometric and functional characteristics of the crane operation area, quantitatively analyzes the change trend of the electric field in space and its intensity level, identifies the risk area that poses a threat to operational safety, and calibrates the location and range of the target area. The electric field spatial risk assessment model is used, and the formula is: ,in, is the risk factor, which is used to quantify the extent to which the electric field strength exceeds the safety threshold and measure the potential risk level. For the The electric field strength of each monitoring point is used to indicate the electric field strength level of the target monitoring point. is the safety electric field strength threshold, which is used to calibrate the safety upper limit of the electric field strength. is the total number of monitoring points, used to indicate the number of electric field strength monitoring points involved in risk assessment. It is the index of the monitoring point, which is used to traverse multiple electric field strength data points in the formula calculation to generate the electric field distribution model.

[0076] See also Figure 2 and Figure 5 , the motion trajectory comparison module includes:

[0077] The input command analysis submodule analyzes the operation command input by the crane based on the electric field distribution model, calculates the ideal motion trajectory of the crane, and generates the specific process of the ideal motion trajectory prediction data as follows;

[0078] In the input command analysis submodule, based on the electric field distribution model, the operation instructions received by the crane are analyzed, the influence of the surrounding electric field on the operation accuracy is considered, and the ideal motion trajectory of the crane is calculated to ensure that the trajectory planning considers the influence of the electric field strength on the mechanical operation. The calculation of the ideal motion trajectory uses a composite influence model, and the formula is: ,in, is the ideal motion trajectory calculated, The basic trajectory defined for the original operation instruction, For the The intensity of the electric field at each monitoring point, is the influence coefficient corresponding to the electric field strength, is the total number of electric field monitoring points. Considering the impact of the dynamic change of the electric field on the motion accuracy of the crane, the ideal motion trajectory prediction data is generated.

[0079] The motion trajectory recording submodule monitors and records the actual motion trajectory of the crane in real time through the position sensor based on the ideal motion trajectory prediction data. The specific process of generating the actual trajectory monitoring record is as follows;

[0080] In the motion trajectory recording submodule, based on the ideal motion trajectory prediction data, the actual motion trajectory of the crane is monitored in real time through the position sensor. The real-time data is processed by the position synchronization algorithm to ensure the timeliness and accuracy of the monitoring data. The actual motion trajectory is recorded using the trajectory monitoring model, and the formula is: ,in, is the actual motion trajectory recorded, To predict the ideal motion trajectory, The deviation value is measured in real time, the offset in motion is identified and adjusted in real time, and the actual trajectory monitoring record is generated.

[0081] The deviation comparison and analysis submodule uses the least squares algorithm based on the actual trajectory monitoring record to compare and analyze the ideal and actual motion trajectories of the crane, evaluate the deviation of the actual motion trajectory, identify the deviation type and degree, and generate the specific process of trajectory deviation monitoring record as follows;

[0082] In the deviation comparison and analysis submodule, based on the actual trajectory monitoring records, the least squares algorithm is used to compare the ideal and actual motion trajectories, evaluate the trajectory accuracy and operation safety during crane operation, analyze the deviation of the motion trajectory and identify the type and degree of deviation, optimize the operating accuracy of the crane, quantify and evaluate the deviation, and generate trajectory deviation monitoring records.

[0083] The least squares algorithm, according to the formula:

[0084]

[0085] Calculate the trajectory deviation, where is the total squared deviation, which is used to measure the overall deviation between the actual trajectory and the ideal motion trajectory. is the total number of data points, which is used to represent the number of measurement points considered during the entire observation period, is the index variable used to iterate each data point during the summation process. For the The weights of the data points are used to adjust the influence of each point to take into account various environmental and operational factors. For the The actual trajectory position of the data points is used to represent the actual position of the crane at a specific time. For the The ideal motion trajectory position of the data point is calculated by the ideal motion trajectory function and parameters Determine, used to indicate a position that should be reached at a specific time, is the ideal motion trajectory function, is the parameter set of the ideal motion trajectory function, which is used to adjust the ideal motion trajectory model to best fit the desired path. For the The time label of each data point is used to identify the observation time point. For the The standard deviation of the measurement error for each data point is used to quantify the observation uncertainty at each point.

[0086] The specific execution process of the formula is as follows:

[0087] Obtain the actual trajectory position at each time point through real-time monitoring data and ideal motion trajectory position , combining the environmental complexity, speed change, and directional stability parameters of each point to calculate the weight of each data point , Substitute the parameters into the formula to calculate the total sum of squared deviations , used to evaluate the trajectory accuracy and safety of crane operations.

[0088] See also Figure 2 and Figure 6 , the control parameter matching module includes:

[0089] The motion deviation assessment submodule analyzes and identifies the key factors affecting the crane trajectory deviation based on the trajectory deviation monitoring records, records the control parameters that need to be adjusted, and generates the specific process of the adjustment demand analysis results as follows;

[0090] In the motion deviation assessment submodule, based on the trajectory deviation monitoring record, the deviation of the crane motion trajectory is analyzed, and the factor analysis method is used to identify and quantify the various factors that cause the deviation, including environmental impact, mechanical wear, and operating errors. The key influencing factors are determined, and the control parameters that need to be adjusted are listed according to the analysis results, including the calibration of the power output and the guide system. The key factor identification model is used, and the formula is: ,in, : The key influencing factor score of the deviation is used to measure the contribution of the target factor to the deviation of the crane motion trajectory. is the baseline influence coefficient, which indicates the initial deviation value when there is no external factor. For the A quantitative indicator of a factor, used to describe the intensity or existence of the target factor. is the influence coefficient of the corresponding factor, which is used to quantify the specific impact of each factor on the deviation. It is the total number of factors, which is used to indicate the number of different influencing factors considered in the analysis, determine the priority and scope of adjustments, and generate adjustment demand analysis results.

[0091] The input parameter adjustment submodule adjusts the control parameters of the crane's speed vector and steering angle based on the deviation evaluation results, matches the ideal motion trajectory and optimizes the deviation. The specific process of generating the control parameter adjustment results is as follows;

[0092] In the input parameter adjustment submodule, based on the deviation evaluation results, the speed vector and steering angle of the crane are adjusted. By simulating the impact of various adjustment configurations, the optimal solution is selected to match the ideal motion trajectory and reduce the deviation. The parameter optimization adjustment model is adopted, and the formula is: ,in, is the optimized control parameter, is the current parameter value, To evaluate the proposed adjustment value based on the deviation, In order to adjust the strength coefficient and optimize the operating performance of the crane, the control parameter adjustment results are generated.

[0093] The parameter matching verification submodule monitors and calibrates the motion trajectory of the truck crane in real time according to the control parameter adjustment results, matches the ideal motion trajectory of the input parameters, and generates the parameter optimization results in the following specific process:

[0094] In the parameter matching verification submodule, the motion trajectory of the crane is monitored and calibrated in real time according to the control parameter adjustment results to ensure that it matches the ideal motion trajectory of the input parameters. Through continuous monitoring and real-time feedback adjustment, the motion accuracy of the crane is optimized. The real-time calibration model is used, and the formula is: ,in, is the adjusted trajectory, is the real-time measurement trajectory, is the ideal motion trajectory, It is a calibration factor used to adjust the proximity between the actual trajectory and the ideal motion trajectory, ensure the continuous optimization of the trajectory, and generate parameter optimization results.

[0095] See also Figure 2 and Figure 7 ,The abnormal behavior recording module includes:

[0096] The abnormal data collection submodule collects and records the speed, acceleration, and direction change data of the crane movement in real time based on the parameter optimization results. The specific process of generating the motion data record is as follows:

[0097] In the abnormal data collection submodule, based on the parameter optimization results, the speed, acceleration and direction change data of the crane during operation are collected and recorded in real time. The collected data is filtered by the data filtering algorithm to eliminate noise interference to ensure the accuracy of the data. The data collection process adopts a real-time monitoring model, and the formula is: ,in, A comprehensive indicator of motion data. , and Represents time The measured values ​​of velocity, acceleration and direction angle, In order to measure the total duration and reflect the motion status of the crane, a motion data record is generated.

[0098] The abnormal motion analysis submodule analyzes the abnormal motion of the truck crane in real time based on the motion data records, including sudden acceleration and direction changes, identifies operating errors and crane failures, and generates abnormal behavior identification records. The specific process is as follows;

[0099] In the abnormal motion analysis submodule, based on the motion data record, the abnormal motion behavior of the crane is analyzed in real time, including sudden acceleration and sharp direction change. The abnormal behavior is determined by setting the threshold, and the abnormalities caused by operating errors and mechanical failures are identified. The abnormal identification model is used, and the formula is: ,in, is an indicator of abnormal behavior. and Continuous time points speed and direction, is the time interval, Indicates at a point in time The speed measurement value is used to calculate the change in speed and identify sudden acceleration or deceleration behavior in crane movement. Indicates at a point in time The direction angle measurement value, that is, in the current measurement The direction data at a previous point in time is used to calculate the change in direction, identify sudden direction changes in crane movement, identify and respond to abnormal changes, and generate abnormal behavior identification records.

[0100] The monitoring result evaluation submodule calculates the risk scores of various abnormal behaviors based on the abnormal behavior identification records, evaluates the severity and frequency of abnormal behaviors, and classifies abnormal operation behaviors. The specific process of generating abnormal operation monitoring results is as follows;

[0101] In the monitoring result evaluation submodule, based on the abnormal behavior identification records, the risk scores of differentiated abnormal behaviors are calculated by analyzing the severity and frequency of abnormal behaviors. The risk assessment model is adopted, and the formula is: ,in, is the total risk score, For the The frequency of abnormal behavior, is the corresponding severity rating, The total number of abnormal types is used to classify abnormal behaviors and evaluate their impact on operational safety, generating abnormal operation monitoring results.

[0102] See also Figure 2 and Figure 8 , the risk response control module includes:

[0103] The operation risk analysis submodule identifies the key risk points that lead to safety accidents based on abnormal operation monitoring results and combines real-time electric field distribution data, analyzes collision and near-electric risks, and generates the specific process of near-electric risk assessment results as follows;

[0104] In the operation risk analysis submodule, based on the abnormal operation monitoring results and combined with the real-time electric field distribution data, the operating environment and behavior of the crane are analyzed to identify the key risk points that lead to safety accidents, including the assessment of the risk of proximity to high-voltage lines when the crane is close to high-voltage lines and the risk of collision when operating in narrow spaces. The risk point analysis adopts an integrated risk assessment model, and the formula is: ,in, represents the total risk score, Indicates The severity of the risk, Indicates the frequency of occurrence of the target risk, represents the electric field strength, Indicates the distance to the power source, quantifies the risk of near electric shock and collision, and generates near electric shock risk assessment results.

[0105] The risk notification sending submodule sends warning information to managers and operators based on the near-electric risk assessment results and the identified near-electric and collision risks, including the warning level and risk location. The specific process of generating the warning information sending record is as follows;

[0106] In the risk notification sending submodule, timely warning information is sent to management personnel and operators based on the near-electricity risk assessment results, including the warning level and risk location, so that related personnel can take safety measures in time. The formatting and sending process of the warning information uses the notification system optimization algorithm, and the formula is: ,in, Indicates the priority of the warning information. Indicates the warning level. Indicates the accuracy of the risk location, To determine the warning level Prioritize warning information The influence weight of The accuracy used to determine the location of the risk Prioritize warning information The impact weight is used to ensure the effectiveness and timeliness of the early warning information and generate a record of the early warning information being sent.

[0107] The operation parameter control submodule adjusts the operation parameters of the truck crane, including speed, height, and start-stop control, based on the early warning information sending records, to match the safety operation needs, and generates the specific process of anti-collision and anti-close-electric control parameters as follows;

[0108] In the operating parameter control submodule, based on the early warning information sending record, the operating parameters of the truck crane are adjusted to match the needs of safe operation, including adjusting the speed, height and start-stop control of the machine to avoid collision and near-electric risks. The parameter adjustment adopts a dynamic safety control model, and the formula is: ,in, Represents the safety control parameters, , and Respectively represent the adjustment values ​​of speed, altitude and start-stop control, Used to measure speed adjustment value In the overall safety control parameters The influence of Used to measure height adjustment value In the overall safety control parameters The influence of Used to measure start-stop control In the overall safety control parameters The influence of the environment is used to ensure that the operation of the crane remains safe in risky environments and to generate anti-collision and anti-close-circuit control parameters.

[0109] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. The anti-collision and anti-electricity system for truck cranes in power grid construction scenarios is characterized by: The system comprises: The environmental data acquisition module is based on IoT environmental sensors. Temperature and humidity sensors and electric field sensors are deployed at multiple structural nodes of the crane to monitor and record data in real time and generate real-time environmental monitoring data. The electric field mapping analysis module analyzes the electric field strength data based on the real-time environmental monitoring data, draws an electric field strength distribution map around the crane, and generates an electric field distribution model; The motion trajectory comparison module analyzes the operation instructions input by the crane based on the electric field distribution model, calculates the ideal motion trajectory of the crane, monitors and records the actual motion trajectory of the crane in real time through the position sensor, compares the ideal motion trajectory with the actual motion trajectory, and generates a trajectory deviation monitoring record; The control parameter matching module analyzes the motion deviation according to the trajectory deviation monitoring record, adjusts the crane drive parameters to match the ideal motion trajectory, and generates parameter optimization results; The abnormal behavior recording module monitors the speed, acceleration, and direction change data of the crane based on the parameter optimization results, identifies abnormal behavior changes, and generates abnormal operation monitoring results; The risk response control module analyzes and identifies collision and near-electric risks based on the abnormal operation monitoring results and the real-time electric field distribution, sends warning information and adjusts the crane start and stop parameters to generate anti-collision and anti-near-electric control parameters.

2. The anti-collision and anti-electricity system for truck cranes in power grid construction scenarios according to claim 1 is characterized in that: The real-time environmental monitoring data includes temperature data, humidity information, and electric field strength values ​​around the crane; the electric field distribution model includes electric field strength levels, distribution area boundaries, and risk area identification results; the trajectory deviation monitoring records include deviation values, deviation directions, and deviation durations between actual motion trajectories and ideal motion trajectories; the parameter optimization results include speed vector adjustment results, steering angle adjustment records, and drive power output setting parameters; the abnormal operation monitoring results include abnormal acceleration event information, abnormal direction change events, and operation risk levels; the anti-collision and anti-near-electric control parameters include emergency stop thresholds, warning start time, and safety distance settings.

3. The anti-collision and anti-electricity system for truck cranes in power grid construction scenarios according to claim 1 is characterized in that: The environmental data acquisition module includes: The environmental sensor installation submodule is based on IoT environmental sensors. Considering coverage and monitoring efficiency, it installs temperature, humidity and electric field sensors at multiple key structural nodes of the crane and generates sensor deployment records. The environmental data monitoring submodule monitors the environmental data around the crane in real time based on the sensor deployment record, records the changes in temperature, humidity and electric field strength, and generates an operating environment information record; The environmental information analysis submodule analyzes the collected environmental data and electric field data based on the operating environment information records, performs data cleaning and optimizes the consistency of the data format, and generates real-time environmental monitoring data.

4. The anti-collision and anti-electricity system for truck cranes in power grid construction scenarios according to claim 1 is characterized in that: The electric field mapping analysis module comprises: The electric field information analysis submodule receives the real-time environmental monitoring data, analyzes the electric field data, identifies data missing and processes the electric field data using data interpolation, optimizes data continuity and smoothness, and generates electric field data processing results; The electric field distribution evaluation submodule analyzes the electric field distribution around the crane based on the electric field data processing result, draws a spatial distribution map of the electric field strength around the crane, and generates an electric field strength distribution map; The spatial model building submodule constructs a distribution model of the electric field around the crane based on the electric field intensity distribution map, analyzes the spatial variation and intensity level of the electric field, identifies the location and range of the risk area in combination with the operating area of ​​the crane, and generates an electric field distribution model.

5. The anti-collision and anti-electricity system for truck cranes in power grid construction scenarios according to claim 1 is characterized in that: The motion trajectory comparison module includes: The input instruction analysis submodule analyzes the operation instruction input by the crane based on the electric field distribution model, calculates the ideal motion trajectory of the crane, and generates the ideal motion trajectory prediction data; The motion trajectory recording submodule monitors and records the actual motion trajectory of the crane in real time through a position sensor based on the ideal motion trajectory prediction data, and generates an actual trajectory monitoring record; The deviation comparison and analysis submodule compares and analyzes the ideal and actual motion trajectories of the crane based on the actual trajectory monitoring record and adopts the least squares algorithm to evaluate the deviation of the actual motion trajectory, identify the deviation type and degree, and generate a trajectory deviation monitoring record.

6. The anti-collision and anti-electric proximity system for truck cranes in power grid construction scenarios according to claim 5 is characterized in that: The least squares algorithm is based on the formula: ； Calculate the trajectory deviation, where is the total sum of squared deviations, is the total number of data points, is the index variable, For the The weight of the data point, For the The actual trajectory position of the data points, For the The ideal motion trajectory position of the data points is is the ideal motion trajectory function, is the parameter set of the ideal motion trajectory function, For the The time labels of the data points, For the The standard deviation of the measurement error for each data point.

7. The anti-collision and anti-electric proximity system for truck cranes in power grid construction scenarios according to claim 1 is characterized in that: The control parameter matching module includes: The motion deviation assessment submodule analyzes and identifies key factors affecting the crane trajectory deviation based on the trajectory deviation monitoring record, records the control parameters that need to be adjusted, and generates adjustment demand analysis results; The input parameter adjustment submodule adjusts the control parameters of the speed vector and the steering angle of the crane based on the adjustment demand analysis result, matches the ideal motion trajectory and optimizes the deviation, and generates a control parameter adjustment result; The parameter matching verification submodule monitors and calibrates the motion trajectory of the truck crane in real time according to the control parameter adjustment result, matches the ideal motion trajectory of the input parameters, and generates parameter optimization results.

8. The anti-collision and anti-electric proximity system for truck cranes in power grid construction scenarios according to claim 1 is characterized in that: The abnormal behavior recording module includes: The abnormal data collection submodule collects and records the speed, acceleration, and direction change data of the crane movement in real time based on the parameter optimization results, and generates movement data records; The abnormal motion analysis submodule analyzes abnormal motion of the truck crane in real time based on the motion data record, including sudden acceleration and direction change, identifies operating errors and crane failures, and generates abnormal behavior identification records; The monitoring result evaluation submodule calculates the risk scores of various abnormal behaviors based on the abnormal behavior identification records, evaluates the severity and frequency of abnormal behaviors, and classifies abnormal operation behaviors to generate abnormal operation monitoring results.

9. The anti-collision and anti-electric proximity system for truck cranes in power grid construction scenarios according to claim 1 is characterized in that: The risk response control module includes: The operation risk analysis submodule identifies the key risk points that lead to safety accidents based on the abnormal operation monitoring results and combines the real-time electric field distribution data, analyzes the collision and near-electric risks, and generates near-electric risk assessment results; The risk notification sending submodule sends warning information to management personnel and operators according to the near-electric risk assessment result and the identified near-electric and collision risks, including the warning level and risk location, and generates a warning information sending record; The operating parameter control submodule adjusts the operating parameters of the truck crane, including speed, height, and start / stop control, based on the early warning information transmission record, to match the safety operation requirements and generate anti-collision and anti-close-electricity control parameters.

10. A vehicle crane anti-collision and anti-electric proximity device in a power grid construction scenario, comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the processor executes the computer program, it implements the module of the anti-collision and anti-approaching system of the truck crane in the power grid construction scenario as described in any one of claims 1 to 9.

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