Anti-collision early warning control method and system for multi-machine lifting power line repair in mountainous area

Abstract

The invention provides an anti-collision early warning control method and system for multi-crane lifting power line repair in a mountainous area, and relates to the technical field of intelligent safety early warning, and the method comprises the following steps: obtaining original real-time data collected by a positioning device, an attitude sensor and a lifting object state sensor carried by each crane; constructing a data association relationship based on the data of the multi-source sensor, and analyzing the intrinsic characteristics of the association relationship to generate a dynamic correction coefficient; performing gridding interpolation completion processing on missing sampling points existing in the original real-time data, and fusing and correcting the original real-time data after interpolation completion through a dynamic correction coefficient to obtain multi-source fusion correction data; and based on the multi-source fusion correction data, calculating the spatial position and posture of the tail end of the suspension arm of each crane, and fusing the state data of the hoisted object to calculate the real-time spatial position of the hoisted object. According to the invention, accurate fusion correction of multi-source sensor data is realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent safety early warning technology, and in particular to a method and system for collision prevention and early warning control of multi-machine hoisting power emergency repair in mountainous areas. Background Technology

[0002] Power emergency repairs in mountainous areas often face complex environments with rugged terrain, narrow working spaces, and severe signal obstruction. Scenarios involving multiple cranes working together to lift large power equipment such as tower components and transformers are becoming increasingly common, but existing anti-collision warning and control technologies are difficult to adapt to the needs of this scenario.

[0003] Extreme weather in a mountainous area caused a high-voltage transmission tower to collapse. During the emergency repair, multiple truck cranes worked together to lift the main pole of the large tower. Due to the mountain's obstruction, some crane positioning sensor data was missing. The existing system did not effectively interpolate and dynamically correct the multi-source sensor data, and it did not establish a coupling relationship between the boom movement and the swing of the load. It only made simple distance judgments based on the real-time position, ignoring the inertial swing trajectory of the load caused by the boom's start and stop. This resulted in a deviation between the predicted trajectory of the load and the actual trajectory. The system only issued a warning when the actual distance between the load and the boom of another crane approached the safety threshold, and it did not provide precise guidance on avoidance direction and speed. Even when the operator braked suddenly, the load still scraped against the boom, causing equipment damage and delaying the repair progress. This highlights the technical defects of the existing system, such as insufficient multi-source data fusion and correction capabilities, lack of coupled motion trajectory prediction, lagging dynamic identification of cross-operation areas, one-sided collision risk assessment, and inaccurate warning and avoidance control. These defects seriously affect the safety and efficiency of power emergency repair and lifting operations in mountainous areas. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method and system for collision prevention and early warning control of multi-machine hoisting power emergency repair in mountainous areas, so as to achieve accurate fusion and correction of multi-source sensor data.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: Firstly, a collision prevention and early warning control method for multi-machine hoisting power emergency repair in mountainous areas, the method comprising: The system acquires raw real-time data collected by positioning devices, attitude sensors, and load status sensors mounted on each crane; it constructs data correlation relationships based on data from multiple sources, analyzes the inherent characteristics of the correlation relationships to generate dynamic correction coefficients; it performs gridded interpolation to complete missing sampling points in the raw real-time data, and then fuses and corrects the interpolated raw real-time data using dynamic correction coefficients to obtain multi-source fused correction data. Based on multi-source fusion correction data, the spatial position and attitude of the end of each crane boom are calculated, and the state data of the suspended object are fused to calculate the real-time spatial position of the suspended object; Based on real-time spatial location, the coupled motion relationship between the boom and the suspended object is established, and the motion trajectory of the boom and the suspended object in a future set time period is predicted based on the coupled motion relationship. Based on real-time spatial location and movement trajectory, the overlapping work space areas within the working range of each crane are dynamically identified and updated; Based on the cross-operation space area, and the real-time spatial position and movement trajectory of each boom and load within the cross-operation space area, calculate the real-time distance and relative movement trend between booms, between booms and loads, and between loads to obtain the collision risk level; Based on the collision risk level, control commands containing the warning level, avoidance direction, and reference speed are generated and sent to the corresponding crane to implement warning and active avoidance control.

[0006] Furthermore, raw real-time data collected by the positioning devices, attitude sensors, and load status sensors mounted on each crane are acquired; data correlation relationships are constructed based on the data from multiple sources, and the inherent characteristics of these relationships are analyzed to generate dynamic correction coefficients; missing sampling points in the raw real-time data are filled by gridded interpolation, and then the interpolated raw real-time data is fused and corrected using the dynamic correction coefficients to obtain multi-source fused correction data, including: Receive raw real-time data packets periodically reported by each crane; perform timestamp alignment processing on the raw real-time data packets to unify the time base of the data and obtain a time-aligned multi-source data sequence; Based on time-aligned multi-source data sequences, the physical and geometric correlations between data from different sensors are analyzed. By analyzing the inherent consistency characteristics of the correlations over a period of time, the deviations and noise patterns among the data from each sensor are identified, and dynamic correction coefficients for compensating for deviations and noise are obtained. To address missing sampling points caused by sampling anomalies in time alignment, data points from the same sensor are constructed into a discrete point set in the spatiotemporal domain. Based on the spatial and temporal neighborhood information of the valid data points in the discrete point set, the estimated values ​​of the missing sampling points are calculated and completed to obtain a complete data sequence. The complete data sequence is combined with dynamic correction coefficients to obtain multi-source fusion correction data.

[0007] Furthermore, based on multi-source fusion correction data, the spatial position and attitude of the end of each crane boom are calculated, and the state data of the suspended object are fused to calculate the real-time spatial position of the suspended object, including: Receive and integrate multi-source fusion correction data from various cranes, establish a global unified coordinate system based on the crane body positioning data, and construct a geometric motion relationship description of the boom based on the boom's structural parameters, joint connection relationships, and motion constraints. Based on the description of geometric motion relationships, and combined with the real-time angle data of each boom joint and the real-time length data of the telescopic boom in the multi-source fusion correction data, the precise spatial position coordinates of the end of each crane boom in the global unified coordinate system are obtained through geometric derivation and calculation. Based on spatial location coordinates, real-time measurement data from the boom attitude sensor in the multi-source fusion correction data are fused, and the three-dimensional attitude angle of the boom end in the global unified coordinate system is calculated through geometric relationship transformation and compensation processing. Based on the spatial position coordinates and three-dimensional attitude angles of the boom end, the real-time length of the hoisting rope, the real-time swing angle and swing angular velocity data of the hoisting object collected by the hoisting object state sensor in the multi-source fusion correction data are integrated. Through spatial geometric relationship calculation, the real-time spatial position of the hoisting object's center of gravity in the global unified coordinate system is obtained.

[0008] Furthermore, based on real-time spatial location, a coupled motion relationship between the boom and the suspended load is established, and the motion trajectory of the boom and the suspended load within a future set time period is predicted based on this coupled motion relationship, including: Receive real-time spatial position data of the boom end, three-dimensional attitude angle, and center of gravity of the suspended object, and calculate the real-time relative position vector and spatial connection relationship between the boom end and the center of gravity of the suspended object. Based on real-time relative position vectors and spatial connectivity, and combined with the boom joint motion velocity and load swing state data in multi-source fusion correction data, the characteristics of the transmission influence of boom motion on load swing are analyzed, and the dynamic coupling relationship between boom motion and load motion is established. Based on the dynamic coupling relationship, and combining the current operation command signals of each crane with historical motion trend data, the time series motion trajectory of the boom end in a future set period of time is predicted. Based on the time-series motion trajectory and dynamic coupling relationship of the boom end, the time-series motion trajectory of the center of gravity of the suspended object within a future set time period is calculated. The time-series motion trajectories of the boom end and the center of gravity of the suspended object are spatiotemporally aligned and smoothed to obtain continuous and stable future motion trajectories of the boom and the suspended object.

[0009] Furthermore, based on real-time spatial location and movement trajectory, the system dynamically identifies and updates overlapping workspace areas within the operating range of each crane, including: Receive continuous and stable motion trajectory data of the boom end and the center of gravity of the suspended object, and combine the spatial position coordinates of the boom end, the three-dimensional attitude angle and the real-time spatial position coordinates of the center of gravity of the suspended object to obtain the position distribution and motion state description of the boom entity and the suspended object entity in three-dimensional space. Based on the positional distribution and motion state description of the boom entity and the suspended object entity in three-dimensional space, the three-dimensional working space envelope of each crane at the current moment is calculated according to the boom length, suspended object size and preset safety distance parameters of each crane. Based on the three-dimensional operating space envelope of each crane, the spatial overlap region between any two cranes' three-dimensional operating space envelopes is identified through spatial geometric intersection operations, thus obtaining the initial set of cross-operating space regions; Based on the initial set of overlapping work space regions, and combined with the future motion trajectory data of the boom and the load, the evolution trend of the overlapping work space regions in the time dimension is predicted. The boundaries of the spatially overlapping regions are dynamically expanded and updated in time to obtain the dynamically changing overlapping work space regions. Based on the dynamic cross-operation space area, the area is divided and prioritized according to the spatial coordinate range, time duration and number of cranes involved, to obtain structured cross-operation space area data.

[0010] Furthermore, based on the cross-operation space area and the real-time spatial position and trajectory of each boom and load within the cross-operation space area, the real-time distances and relative motion trends between booms, between booms and loads, and between loads are calculated to obtain the collision risk level, including: Receive structured cross-operation space area data, and combine the spatial position coordinates of the boom end, three-dimensional attitude angle and real-time spatial position coordinates of the center of gravity of the load to extract the current spatial position information of all boom entities and load entities located in the cross-operation space area; Based on the current spatial location information, combined with the continuous and stable motion trajectory data of the boom end and the center of gravity of the suspended object, the real-time spatial distances between each boom entity, between each boom entity and the suspended object entity, and between each suspended object entity are calculated to form a multi-dimensional distance matrix; Based on a multi-dimensional distance matrix and combined with the motion trajectory data of the boom and the load, the relative motion velocity vector and relative motion direction between each entity pair are calculated, and the approach and separation trends between entities are analyzed. Based on relative motion trends and real-time spatial distances, and according to preset safety distance thresholds and dynamic risk assessment rules, the collision probability between booms, between booms and suspended objects, and between suspended objects is graded and assessed to obtain a preliminary collision risk index. Based on the preliminary collision risk index, and combined with the priority ranking of the cross-operation space areas and the risk duration factor, the risk indices are weighted, integrated, and accumulated over time to obtain the final collision risk level.

[0011] Furthermore, based on the collision risk level, control commands containing the warning level, avoidance direction, and reference speed are generated and issued to the corresponding crane to implement warning and active avoidance control, including: Receive the final collision risk level, convert the collision risk level into the corresponding warning level according to the preset risk level mapping rules, and determine the set of target cranes that need to perform avoidance operations; Based on the set of target cranes and combined with the future movement trajectory data of the boom and the load, the movement path conflict of each target crane in the cross-operation space area is analyzed to determine the avoidance priority order of each target crane; Based on the priority order of avoidance and combined with the structured cross-operation space area data, the safe movement direction of each target crane during the avoidance process is calculated, and the avoidance direction command is generated. Based on the avoidance direction command, combined with the spatial position coordinates of the boom end, three-dimensional attitude angle and real-time spatial position coordinates of the center of gravity of the suspended object, the maximum safe movement speed of each target crane in the avoidance direction is calculated, and a reference speed command is generated. The warning level, avoidance direction command, and reference speed command are integrated to generate a control command that includes the warning level, avoidance direction, and reference speed. This command is then sent to the corresponding target crane in real time via the communication network, triggering the audible and visual warning device at the crane end to implement warning and active avoidance control.

[0012] Secondly, the multi-machine hoisting power emergency repair collision prevention and early warning control system in mountainous areas includes: The acquisition module is used to acquire raw real-time data collected by the positioning devices, attitude sensors and load status sensors mounted on each crane; construct data correlation relationships based on the data from multiple sources, analyze the inherent characteristics of the correlation relationships to generate dynamic correction coefficients; perform grid interpolation to complete the missing sampling points in the raw real-time data, and then fuse and correct the interpolated raw real-time data through the dynamic correction coefficients to obtain multi-source fused correction data; The fusion module is used to calculate the spatial position and attitude of the end of each crane boom based on multi-source fusion correction data, and to fuse the state data of the suspended object to calculate the real-time spatial position of the suspended object; The prediction module is used to establish the coupled motion relationship between the boom and the suspended object based on the real-time spatial location, and predict the motion trajectory of the boom and the suspended object within a set time period in the future based on the coupled motion relationship. The identification module is used to dynamically identify and update the overlapping work space areas within the working range of each crane based on real-time spatial location and movement trajectory; The calculation module is used to calculate the real-time distances and relative motion trends between booms, between booms and loads, and between loads based on the cross-operation space area and the real-time spatial position and motion trajectory of each boom and load within the cross-operation space area, so as to obtain the collision risk level. The processing module is used to generate and send control commands containing warning level, avoidance direction and reference speed to the corresponding crane according to the collision risk level, so as to implement warning and active avoidance control.

[0013] Thirdly, a computing device, comprising: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0014] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0015] The above-described solution of the present invention has at least the following beneficial effects: Because this invention employs a precise processing technique that integrates multi-source sensor data timestamp alignment, gridded interpolation completion, and dynamic correction coefficient fusion, it establishes a dynamic coupled motion relationship between the boom and the load, predicts future motion trajectories, dynamically identifies and updates overlapping work areas and prioritizes them, and constructs a multi-dimensional collision risk assessment system that combines real-time distance, relative motion trends, and risk duration. Simultaneously, it generates precise control commands including warning levels, avoidance directions, and reference speeds. Therefore, it effectively overcomes the technical problems of insufficient multi-source data fusion and correction capabilities, lack of coupled motion trajectory prediction, delayed dynamic identification of overlapping work areas, one-sided collision risk assessment, and inaccurate warning and avoidance control in existing technologies. This results in improved accuracy in calculating the spatial position of the boom and the load, reduced motion trajectory prediction deviations, timely and accurate identification of collision risks, and efficient implementation of active avoidance, thereby enhancing the safety and collaborative efficiency of multi-machine lifting power repair operations in mountainous areas. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the collision prevention and early warning control method for multi-machine hoisting power emergency repair in mountainous areas provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of a multi-machine hoisting power emergency repair anti-collision early warning control system for mountainous areas provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of a computing device. Detailed Implementation

[0017] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0018] like Figure 1 As shown, an embodiment of the present invention proposes a collision prevention and early warning control method for multi-machine hoisting power emergency repair in mountainous areas. The method includes the following steps: Step 1: Acquire raw real-time data collected by the positioning devices, attitude sensors, and load status sensors mounted on each crane; construct data association relationships based on the data from multiple sources, analyze the inherent characteristics of the association relationships to generate dynamic correction coefficients; perform gridded interpolation to complete the missing sampling points in the raw real-time data, and then fuse and correct the interpolated raw real-time data using dynamic correction coefficients to obtain multi-source fused correction data; Step 2: Based on the multi-source fusion correction data, calculate the spatial position and attitude of the end of each crane boom, and fuse the state data of the suspended object to calculate the real-time spatial position of the suspended object; Step 3: Based on the real-time spatial location, establish the coupled motion relationship between the boom and the suspended object, and predict the motion trajectory of the boom and the suspended object within a set time period in the future based on the coupled motion relationship; Step 4: Based on real-time spatial location and movement trajectory, dynamically identify and update the overlapping work space areas within the working range of each crane; Step 5: Based on the cross-operation space area and the real-time spatial position and motion trajectory of each boom and load in the cross-operation space area, calculate the real-time distance and relative motion trend between booms, between booms and loads, and between loads to obtain the collision risk level. Step 6: Based on the collision risk level, generate and issue control commands containing the warning level, avoidance direction and reference speed to the corresponding crane to implement warning and active avoidance control.

[0019] In this embodiment of the invention, because the invention acquires the original real-time data from multiple sources of sensors of each crane, generates dynamic correction coefficients by constructing data association relationships, performs grid interpolation to complete missing sampling points, and then fuses and corrects the data to obtain multi-source fusion correction data, calculates the spatial position and attitude of the boom end and the real-time spatial position of the suspended object based on the data, establishes the coupled motion relationship between the boom and the suspended object and predicts the motion trajectory for a future set period, dynamically identifies and updates the cross-operation space area, and calculates the real-time distance and relative motion trend between entities by combining the real-time spatial position and motion trajectory to determine the collision risk level, and finally generates control commands including warning level, avoidance direction and reference speed and sends them to the corresponding crane, the invention effectively overcomes the technical problems of insufficient multi-source data fusion correction, lack of coupled motion trajectory prediction, static identification of cross-operation areas, one-sided collision risk assessment and inaccurate warning and avoidance control in existing multi-crane lifting collision avoidance technology in mountainous areas. Thus, it achieves the technical effects of improving the spatial positioning accuracy of the boom and the suspended object, improving the accuracy of motion trajectory prediction, timely and accurate identification of collision risks, and achieving efficient active avoidance, thereby enhancing the safety and collaborative efficiency of multi-crane lifting operations in mountainous power emergency repairs.

[0020] In a preferred embodiment of the present invention, step 1 above may include: Step 1.1: Receive the raw real-time data packets periodically reported by each crane; perform timestamp alignment processing on the raw real-time data packets to unify the time base of the data, resulting in a time-aligned multi-source data sequence. Specifically, each truck crane participating in the lifting operation at the repair site is equipped with a positioning device, attitude sensor, and load status sensor. Each sensor periodically reports raw real-time data packets to the on-site collision avoidance warning control center according to a pre-set fixed cycle. The reporting cycle of the positioning device is 200 milliseconds, the reporting cycle of the attitude sensor is 150 milliseconds, and the reporting cycle of the load status sensor is 200 milliseconds. After receiving the raw real-time data packets from all cranes, first extract and verify the sensor acquisition timestamps carried in each data packet, and remove invalid data packets with forged or damaged timestamps. Then, select the standard clock of the on-site Beidou positioning base station as the unified time base, convert the timestamps of raw real-time data packets from different sensors and different reporting cycles into the time format of the standard clock, and adjust the time axis of data packets with timestamp deviations to ensure that the positioning data, attitude data, and load status data of all cranes are synchronized in the time dimension, ultimately forming a time-aligned multi-source data sequence.

[0021] Step 1.2: Based on the time-aligned multi-source data sequence, analyze the physical and geometric correlations between different sensor data. By analyzing the inherent consistency characteristics of the correlations over a period of time, identify the deviations and noise patterns among the sensor data, and obtain dynamic correction coefficients to compensate for the deviations and noise. Specifically, this includes: after acquiring the time-aligned multi-source data sequence, analyzing the physical and geometric correlations between different types of sensor data. At the geometric correlation level, analyzing the correlation between the crane body position data collected by the positioning device and the boom joint angle data collected by the attitude sensor. Based on the fixed segment length of the boom, clarifying the geometric correspondence between the boom joint angle change and the crane body position change; at the physical correlation level... At the level of analysis, the correlation between the boom start-stop speed data collected by the attitude sensor and the swing amplitude data of the suspended object collected by the object status sensor is analyzed to clarify the physical influence of the boom speed on the swing state of the suspended object. Then, a continuous 30-second time-aligned multi-source data sequence is selected as the analysis sample to monitor and record the inherent consistency characteristics of the correlation between various sensor data. By comparing the difference between the theoretical correlation data and the actual collected data in the same time period, the positional deviation of the positioning device caused by the mountain signal blockage and the periodic noise pattern of the attitude sensor caused by mechanical vibration are identified. For the identified deviations and noise patterns, the position correction coefficient for compensating for the spatial offset of the positioning data is calculated by statistically analyzing the mean deviation and the noise fluctuation range.

[0022] Step 1.3: For missing sampling points caused by sampling anomalies in time alignment, data points from the same sensor are constructed into a discrete point set in the spatiotemporal domain. Based on the spatial and temporal neighborhood information of the valid data points in the discrete point set, the estimated values ​​of the missing sampling points are calculated and completed to obtain a complete data sequence. Specifically, for missing sampling points in positioning data caused by sampling anomalies such as mountain occlusion after time alignment, the control center first constructs a discrete point set in the spatiotemporal domain for all valid data points collected by the same crane positioning device. The discrete point set contains the spatial location information and corresponding standard timestamp information of each valid data point. For each missing sampling point, the timestamps and spatial location information of the five valid data points before and after it in the time neighborhood are extracted. At the same time, the distribution characteristics of valid data points in the surrounding areas in the same time period in the spatial neighborhood are extracted. By fitting the position change trend in the time neighborhood and the position distribution pattern in the spatial neighborhood, the estimated spatial location value and the corresponding timestamp completion value of the missing sampling point are calculated. The estimated values ​​of all missing sampling points of the positioning data are completed in sequence. At the same time, the same completion operation is performed on a small number of missing sampling points of the attitude sensor and the suspended object status sensor, and finally a complete data sequence without missing data is obtained.

[0023] Step 1.4 involves comprehensively processing the complete data sequence with dynamic correction coefficients to obtain multi-source fusion correction data. Specifically, this includes: first, classifying and organizing the completed data sequence according to sensor type to form three independent data sequences: positioning data sequence, attitude data sequence, and suspended object status data sequence; then, comprehensively processing the corresponding dynamic correction coefficients with each type of data sequence. For the positioning data sequence, weighting the spatial position value of each data point with the position correction coefficient to correct its systematic spatial offset; for the attitude data sequence, filtering the joint angle value of each data point with the attitude correction coefficient to eliminate its periodic mechanical noise; for the suspended object status data sequence, combining its physical correlation with the boom attitude data, correcting the measurement deviation of the suspended object's swing amplitude through dynamic correction coefficients; finally, fusing the three corrected data sequences using multi-source data, assigning weights to different data types according to the accuracy of data acquisition, and integrating them to form multi-source fusion correction data that combines time synchronization, data integrity, and numerical accuracy.

[0024] In this embodiment of the invention, because the invention receives the raw real-time data packets periodically reported by each crane and performs timestamp alignment to unify the data time reference, analyzes the physical and geometric correlation of different sensor data based on the time-aligned multi-source data sequence and generates dynamic correction coefficients to compensate for deviations and noise, and constructs a spatiotemporal discrete point set and calculates the estimated value to complete the missing sampling points caused by sampling anomalies, and then integrates the complete data sequence with the dynamic correction coefficients, the invention effectively overcomes the technical problems of inconsistent time references, deviations and noise in existing multi-source sensor data, and data loss caused by sampling anomalies, which in turn affects the accuracy of the data, and thus achieves the technical effect of obtaining accurate multi-source fusion correction data.

[0025] In a preferred embodiment of the present invention, step 2 above may include: Step 2.1: Receive and integrate multi-source fusion correction data from each crane. Establish a globally unified coordinate system based on the crane's positioning data. Based on the boom's structural parameters, joint connections, and motion constraints, construct a description of the boom's geometric motion relationships. Specifically, the on-site collision avoidance and early warning control center first receives and integrates multi-source fusion correction data from all participating cranes via a dedicated communication link. The positioning, attitude, and load status data of each crane are categorized and archived according to the crane number to ensure accurate data mapping to specific cranes. Then, the global unified coordinate system is established, using the center point of the uncollapsed high-voltage transmission tower base as a fixed reference origin. Combined with the crane's positioning data as the core benchmark, the three axes of the coordinate system are determined, with the parallel axis as the core reference. The horizontal axis is defined by pointing due east from the ground, the vertical axis by pointing due north parallel to the ground, and the vertical axis by pointing upwards perpendicular to the ground. The coordinate system uses meters for length and degrees for angles, establishing a globally unified coordinate system. Next, the complete structural parameters of each crane boom are retrieved, including the number of boom segments, the base length of each segment, the number of joints, the connection methods of each joint, and the motion constraints of the joints, such as the pitch and yaw angle ranges of the main boom joints, the maximum extension length and minimum retraction length of the telescopic boom, etc. Based on these parameters, the motion relationship of each boom segment relative to the previous segment and the positional relationship of the overall boom relative to the crane body are clarified, ultimately forming a geometric motion relationship description that accurately describes the boom's motion.

[0026] Step 2.2: Based on the description of geometric motion relationships, and combining the real-time angle data of each boom joint and the real-time length data of the telescopic boom in the multi-source fusion correction data, the precise spatial position coordinates of the end of each crane boom in the global unified coordinate system are obtained through geometric derivation and calculation. Specifically, this includes: after obtaining the description of geometric motion relationships, the spatial position coordinates of the end of the crane boom are calculated; firstly, taking a certain crane as an example, starting from the base position of the crane body, and combining the real-time angle data of the root joint of the crane boom in the multi-source fusion correction data, the spatial tilt direction of the first boom section relative to the crane base is determined. Then, based on the real-time length data of the boom section, the preliminary spatial position of the end of the first boom section relative to the base is calculated. Next, taking the end of the first boom section as a new starting point, and combining the real-time angle data of the second boom section joint and the real-time length data of the second boom section, the spatial position offset of the end of the second boom section relative to the end of the first boom section is derived. After superposition, the spatial position of the end of the second boom section relative to the crane base is obtained. This derivation is repeated section by section until the calculation reaches the last segment of the boom. Finally, the spatial position of the boom end relative to the crane base is transformed into a global unified coordinate system. Through the coordinate transformation rules of the coordinate system, the position deviation of the crane body in the global coordinate system is corrected, and the precise spatial position coordinates of the boom end of the crane in the global unified coordinate system are finally obtained. Following the same derivation and calculation process, the spatial position coordinates of the boom ends of all cranes involved in the lifting are calculated sequentially to ensure that each coordinate data can be compared under a unified benchmark.

[0027] Step 2.3: Based on the spatial position coordinates, the real-time measurement data of the boom attitude sensor in the multi-source fusion correction data is fused. Through geometric transformation and compensation processing, the three-dimensional attitude angle of the boom end in the global unified coordinate system is calculated. Specifically, this includes: Based on the spatial position coordinates of the boom end, the control center initiates the calculation of the three-dimensional attitude angle of the boom end; First, the raw attitude data measured in real time by the boom attitude sensor in the multi-source fusion correction data is retrieved, including the real-time tilt angle and real-time deflection angle of the boom end; Then, combined with the existing spatial position coordinates of the boom end, the local attitude data collected by the attitude sensor is transformed to the attitude reference of the global unified coordinate system through geometric transformation, eliminating the attitude deviation between the local coordinate system and the global coordinate system; At the same time, targeted compensation processing is performed for the measurement error of the attitude sensor caused by boom vibration and the mechanical deviation during sensor installation. For example, mean filtering compensation is performed for the angle fluctuation caused by periodic vibration, and fixed numerical compensation is performed for the angle offset caused by installation deviation. After transformation and compensation processing, the three-dimensional attitude angle of the boom end in the global unified coordinate system is finally calculated.

[0028] Step 2.4: Based on the spatial position coordinates and three-dimensional attitude angles of the boom end, the real-time length of the lifting rope, the real-time swing angle of the suspended object, and the swing angular velocity data collected by the suspended object state sensor in the multi-source fusion correction data are fused. Through spatial geometric relationships, the real-time spatial position of the suspended object's center of gravity in the global unified coordinate system is calculated. Specifically, this includes: after obtaining the spatial position coordinates and three-dimensional attitude angles of the boom end, the real-time spatial position of the suspended object's center of gravity is calculated; firstly, the real-time length data of the lifting rope collected by the suspended object state sensor in the multi-source fusion correction data is retrieved. Starting from the spatial position of the boom end, the three-dimensional attitude angles of the boom end are combined to determine the real-time spatial position of the suspended object's center of gravity. The initial spatial extension direction of the rope is used to calculate the preliminary spatial position of the end of the rope based on its real-time length. Then, combined with the real-time swing angle data of the suspended object collected by the object's status sensor, the swing offset of the suspended object in the horizontal and vertical axes is corrected. Then, combined with the real-time swing angular velocity data of the suspended object, the position advance or lag deviation caused by the swing inertia of the suspended object is compensated and corrected. Finally, based on the structural dimension parameters of the main tower of the hoisted tower, the position offset of the center of gravity of the main tower relative to the rope connection point is determined. This offset is superimposed on the spatial position of the end of the rope, and finally, the real-time spatial position of the center of gravity of the suspended object in the global unified coordinate system is calculated.

[0029] In this embodiment of the invention, because the invention establishes a globally unified coordinate system based on the crane body positioning data, constructs a geometric motion relationship description by combining the boom structure parameters, joint connection relationships, and motion constraints, and then integrates the real-time angles of the boom joints, the length of the telescopic boom, the attitude measurement data, the length of the hoisting rope, and the swing parameters of the suspended object from the multi-source fusion correction data, through geometric relationship derivation calculation, transformation compensation, and spatial geometric operations, it effectively overcomes the technical problems in the prior art that lack a unified spatial reference and do not fully combine the boom structure with multi-source accurate data for calculation, resulting in inaccurate calculation of the spatial position of the boom end, the three-dimensional attitude angle, and the real-time spatial position of the center of gravity of the suspended object, and inconsistent references. Thus, it achieves the technical effect of accurately obtaining the precise spatial position coordinates of the boom end, the three-dimensional attitude angle, and the real-time spatial position of the center of gravity of the suspended object under a globally unified coordinate system.

[0030] In a preferred embodiment of the present invention, step 3 above may include: Step 3.1: Receive the spatial position, three-dimensional attitude angle, and real-time spatial position coordinates of the center of gravity of the hoisted object at the end of the boom. Calculate the real-time relative position vector and spatial connection relationship between the end of the boom and the center of gravity of the hoisted object. Specifically, the on-site anti-collision early warning control center first receives the spatial position coordinates, three-dimensional attitude angles, and real-time spatial position coordinates of the center of gravity of the hoisted object from all cranes involved in the hoisting process, obtained from the previous steps, through an internal data transmission link. The data is then classified and archived according to the crane number and the time node of data acquisition to ensure that each set of data accurately corresponds to the crane's operating status at the same moment. Subsequently, the calculation of the real-time relative position vector is initiated. Taking a single crane as an example, the spatial coordinates of the end of the boom are taken as the starting point, and the real-time spatial coordinates of the center of gravity of the suspended object are taken as the ending point. Based on the three-axis directions of the global unified coordinate system, the position difference between the two points in the three directions of the horizontal, vertical, and longitudinal axes is calculated to determine the direction and length of the relative position vector, thus clarifying the spatial positional relationship between the end of the boom and the center of gravity of the suspended object. At the same time, the spatial connection relationship between the two is analyzed. Combined with the actual working conditions of the hoisting scenario, the physical constraints of the rigid suspension connection between the end of the boom and the center of gravity of the suspended object through the hoisting rope are clarified, as well as the dynamic influence of the change in the length of the hoisting rope and the swing angle of the suspended object on the connection relationship. Finally, a real-time relative position vector and spatial connection relationship that can accurately reflect the spatial relationship between the two are formed.

[0031] Step 3.2: Based on the real-time relative position vector and spatial connectivity, and combined with the boom joint motion speed and load swing state data from the multi-source fusion correction data, analyze the transmission influence characteristics of boom motion on load swing, and establish the dynamic coupling relationship between boom motion and load motion. Specifically, after obtaining the real-time relative position vector and spatial connectivity, retrieve the boom joint motion speed data and load swing state data of the crane from the multi-source fusion correction data. The boom joint motion speed includes the main joint pitch speed, telescopic boom extension speed, and rotation speed, while the load swing state data includes the swing angle and swing angular velocity. Then, begin analyzing the transmission influence characteristics of boom motion on load swing, first selecting 20 consecutive seconds of historical operation data as the analysis sample. The study statistically analyzed the changes in the swing amplitude of the suspended load under different movement speeds of each joint of the boom. For example, it measured the percentage increase in the swing angle of the suspended load when the pitch speed of the main boom joint increased, and the peak value of the swing angular velocity of the suspended load due to inertia when the telescopic boom suddenly stopped extending or retracting. By comparing the transmission influence patterns under different motion states, the study identified the positive correlation between the boom movement speed and the swing amplitude of the suspended load, as well as the hysteresis response characteristics of the boom movement start and stop and the inertial swing of the suspended load. Based on these transmission influence characteristics and the spatial connection relationship between the boom end and the center of gravity of the suspended load, a dynamic correlation model of the boom joint motion parameters and the swing parameters of the suspended load was constructed. This clarified the motion response law of the suspended load corresponding to each motion action of the boom, and finally established the dynamic coupling relationship between the boom motion and the suspended load motion.

[0032] Step 3.3: Based on the dynamic coupling relationship, and combining the current operation command signals and historical motion trend data of each crane, predict the time series motion trajectory of the boom end within a set time period in the future. Specifically, this includes: determining the prediction time period of the future motion trajectory, and combining the operation rhythm of power emergency repair and hoisting in mountainous areas, setting the set time period to five seconds, and setting the time series interval to one hundred milliseconds, that is, each predicted trajectory contains position data of fifty time nodes. Subsequently, based on the established dynamic coupling relationship, the current operation command signals of each crane are retrieved, such as boom extension command, boom left rotation command, boom pitch and lift command, etc., to clarify the expected movement direction and amplitude of the boom of each crane. At the same time, the historical movement trend data of each crane over the past 30 seconds are retrieved to analyze the average movement speed, movement direction stability and other patterns of the boom during that period. For example, the average speed of boom extension and retraction of a certain crane over the past 30 seconds is 0.5 meters per second and there is no sudden change in movement direction. The current operation command signals are combined with the historical movement trend data, and according to the response rules of boom movement in the dynamic coupling relationship, the spatial position coordinates of the boom end of each crane in the next 5 seconds are predicted node by node. For example, at the first 100-millisecond node, the boom end moves 0.05 meters in the horizontal direction, has no offset in the vertical direction, and rises 0.03 meters in the vertical direction. The position prediction is completed for 50 time nodes in sequence, and finally the time sequence movement trajectory of the boom end in the future set period is formed.

[0033] Step 3.4: Based on the time-series motion trajectory of the boom end and the dynamic coupling relationship, calculate the time-series motion trajectory of the center of gravity of the hoisted object within a future set time period. Specifically, after obtaining the time-series motion trajectory of the boom end, the control center starts to calculate the time-series motion trajectory corresponding to the center of gravity of the hoisted object based on the established dynamic coupling relationship. Taking a single crane at a specific time point as an example, firstly, the spatial coordinates and motion state of the boom end at that point are extracted. Then, considering the transmission influence of boom motion on the swing of the suspended load in the dynamic coupling relationship, the swing angle offset and angular velocity change of the suspended load caused by the boom motion at that moment are calculated. For example, if the pitch velocity of the boom end at that point is five degrees per second, the corresponding swing angle of the suspended load increases by one degree and the angular velocity increases by 0.2 degrees per second. Next, considering the real-time length of the lifting rope and the inertial motion characteristics of the suspended load, the spatial position of the suspended load's center of gravity is corrected. For instance, due to inertia, the trajectory of the suspended load's center of gravity lags behind the trajectory of the boom end by 0.2 seconds, requiring lag compensation for the position at the corresponding time point. Following this method, the spatial position of the suspended load's center of gravity is calculated for each time point within the next five seconds, ultimately forming the time-series motion trajectory of the suspended load's center of gravity within a set future time period.

[0034] Step 3.5 involves performing spatiotemporal alignment and trajectory smoothing on the time-series motion trajectories of the boom end and the center of gravity of the suspended load to obtain continuous and stable future motion trajectories of the boom and the suspended load. Specifically, this includes: firstly, performing spatiotemporal alignment on the time-series motion trajectories of the boom end and the center of gravity of the suspended load, checking whether the number of time nodes and the time intervals of the two sets of trajectories are completely consistent. If there are misalignments in individual time nodes, the position data of the missing nodes is supplemented by linear interpolation, or redundant data of duplicate nodes is removed to ensure that the two sets of trajectories are completely synchronized in the time dimension, enabling position comparison at the same time node; then, the trajectory smoothing process is initiated. For abnormal position data points in the trajectory caused by instantaneous fluctuations of the sensor or sudden changes in motion state, the position data of the five adjacent time nodes before and after the data point are selected, and the abnormal points are corrected by calculating the average of the neighborhood positions. For example, if the vertical axis coordinate of the boom end of a certain node suddenly rises by one meter, the abnormal value is replaced by the average vertical axis coordinate of the five nodes before and after it. At the same time, the small vibrations caused by inertial oscillation in the trajectory of the center of gravity of the suspended object are smoothed to ensure that the trajectory curve is continuous without obvious inflection points. After spatiotemporal alignment and trajectory smoothing, a continuous and stable future motion trajectory of the boom and the suspended object is finally obtained.

[0035] In this embodiment of the invention, because the invention first calculates the real-time relative position vector and spatial connection relationship between the end of the boom and the center of gravity of the suspended object, then combines the boom joint movement speed and the swing state data of the suspended object to establish a dynamic coupling relationship between the boom and the suspended object, and then, based on this coupling relationship and combined with the current operation command of the crane and the historical movement trend, predicts the time sequence movement trajectory of the end of the boom for a future set period of time, calculates the trajectory corresponding to the center of gravity of the suspended object, and finally performs spatiotemporal alignment and smoothing processing on the two types of trajectories, it effectively overcomes the technical problems in the prior art that the coupling motion relationship between the boom and the suspended object is not established, that only the movement trajectory of a single component can be predicted and the trajectory data is not continuous and stable, and that the inertial swing trajectory of the suspended object with the boom movement cannot be accurately predicted, thus achieving the technical effect of obtaining a continuous and stable future movement trajectory of the boom and the suspended object under a globally unified benchmark, and improving the accuracy and foresight of trajectory prediction.

[0036] In a preferred embodiment of the present invention, step 4 above may include: Step 4.1: Receive continuous and stable motion trajectory data of the boom end and the center of gravity of the suspended load. Combine this with the spatial coordinates of the boom end, the three-dimensional attitude angle, and the real-time spatial coordinates of the center of gravity of the suspended load to obtain the position distribution and motion state description of the boom entity and the suspended load entity in three-dimensional space. Specifically, this includes: First, receiving the continuous and stable motion trajectory data of all crane boom ends and the center of gravity of the suspended load obtained in the previous steps through the data transmission channel. Simultaneously, retrieving the current spatial coordinates of the boom end, the three-dimensional attitude angle, and the real-time spatial coordinates of the center of gravity of the suspended load for each crane. Archive these data according to the crane number and data acquisition time to ensure time consistency between the motion trajectory data and the real-time status data. Then, based on a globally unified coordinate system, [the data is processed separately]. The system analyzes the positional distribution of each crane boom entity, clarifying the extension range of the boom from its root to its tip in three-dimensional space, as well as the spatial orientation of the boom in its current posture. Simultaneously, it determines the current spatial landing point and the three-dimensional volume occupied by the suspended object. Regarding motion state description, it combines continuous and stable motion trajectory data to extract the real-time motion speed and direction change trends of each crane boom, as well as the swing frequency and amplitude changes of the suspended object, clarifying whether the motion of the boom and the suspended object is uniform, accelerating, or decelerating, and whether there is any adjustment in motion direction. Finally, it integrates all information to form a complete positional distribution and motion state description that includes the spatial position range, motion speed, motion direction, and swing characteristics of each crane boom and the suspended object.

[0037] Step 4.2: Based on the description of the positional distribution and motion state of the boom and the load in three-dimensional space, and according to the boom length, load size, and preset safety distance parameters of each crane, calculate the three-dimensional working space envelope of each crane at the current moment. Specifically, after obtaining the description of the positional distribution and motion state of the boom and the load, first retrieve the equipment parameter file of each crane, extract the maximum extension length, minimum retraction length, cross-sectional dimensions of each segment of the boom, and the length, diameter, weight, and other load size data of the currently hoisted tower main pole; simultaneously, considering the narrow working space and high collision risk in mountainous areas, preset safety distance parameters are established. These parameters comprehensively consider crane operation errors and load swing redundancy space, and are set to be greater than the existing technical standards. The system first determines the safe distance value to ensure sufficient clearance for maneuvering. Then, using the position of each crane's base in the global coordinate system as the core reference point, and combining the boom's structural parameters and current three-dimensional attitude angle, it determines the maximum range of motion of the boom in three-dimensional space. The actual extension length of the boom is added to the preset safe distance to form the boundary of the envelope in the boom's extension direction. Simultaneously, based on the dimensions of the load, the space occupied by the load is extended outwards by a preset safe distance in all directions to form the boundary of the envelope around the load. Combining the boom's range of motion boundary and the load's extension boundary, a three-dimensional space area that completely covers all areas that the boom and load may touch during the crane's current operation is constructed, i.e., the three-dimensional working space envelope of each crane at the current moment.

[0038] Step 4.3: Based on the 3D operating space envelope of each crane, identify the spatial overlap region between any two cranes' 3D operating space envelopes through spatial geometric intersection operations, obtaining an initial set of intersecting operating space regions. Specifically, this includes: after obtaining the 3D operating space envelopes of all cranes, initiating the spatial geometric intersection operation process to perform pairwise comparison analysis on the 3D operating space envelopes of any two cranes; first, selecting the envelope of one crane as the reference envelope, extracting the coordinate ranges in the horizontal, vertical, and triangular axes of the globally unified coordinate system; then sequentially selecting the envelopes of each of the other cranes... The envelope, used as the comparison envelope, also has its coordinate ranges extracted along its three axes. By determining whether there is an overlap in the coordinate ranges of the two envelopes along the three axes (i.e., the minimum horizontal axis value of the reference envelope is less than the maximum horizontal axis value of the comparison envelope, and the maximum horizontal axis value of the reference envelope is greater than the minimum horizontal axis value of the comparison envelope; the same applies to the vertical axis), if there is an overlap in all three axes, then the two envelopes are determined to have a spatial overlap region. The three-dimensional coordinate range of the overlap region is recorded, including the start and end values ​​of the overlap along the horizontal, vertical, and axial axes, and this is used as an initial cross-operation space region. Following the pairwise comparison method described above, the intersection calculation of the envelopes between all participating hoisting cranes is completed, and all identified spatial overlap regions are collected to form an initial cross-operation space region set.

[0039] Step 4.4: Based on the initial set of overlapping work space regions, and combined with the future motion trajectory data of the crane boom and the load, predict the evolution trend of the overlapping work space regions in the time dimension. Dynamically expand and update the boundaries of the spatially overlapping regions to obtain dynamically changing overlapping work space regions. Specifically, this includes: based on the initial set of overlapping work space regions, retrieving all future motion trajectory data of the crane boom and the load obtained in the previous steps, and starting to predict the evolution trend of the overlapping work space regions in the time dimension; firstly, for each initial overlapping region, analyzing the future motion direction and speed of the two or more cranes involved, and determining how the region will gradually change with the movement of the cranes. Whether expanding, shrinking, or moving, for example, if one crane's boom extends towards the intersection area, the intersection area will expand along that direction; if both cranes move away from the intersection area, the intersection area will gradually shrink until it disappears. Based on the evolution trend, the boundary of the initial intersection area is dynamically expanded. For instance, if expansion is predicted, the boundary is extended according to the maximum future expansion range, ensuring that the expanded boundary covers all possible areas within a set future time period. Simultaneously, according to a time series, the three-dimensional coordinate range of each intersection area is updated at each time node, recording the changes in size and position of the area at different time nodes, forming time-seriesd area data. Through this dynamic expansion and time-series update, a dynamic intersection work space area reflecting real-time changes in spatial overlap during operations is obtained.

[0040] Step 4.5: Based on the dynamic cross-operation spatial area, divide and prioritize the area according to its spatial coordinate range, duration, and the number of cranes involved to obtain structured cross-operation spatial area data. Specifically, after obtaining the dynamic cross-operation spatial area, first divide it according to its spatial coordinate range, merging spatially adjacent areas involving the same cranes into a single comprehensive area, and dividing spatially independent areas into different independent areas, clearly defining the specific three-dimensional coordinate boundaries and coverage area of ​​each area; then, calculate the duration of each dynamic cross-operation area, i.e., the total duration of the area from its appearance to its disappearance in the time series, distinguishing short-term instantaneous cross-operations. The system identifies cross-regions and persistent cross-regions; it also records the number of cranes involved in each region, distinguishing between binary regions with two cranes crossing and multi-regions with three or more cranes crossing; based on the division results, priority is ranked, with multi-regions having higher priority than binary regions, regions with longer durations having higher priority than regions with shorter durations, and regions with larger spatial ranges having higher priority than regions with smaller spatial ranges, because multi-regions, regions with longer durations, and regions with larger spatial ranges have a higher probability of collision and more severe consequences; finally, the spatial coordinate range, duration, number of cranes involved, priority level, and other information of each region are integrated to form structured cross-operation spatial region data.

[0041] In this embodiment of the invention, because the invention uses the continuous and stable motion trajectory and real-time spatial position data of the boom end and the center of gravity of the suspended object to clarify its three-dimensional spatial position distribution and motion state, and calculates the three-dimensional working space envelope of each crane by combining the boom length, the size of the suspended object and the preset safety distance, identifies the initial cross-operation space area through spatial geometric intersection operation, and then predicts the evolution trend of the area by combining the future motion trajectory and dynamically expands and updates the area boundary, and finally completes the area division and priority sorting according to the area spatial range, duration and number of cranes involved, the invention effectively overcomes the technical problems of static cross-operation space area identification, inability to adapt to the spatial changes of crane dynamic operation, and lack of area priority distinction leading to the omission of high-risk areas in the prior art, thus achieving the technical effects of accurately obtaining dynamically changing and structured cross-operation space area data, predicting the evolution trend of cross areas in advance, and clarifying the risk priority of different areas.

[0042] In a preferred embodiment of the present invention, step 5 above may include: Step 5.1: Receive the structured cross-operation space area data. Combine the spatial coordinates of the boom end, the three-dimensional attitude angle, and the real-time spatial coordinates of the load's center of gravity to extract the current spatial position information of all boom entities and load entities located within the cross-operation space area. Specifically, the on-site collision avoidance warning control center first receives the structured cross-operation space area data generated in the previous steps through a dedicated data interface. The data includes information such as the three-dimensional coordinate range, priority level, number of cranes involved, and duration of time for each cross-area. Then, it retrieves the current spatial coordinates of the boom end, the three-dimensional attitude angle, and the real-time spatial position of the load's center of gravity for all cranes. Coordinate data is categorized and organized according to crane number and entity type, clarifying the coordinate information corresponding to the boom entity and the suspended object of each crane. Next, for each structured cross-operation space area, the coordinate boundary range of its horizontal, vertical, and longitudinal axes is extracted. The key position coordinates of each crane boom entity and the center of gravity coordinates of the suspended object are compared one by one to determine whether these coordinates fall within the coordinate boundary range of the cross-operation area. If the coordinates of any segment of a crane boom entity or the center of gravity coordinates of a suspended object are within the cross-operation area, the entity is determined to belong to the participating entity within that cross-operation area, and its corresponding crane number, entity type, and complete current spatial position coordinates are recorded. Through the above method, the screening and information extraction of crane boom entities and suspended objects in all cross-operation space areas are completed, and finally, complete spatial position information including entity identifier, belonging cross-operation area, and current three-dimensional coordinates is formed.

[0043] Step 5.2: Based on the current spatial location information and combined with the continuous and stable motion trajectory data of the boom end and the center of gravity of the suspended object, calculate the real-time spatial distances between each boom entity, between each boom entity and the suspended object entity, and between each suspended object entity, forming a multi-dimensional distance matrix. Specifically, this includes: after obtaining the current spatial location information of all entities within the intersection area, identifying the entity combination type for which distance needs to be calculated, including combinations between boom entities within the same intersection area, combinations between each boom entity and each suspended object entity, and combinations between each suspended object entity; for each entity combination, using a globally unified coordinate system as a reference, calculate the distance between the two entities in three-dimensional space based on their current spatial location coordinates. The straight-line distance can intuitively reflect the current spatial proximity between entities. For example, the straight-line distance between the coordinates of the end of the boom of crane 1 and the coordinates of the center of gravity of the load of crane 2 is calculated, and the straight-line distance between the coordinates of the middle section of the boom of crane 2 and the coordinates of the end of the boom of crane 3 is calculated. In the calculation process, for entities with a certain length and volume, such as boom entities, the coordinates of their key feature points are selected for distance calculation, including three feature points: the root of the boom, the middle section of the boom, and the end of the boom. This ensures that no distance risk between any part of the boom and other entities is missed. The real-time spatial distance of all entity combinations is classified and organized according to the entity combination type and the intersection area to which they belong, and a multi-dimensional distance matrix is ​​constructed.

[0044] Step 5.3: Based on the multi-dimensional distance matrix and combined with the motion trajectory data of the boom and the load, calculate the relative motion velocity vector and relative motion direction between each entity pair, and analyze the approach and separation trends between entities. Specifically, this includes: based on the constructed multi-dimensional distance matrix, retrieving the continuous and stable motion trajectory data corresponding to all entities within the intersection area. The data includes the spatial position coordinates of each entity at each time node within a future set time period. For each entity combination, select the position coordinates of multiple consecutive time nodes in the trajectory data, calculate the position change of the two entities in the entity combination within the same time interval, and combine the time interval length to obtain the absolute motion velocity and motion direction of each entity. By performing vector operations on the absolute motion velocity and motion direction of the two entities, the relative motion velocity vector between them is obtained. The magnitude of the vector reflects the speed at which the entities approach or move away from each other, and the direction of the vector reflects the specific orientation of the relative motion. Simultaneously, by combining the real-time spatial distance change trend in the multi-dimensional distance matrix of the entity combination, the motion relationship between the two is analyzed: if the direction of the relative motion velocity vector points to the other entity and the real-time spatial distance is continuously decreasing, it is determined that the two have a tendency to approach each other; if the direction of the relative motion velocity vector is away from the other entity and the real-time spatial distance is continuously increasing, it is determined that the two have a tendency to separate. The relative motion velocity vector, relative motion direction and motion trend are calculated and judged for each entity combination, forming a complete inter-entity motion trend analysis report.

[0045] Step 5.4: Based on relative motion trends and real-time spatial distance, and according to preset safety distance thresholds and dynamic risk assessment rules, the collision probability between booms, between booms and loads, and between loads is graded and assessed to obtain a preliminary collision risk index. Specifically, this includes: First, based on the operational characteristics of power emergency repair hoisting in mountainous areas, and considering factors such as crane equipment size, load weight, and operational precision requirements, differentiating safety distance thresholds are preset: The safety distance threshold between boom entities is set to be greater than twice the boom cross-sectional size; the safety distance threshold between boom entities and load entities is set to be greater than the sum of the maximum swing amplitude of the load and the boom operation error; and the safety distance threshold between load entities is set to be greater than the sum of the maximum dimensions of the two loads, ensuring that the safety redundancy of different types of entity combinations is adapted to their collision risks. Risk level; simultaneously, dynamic risk assessment rules are formulated, which clearly combine real-time spatial distance and relative motion trend for comprehensive judgment: if the real-time spatial distance is less than the safe distance threshold and there is a tendency to approach, it is judged as high risk tendency; if the real-time spatial distance is equal to the safe distance threshold and the motion trend is stable, it is judged as medium risk tendency; if the real-time spatial distance is greater than the safe distance threshold and there is a tendency to separate, it is judged as low risk tendency; based on the preset safe distance threshold and dynamic risk assessment rules, the collision probability of each group of entities is graded and assessed, and a corresponding preliminary collision risk index is assigned to different risk tendencies. High risk tendency corresponds to a higher index value, medium risk tendency corresponds to a medium index value, and low risk tendency corresponds to a lower index value, and finally, the preliminary collision risk index of all entity combinations is obtained.

[0046] Step 5.5: Based on the preliminary collision risk index, and combining the priority ranking results of the overlapping work space areas and the risk duration factor, a weighted fusion and time-cumulative calculation is performed on each risk index to obtain the final collision risk level. Specifically, this includes: first, assigning corresponding weight coefficients to the priority ranking results of the overlapping work space areas and the risk duration factor, with the weight coefficient for the priority of the overlapping area being higher than that for the risk duration factor, because collisions in high-priority areas have more severe consequences and require priority consideration; specifically, the weight coefficient for the priority of the overlapping area is set to 0.6, and the weight coefficient for the risk duration is set to 0.4; subsequently, for the preliminary collision risk index of each entity combination, combined with the priority level of its corresponding overlapping area... The weighted scores of the entities and the weighted scores corresponding to the duration of risk within the intersection area are weighted and fused to obtain the fused risk index. Based on this, a time accumulation calculation is performed. If the fused risk index of a certain entity combination remains at a high level for multiple consecutive time points, its risk index is accumulated to reflect the cumulative effect of risk. If the risk index gradually decreases over time, no accumulation is performed, and the current fused risk index is maintained. Finally, based on the results of the weighted fusion and time accumulation calculations, the final collision risk level is divided into four levels, usually from high to low: extremely dangerous, highly dangerous, moderately dangerous, and low dangerous. Different levels correspond to different risk index ranges, ultimately obtaining the final collision risk level for all entity combinations.

[0047] In this embodiment of the invention, the present invention employs a technical approach that first extracts the current spatial position information of the boom and the suspended object within the cross-operation space area, then calculates the real-time spatial distance between the entities by combining the motion trajectory and forming a multi-dimensional distance matrix, subsequently analyzes the relative motion velocity vector and direction of each entity pair to determine the approach or separation trend, then obtains a preliminary collision risk index based on safety thresholds and dynamic rules, and finally combines the priority ranking results of the cross-area area with the risk duration for weighted fusion and time accumulation calculation to determine the final collision risk level. Therefore, this invention effectively overcomes the technical problems of existing technologies, such as single collision risk assessment dimension, reliance on real-time distance judgment while ignoring motion trend and regional risk weight, and inaccurate risk level classification. As a result, it achieves the technical effect of realizing multi-dimensional and refined collision risk classification assessment and accurately distinguishing risk levels under different scenarios.

[0048] In a preferred embodiment of the present invention, step 6 above may include: Step 6.1: Receive the final collision risk level. Based on the preset risk level mapping rules, convert the collision risk level into the corresponding warning level and determine the set of target cranes requiring avoidance operations. Specifically, the on-site collision avoidance warning control center first receives the final collision risk level of all entity combinations output from the previous steps through the internal data interaction link. The level includes four levels: extremely dangerous, highly dangerous, moderately dangerous, and low dangerous, each corresponding to a specific risk index range. Then, it retrieves the preset risk level mapping rules, which are a one-to-one mapping system developed based on the risk tolerance of mountainous operations, the severity of equipment damage consequences, and the impact on repair progress. The mapping relationship is as follows: extremely dangerous corresponds to Level 1 warning, high dangerous corresponds to Level 2 warning, moderate dangerous corresponds to Level 3 warning, and low dangerous corresponds to Level 4 warning, with Level 1 warning being the highest level and Level 4 warning being the lowest level. According to this mapping rule, the final collision risk level of each group of entities is converted into the corresponding warning level one by one, and the crane number involved in each warning level is recorded. Then, the entity groups involving Level 1 and Level 2 warnings are selected, because the collision risk of these two levels has reached the threshold that requires immediate avoidance operation. All cranes corresponding to these entity groups are extracted, and after removing duplicate numbers, a target crane set that requires avoidance operation is formed.

[0049] Step 6.2: Based on the target crane set and combined with the future motion trajectory data of the boom and the load, analyze the motion path conflicts of each target crane in the cross-operation space area, and determine the avoidance priority order of each target crane. Specifically, this includes: after determining the target crane set, retrieving the future motion trajectory data of the boom and the load of all target cranes, as well as the corresponding cross-area information in the structured cross-operation space area data; for each target crane in the cross-operation space area, visualizing the time-series motion trajectory within a future set time period in a globally unified coordinate system, comparing the motion paths of each target crane one by one, and analyzing whether there are any conflicts such as path overlap or intersection; for example, the future trajectory of the boom of crane one will cross... If the trajectory of the load being lifted by crane 2 is within the range of the crane's movement path, it is determined that there is a conflict in movement paths between the two. Subsequently, a priority ranking rule for avoiding the crane is established. The rule comprehensively considers the crane's working load status, the flexibility of movement adjustment, and the scope of risk impact. Specifically, the crane with a larger load capacity has a lower priority for avoiding the crane because the load has greater inertia when adjusting its movement state, making the operation more difficult. The crane whose movement trajectory is closer to the core area of ​​the conflict has a higher priority for avoiding the crane because it is closer to the collision point and has a more urgent response time. The crane involved in the conflict of multiple entities has a higher priority for avoiding the crane than the crane involved in the conflict of two entities because the impact range is wider and the collision consequences are more severe. The target cranes in each intersection area are prioritized to form a clear order of avoidance.

[0050] Step 6.3: Based on the avoidance priority order and combined with the structured cross-operation space area data, calculate the safe movement direction of each target crane during the avoidance process and generate avoidance direction instructions. Specifically, this includes: based on the avoidance priority order of each target crane, starting with the target crane with the highest priority, calculating its safe movement direction one by one; first, retrieving the structured data of the cross-operation space area to which the target crane belongs, clarifying the three-dimensional coordinate boundaries of the cross area, the current spatial position of other entities within the area, and their future movement trajectories; then analyzing the current spatial position coordinates of the boom end of the target crane, its three-dimensional attitude angle, and the position of the center of gravity of the suspended load, and combining this with the conflict point positions of its future movement trajectory to eliminate... All dangerous movement directions that may cause new collisions with entities are considered. For example, if there is the boom trajectory of another crane to the left of the target crane and the trajectory of a suspended object in front, then the left and front directions are dangerous and must be eliminated. In combination with the distribution of open space outside the intersection area, movement directions that do not conflict with other entity trajectories and can quickly escape the intersection risk area are selected. The direction must meet the requirements of minimum boom movement amplitude and minimum impact of suspended object swing. For example, if there is no entity on the right side of the intersection area and the boom movement on the right side of the crane is not structurally constrained, then the right side is determined to be a safe movement direction. Safe movement directions are calculated for each target crane in turn to generate clear avoidance direction instructions.

[0051] Step 6.4: Based on the avoidance direction command, and combining the spatial coordinates of the boom end, three-dimensional attitude angles, and the real-time spatial coordinates of the load's center of gravity, calculate the maximum safe movement speed of each target crane in the avoidance direction, and generate a reference speed command. Specifically, after generating the avoidance direction command, combine the spatial coordinates of the boom end, three-dimensional attitude angles, and the real-time spatial coordinates of the load's center of gravity to calculate its maximum safe movement speed in the avoidance direction; first, retrieve the crane's equipment performance parameters, including the maximum movement speed of the boom joints, the maximum extension speed of the telescopic boom, and the load's swing inertia parameters, to clarify the crane's... The physical movement limits are then analyzed; subsequently, the spatial margin in the avoidance direction is determined, i.e., the movement distance required to completely escape the intersection risk area from the current position. Combined with the current warning level, the allowable avoidance time is determined. For a Level 1 warning, escape from risk must be completed in the shortest possible time; for a Level 2 warning, avoidance can be completed within a relatively ample time. Simultaneously, the inertial swaying effect of the suspended load is considered to avoid excessive movement speed leading to increased swaying amplitude, potentially causing contact with other entities or exceeding safe limits. For example, if the suspended load is heavy, has strong swaying inertia, and the avoidance distance is short, the maximum safe movement speed must be reduced to prevent the suspended load from causing new risks due to inertial swinging. By comprehensively considering the equipment's limit avoidance time and spatial margin, as well as the inertial factors of the suspended load, the maximum safe movement speed of each target crane in the avoidance direction is calculated, generating a reference speed command.

[0052] Step 6.5 integrates the warning level, avoidance direction command, and reference speed command to generate a control command containing the warning level, avoidance direction, and reference speed. This command is then transmitted in real-time to the corresponding target crane via the communication network, triggering the audible and visual warning device at the crane end to implement warning and active avoidance control. Specifically, this includes: first, integrating the warning level, avoidance direction command, and reference speed command corresponding to each target crane, generating a control command package for each crane according to a unified data format. The command package includes the crane number, warning level identifier, avoidance direction parameters, reference speed parameters, and execution time requirements, ensuring the completeness and unambiguity of the command information; then, transmitting the control command package in real-time to the corresponding target crane via a stable wireless communication network. The communication network uses mountain signal enhancement technology, which can effectively overcome the signal instability caused by terrain obstruction, ensuring the transmission of the command. The system ensures the real-time and completeness of the issued commands. After receiving the control command packet, the target crane first analyzes the warning level in the command and triggers the corresponding audible and visual warning device. The first-level warning activates a high-frequency, rapid audible and visual alarm signal, accompanied by vibration alerts in the cab. The second-level warning activates a normal-frequency audible and visual alarm signal. The third and fourth-level warnings only activate suggestive audible and visual signals to ensure that the operator can quickly identify the urgency of the risk. The operator performs the avoidance operation based on the avoidance direction and reference speed in the command, combined with the real-time operating status of the crane. If the crane has an automatic control function, it can directly adjust the boom movement state automatically according to the command parameters to achieve active avoidance. During the avoidance process, the target crane transmits real-time motion status data back to the control center through feedback sensors to monitor the avoidance effect in real time. If a collision risk is still detected, the control command can be adjusted in a timely manner to form a closed-loop control.

[0053] In this embodiment of the invention, because the invention adopts the following technical means: receiving the final collision risk level and mapping it to the corresponding warning level according to preset rules, determining the set of target cranes to be avoided, analyzing path conflicts and clarifying avoidance priorities based on the future movement trajectories of the boom and the load, calculating the safe avoidance direction based on the priority and cross-operation area data, determining the maximum safe reference speed based on the spatial position data of the boom and the load, and finally integrating the warning level, avoidance direction and reference speed to generate control commands and send them to the target cranes in real time to trigger audible and visual warnings and active avoidance, the invention effectively overcomes the technical problems in the prior art, such as mismatch between warning level and collision risk, lack of priority distinction in avoidance operations, lack of precise avoidance direction and speed guidance, and disconnect between warning and avoidance control leading to chaotic avoidance operations and delayed response. This achieves the technical effects of realizing differentiated and precise warnings, ensuring orderly avoidance operations, and improving the timeliness and accuracy of avoidance control. Ultimately, it ensures that collision risks in multi-crane power repair operations in mountainous areas are dealt with quickly and effectively, significantly improving operational safety and collaborative management efficiency.

[0054] like Figure 2As shown, embodiments of the present invention also provide a collision avoidance and early warning control system for multi-machine hoisting power emergency repair in mountainous areas, including: The acquisition module is used to acquire raw real-time data collected by the positioning devices, attitude sensors and load status sensors mounted on each crane; construct data correlation relationships based on the data from multiple sources, analyze the inherent characteristics of the correlation relationships to generate dynamic correction coefficients; perform grid interpolation to complete the missing sampling points in the raw real-time data, and then fuse and correct the interpolated raw real-time data through the dynamic correction coefficients to obtain multi-source fused correction data; The fusion module is used to calculate the spatial position and attitude of the end of each crane boom based on multi-source fusion correction data, and to fuse the state data of the suspended object to calculate the real-time spatial position of the suspended object; The prediction module is used to establish the coupled motion relationship between the boom and the suspended object based on the real-time spatial location, and predict the motion trajectory of the boom and the suspended object within a set time period in the future based on the coupled motion relationship. The identification module is used to dynamically identify and update the overlapping work space areas within the working range of each crane based on real-time spatial location and movement trajectory; The calculation module is used to calculate the real-time distances and relative motion trends between booms, between booms and loads, and between loads based on the cross-operation space area and the real-time spatial position and motion trajectory of each boom and load within the cross-operation space area, so as to obtain the collision risk level. The processing module is used to generate and send control commands containing warning level, avoidance direction and reference speed to the corresponding crane according to the collision risk level, so as to implement warning and active avoidance control.

[0055] The collision avoidance warning control system according to embodiments of the present invention can correspond to the execution of the methods described in the embodiments of the present invention, and the above and other operations and / or functions of each module of the collision avoidance warning control system are respectively for implementing Figure 1 The corresponding process of the method in the illustrated embodiment will not be described in detail here for the sake of brevity.

[0056] This application also provides a computing device. This computing device can utilize a server.

[0057] like Figure 3 As shown in the figure, this is a schematic diagram of a computing device provided in an embodiment of this application. The computing device 700 includes a bus 701, a processor 702, a communication interface 703, and a memory 704. The processor 702, the memory 704, and the communication interface 703 communicate with each other via the bus 701.

[0058] The 701 bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0059] The processor 702 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0060] The communication interface 703 is used for external communication. The memory 704 may include volatile memory, such as random access memory (RAM). The memory 704 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD). The memory 704 stores executable code, which the processor 702 executes to perform the aforementioned collision avoidance and early warning control method for multi-machine hoisting power repair in mountainous areas.

[0061] Specifically, in implementing the mountain multi-machine hoisting power emergency repair collision prevention and early warning control system described in the above embodiments, and where each module or unit of the mountain multi-machine hoisting power emergency repair collision prevention and early warning control system described in the above embodiments is implemented by software, the software or program code required to execute the functions of each module / unit in the mountain multi-machine hoisting power emergency repair collision prevention and early warning control system described in the above embodiments can be partially or entirely stored in the memory 704. The processor 702 executes the program code corresponding to each unit stored in the memory 704 to execute the aforementioned mountain multi-machine hoisting power emergency repair collision prevention and early warning control method.

[0062] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computing device can store, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute the aforementioned anti-collision early warning control method for multi-machine hoisting power repair in mountainous areas.

[0063] This application also provides a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application are generated.

[0064] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0065] When the computer program product is executed by a computer, the computer executes any of the methods described in the aforementioned method for collision prevention and early warning control of multi-machine hoisting power repair in mountainous areas. The computer program product can be a software installation package; when any of the methods described in the aforementioned method for collision prevention and early warning control of multi-machine hoisting power repair in mountainous areas needs to be used, the computer program product can be downloaded and executed on the computer.

[0066] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for collision prevention and early warning control of multi-machine hoisting power emergency repair in mountainous areas, characterized in that, The method includes: The system acquires raw real-time data collected by positioning devices, attitude sensors, and load status sensors mounted on each crane; it constructs data correlation relationships based on data from multiple sources, analyzes the inherent characteristics of the correlation relationships to generate dynamic correction coefficients; it performs gridded interpolation to complete missing sampling points in the raw real-time data, and then fuses and corrects the interpolated raw real-time data using dynamic correction coefficients to obtain multi-source fused correction data. Based on multi-source fusion correction data, the spatial position and attitude of the end of each crane boom are calculated, and the state data of the suspended object are fused to calculate the real-time spatial position of the suspended object; Based on real-time spatial location, the coupled motion relationship between the boom and the suspended object is established, and the motion trajectory of the boom and the suspended object in a future set time period is predicted based on the coupled motion relationship. Based on real-time spatial location and movement trajectory, the overlapping work space areas within the working range of each crane are dynamically identified and updated; Based on the cross-operation space area, and the real-time spatial position and movement trajectory of each boom and load within the cross-operation space area, calculate the real-time distance and relative movement trend between booms, between booms and loads, and between loads to obtain the collision risk level; Based on the collision risk level, control commands containing the warning level, avoidance direction, and reference speed are generated and sent to the corresponding crane to implement warning and active avoidance control.

2. The method for collision prevention and early warning control of multi-machine hoisting power emergency repair in mountainous areas according to claim 1, characterized in that, Acquire raw real-time data collected by positioning devices, attitude sensors, and load status sensors mounted on each crane; construct data correlation relationships based on multi-source sensor data, analyze the inherent characteristics of the correlation relationships to generate dynamic correction coefficients; perform gridded interpolation to complete missing sampling points in the raw real-time data, and then fuse and correct the interpolated raw real-time data using dynamic correction coefficients to obtain multi-source fused correction data, including: Receive raw real-time data packets periodically reported by each crane; perform timestamp alignment on the raw real-time data packets to unify the time base of the data and obtain a time-aligned multi-source data sequence; Based on time-aligned multi-source data sequences, the physical and geometric correlations between data from different sensors are analyzed. By analyzing the inherent consistency characteristics of the correlations over a period of time, the deviations and noise patterns among the data from each sensor are identified, and dynamic correction coefficients for compensating for deviations and noise are obtained. To address missing sampling points caused by sampling anomalies in time alignment, data points from the same sensor are constructed into a discrete point set in the spatiotemporal domain. Based on the spatial and temporal neighborhood information of the valid data points in the discrete point set, the estimated values ​​of the missing sampling points are calculated and completed to obtain a complete data sequence. The complete data sequence is combined with dynamic correction coefficients to obtain multi-source fusion correction data.

3. The method for collision prevention and early warning control of multi-machine hoisting power emergency repair in mountainous areas according to claim 2, characterized in that, Based on multi-source fusion correction data, the spatial position and attitude of the end of each crane boom are calculated, and the state data of the suspended object are fused to calculate the real-time spatial position of the suspended object, including: Receive and integrate multi-source fusion correction data from various cranes, establish a global unified coordinate system based on the crane body positioning data, and construct a geometric motion relationship description of the boom based on the boom's structural parameters, joint connection relationships, and motion constraints. Based on the description of geometric motion relationships, and combined with the real-time angle data of each boom joint and the real-time length data of the telescopic boom in the multi-source fusion correction data, the precise spatial position coordinates of the end of each crane boom in the global unified coordinate system are obtained through geometric relationship derivation and calculation. Based on spatial location coordinates, real-time measurement data from the boom attitude sensor in the multi-source fusion correction data are fused, and the three-dimensional attitude angle of the boom end in the global unified coordinate system is calculated through geometric relationship transformation and compensation processing. Based on the spatial position coordinates and three-dimensional attitude angles of the boom end, the real-time length of the hoisting rope, the real-time swing angle and swing angular velocity data of the hoisting object collected by the hoisting object state sensor in the multi-source fusion correction data are integrated. Through spatial geometric relationship calculation, the real-time spatial position of the hoisting object's center of gravity in the global unified coordinate system is obtained.

4. The method for collision prevention and early warning control of multi-machine hoisting power emergency repair in mountainous areas according to claim 3, characterized in that, Based on real-time spatial location, a coupled motion relationship between the boom and the suspended load is established, and the motion trajectory of the boom and the suspended load within a set time period is predicted based on this coupled motion relationship, including: Receive real-time spatial position data of the boom end, three-dimensional attitude angle, and center of gravity of the suspended object, and calculate the real-time relative position vector and spatial connection relationship between the boom end and the center of gravity of the suspended object. Based on real-time relative position vectors and spatial connectivity, and combined with the boom joint motion velocity and load swing state data in multi-source fusion correction data, the characteristics of the transmission influence of boom motion on load swing are analyzed, and the dynamic coupling relationship between boom motion and load motion is established. Based on the dynamic coupling relationship, and combining the current operation command signals of each crane with historical motion trend data, the time series motion trajectory of the boom end in a future set period of time is predicted. Based on the time-series motion trajectory and dynamic coupling relationship of the boom end, the time-series motion trajectory of the center of gravity of the suspended object within a future set time period is calculated. The time-series motion trajectories of the boom end and the center of gravity of the suspended object are spatiotemporally aligned and smoothed to obtain continuous and stable future motion trajectories of the boom and the suspended object.

5. The method for collision prevention and early warning control of multi-machine hoisting power emergency repair in mountainous areas according to claim 4, characterized in that, Based on real-time spatial location and movement trajectory, the system dynamically identifies and updates overlapping workspace areas within the operating range of each crane, including: Receive continuous and stable motion trajectory data of the boom end and the center of gravity of the suspended object, and combine the spatial position coordinates of the boom end, the three-dimensional attitude angle and the real-time spatial position coordinates of the center of gravity of the suspended object to obtain the position distribution and motion state description of the boom entity and the suspended object entity in three-dimensional space. Based on the positional distribution and motion state description of the boom entity and the suspended object entity in three-dimensional space, the three-dimensional working space envelope of each crane at the current moment is calculated according to the boom length, suspended object size and preset safety distance parameters of each crane. Based on the three-dimensional operating space envelope of each crane, the spatial overlap region between any two cranes' three-dimensional operating space envelopes is identified through spatial geometric intersection operations, thus obtaining the initial set of cross-operating space regions; Based on the initial set of overlapping work space regions, and combined with the future motion trajectory data of the boom and the load, the evolution trend of the overlapping work space regions in the time dimension is predicted. The boundaries of the spatially overlapping regions are dynamically expanded and updated in time to obtain the dynamically changing overlapping work space regions. Based on the dynamic cross-operation space area, the area is divided and prioritized according to the spatial coordinate range, time duration and number of cranes involved, to obtain structured cross-operation space area data.

6. The method for collision prevention and early warning control of multi-machine hoisting power emergency repair in mountainous areas according to claim 5, characterized in that, Based on the overlapping work space area, and the real-time spatial position and trajectory of each boom and load within the overlapping work space area, the real-time distances and relative motion trends between booms, between booms and loads, and between loads are calculated to obtain the collision risk level, including: Receive structured cross-operation space area data, and combine the spatial position coordinates of the boom end, three-dimensional attitude angle and real-time spatial position coordinates of the center of gravity of the load to extract the current spatial position information of all boom entities and load entities located in the cross-operation space area; Based on the current spatial location information, combined with the continuous and stable motion trajectory data of the boom end and the center of gravity of the suspended object, the real-time spatial distances between each boom entity, between each boom entity and the suspended object entity, and between each suspended object entity are calculated to form a multi-dimensional distance matrix; Based on a multi-dimensional distance matrix and combined with the motion trajectory data of the boom and the load, the relative motion velocity vector and relative motion direction between each entity pair are calculated, and the approach and separation trends between entities are analyzed. Based on relative motion trends and real-time spatial distances, and according to preset safety distance thresholds and dynamic risk assessment rules, the collision probability between booms, between booms and suspended objects, and between suspended objects is graded and assessed to obtain a preliminary collision risk index. Based on the preliminary collision risk index, and combined with the priority ranking of the cross-operation space areas and the risk duration factor, the risk indices are weighted, integrated, and accumulated over time to obtain the final collision risk level.

7. The method for collision prevention and early warning control of multi-machine hoisting power emergency repair in mountainous areas according to claim 6, characterized in that, Based on the collision risk level, control commands containing the warning level, avoidance direction, and reference speed are generated and issued to the corresponding crane to implement warning and active avoidance control, including: Receive the final collision risk level, convert the collision risk level into the corresponding warning level according to the preset risk level mapping rules, and determine the set of target cranes that need to perform avoidance operations; Based on the set of target cranes and combined with the future movement trajectory data of the boom and the load, the movement path conflict of each target crane in the cross-operation space area is analyzed to determine the avoidance priority order of each target crane; Based on the priority order of avoidance and combined with the structured cross-operation space area data, the safe movement direction of each target crane during the avoidance process is calculated, and the avoidance direction command is generated. Based on the avoidance direction command, combined with the spatial position coordinates of the boom end, three-dimensional attitude angles and the real-time spatial position coordinates of the center of gravity of the suspended object, the maximum safe movement speed of each target crane in the avoidance direction is calculated, and a reference speed command is generated. The warning level, avoidance direction command, and reference speed command are integrated to generate a control command that includes the warning level, avoidance direction, and reference speed. This command is then sent to the corresponding target crane in real time via the communication network, triggering the audible and visual warning device at the crane end to implement warning and active avoidance control.

8. A collision prevention and early warning control system for multi-machine hoisting power emergency repair in mountainous areas, wherein the system implements the method as described in any one of claims 1 to 7, characterized in that, include: The acquisition module is used to acquire raw real-time data collected by the positioning devices, attitude sensors and load status sensors mounted on each crane; construct data correlation relationships based on the data from multiple sources, analyze the inherent characteristics of the correlation relationships to generate dynamic correction coefficients; perform grid interpolation to complete the missing sampling points in the raw real-time data, and then fuse and correct the interpolated raw real-time data through the dynamic correction coefficients to obtain multi-source fused correction data; The fusion module is used to calculate the spatial position and attitude of the end of each crane boom based on multi-source fusion correction data, and to fuse the state data of the suspended object to calculate the real-time spatial position of the suspended object; The prediction module is used to establish the coupled motion relationship between the boom and the suspended object based on the real-time spatial location, and predict the motion trajectory of the boom and the suspended object within a set time period in the future based on the coupled motion relationship. The identification module is used to dynamically identify and update the overlapping work space areas within the working range of each crane based on real-time spatial location and movement trajectory; The calculation module is used to calculate the real-time distances and relative motion trends between booms, between booms and loads, and between loads based on the cross-operation space area and the real-time spatial position and motion trajectory of each boom and load within the cross-operation space area, so as to obtain the collision risk level. The processing module is used to generate and send control commands containing warning level, avoidance direction and reference speed to the corresponding crane according to the collision risk level, so as to implement warning and active avoidance control.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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