A tower machine sensing area redundant overlap optimization method and system

Abstract

The application relates to the technical field of intelligent monitoring of engineering machinery, and provides a tower crane sensing area redundancy overlap optimization method and system. The method performs system initialization on a double-radar module of the tower crane, obtains initial installation positions, rotation angle ranges and sensing distance parameters of the double-radar module, and constructs a radar field of view model of each of the double-radar module. When the tower crane performs hoisting operation, hoisting path planning information is obtained, the overlap degree of the sensing area corresponding to the double-radar module and each area on the hoisting path is dynamically calculated, the key area in the hoisting operation is determined and set as a high-weight redundancy protection area, a preset high sensing weight is allocated to the high-weight redundancy protection area, and rotation angle distribution of the double-radar module is optimized. In the hoisting operation process, the working state of the double-radar module is detected in real time, and the continuity and safety of the hoisting operation are ensured. The method can adapt to changes of high-risk areas in different hoisting scenes.

Description

Technical Field

[0001] This application relates to the field of intelligent monitoring technology for construction machinery, and in particular to a method and system for optimizing redundancy overlap in the sensing area of ​​a tower crane. Background Technology

[0002] In tower crane lifting operations, reliable sensing of critical areas (such as the vertical passageway of the hook and areas prone to obstacles) is a core requirement for ensuring operational safety. Traditional tower crane sensing systems typically employ a single radar or multiple radars operating independently at a fixed angle, which has the following technical shortcomings:

[0003] 1. Inefficient allocation of sensing resources: Existing solutions lack differentiated weight design for key areas, and cannot prioritize overlapping monitoring of high-risk areas when sensor resources are limited, resulting in monitoring blind spots or insufficient redundancy in some key areas.

[0004] 2. Insufficient dynamic adaptability: The hoisting path planning does not take into account the real-time overlap of the radar sensing area for angle optimization, and the radar field of view scheduling relies on preset rules, making it difficult to cope with changes in sensing requirements under complex working conditions.

[0005] 3. Weak fault tolerance: When the radar module is blocked, damaged, or experiences abnormal echoes, there is a lack of an automatic sensing task takeover mechanism, which can easily lead to monitoring interruptions in critical areas and pose a risk of collisions.

[0006] Therefore, a method is urgently needed to solve at least one of the above problems. Summary of the Invention

[0007] This application provides a method and system for optimizing redundancy overlap in the sensing area of ​​tower cranes, aiming to solve the problems of traditional tower crane sensing systems, which typically adopt a fixed-angle single-radar or multi-radar independent monitoring mode, resulting in extensive allocation of sensing resources, insufficient dynamic adaptability, and weak fault tolerance.

[0008] In a first aspect, embodiments of this application provide a method for optimizing redundancy overlap in the sensing area of ​​a tower crane; the method includes:

[0009] The system initializes the dual radar modules of the tower crane, obtains the initial installation position, rotation angle range and sensing distance parameters of the dual radar modules, and constructs the radar field of view model of each dual radar module. The radar field of view model is used to characterize the sensing area of ​​the radar at different rotation angles.

[0010] When the tower crane is carrying out hoisting operations, the hoisting path planning information is obtained. Based on the radar field of view model, the degree of overlap between the sensing area corresponding to the dual radar modules and each area on the hoisting path is dynamically calculated according to the current rotation angle of the dual radar modules.

[0011] Key areas in the hoisting operation are identified and set as high-weight redundant protection zones. The key areas include the vertical passage of the hook and areas with high incidence of obstacles. Preset high perception weights are assigned to the high-weight redundant protection zones. The rotation angle allocation of the dual radar modules is optimized according to the degree of overlap and the high perception weights, so that the perception areas of the dual radar modules overlap and monitor the high-weight redundant protection zones.

[0012] During the hoisting operation, the working status of the dual radar modules is monitored in real time. When an abnormality is detected in either radar module, the perception area of ​​the abnormal radar module is determined, and the overlapping radar field of view of the other normally functioning radar module is reassigned to the critical area to take over the perception task of the abnormal radar module in the critical area, ensuring the continuity and safety of the hoisting operation.

[0013] In some embodiments, constructing the radar field-of-view model for each of the two radar modules includes: establishing a global coordinate system with the tower crane's slewing center as the origin based on the initial installation position coordinates of the two radar modules; for each radar module, dividing the rotation angle range into discrete angle nodes according to a preset angle resolution; for each angle node, generating a fan-shaped detection area in the global coordinate system based on the sensing distance parameter, wherein the center of the fan-shaped detection area is the radar module installation position, the radius is the sensing distance, and the central angle is the radar beam angle; and storing the fan-shaped detection areas corresponding to all angle nodes to form a radar field-of-view model database containing angle and area mapping relationships.

[0014] In some embodiments, the step of dynamically calculating the overlap degree between the sensing area corresponding to the dual radar modules and each area on the hoisting path based on the radar field-of-view model and the current rotation angle of the dual radar modules includes: dividing the hoisting path into path segment regions at preset intervals; extracting the polygon coordinate set of each path segment region in the global coordinate system; retrieving the corresponding fan-shaped detection area polygon from the radar field-of-view model database according to the current rotation angle of the dual radar modules; using a spatial geometric overlap algorithm to calculate the ratio of the overlap area of ​​the path segment region polygon and the fan-shaped detection area polygon to the total area of ​​the path segment region, as the quantized value of the overlap degree; and storing the quantized values ​​of the overlap degree of each dual radar module after normalization.

[0015] In some embodiments, determining the key areas in the hoisting operation and setting them as high-weight redundancy protection zones includes: extracting the vertical motion trajectory of the hook based on hoisting path planning information, generating a cylindrical space centered on the trajectory and with a preset safety radius as the vertical channel of the hook; identifying areas where the frequency of obstacles during hoisting exceeds a preset threshold through statistical analysis of historical operation data, as high-incidence obstacle areas; fusing the spatial coordinate ranges of the vertical channel of the hook and the high-incidence obstacle areas to generate a set of key areas containing three-dimensional spatial coordinates, and marking each key area with a unique identifier.

[0016] In some embodiments, allocating a preset high perception weight to a high-weight redundant protection zone includes: establishing a weight allocation rule base, wherein the perception weight of the hook vertical channel is preset to the highest level weight value, and the perception weight of the obstacle-prone area is dynamically assigned according to the grading results corresponding to the historical obstacle occurrence frequency; for each key area, a weight configuration table containing area identifiers, weight values, and monitoring priorities is generated according to its spatial coordinate range and the weight allocation rule base, and the weight configuration table is used to guide the perception resource scheduling of the dual radar modules.

[0017] In some embodiments, the method further includes: collecting obstacle detection data, radar module scheduling records, and lifting operation risk event data for each key area in historical lifting operations to construct a historical dataset; extracting features from the historical dataset to obtain feature vectors containing key area spatial coordinates, obstacle occurrence frequency, radar sensing angle allocation scheme, and risk event type; training the feature vectors using a machine learning algorithm to establish a dynamic adjustment model for key area sensing weights, wherein the dynamic adjustment model for key area sensing weights is used to predict the optimal sensing weights for each key area based on real-time operating environment parameters; periodically inputting wind speed, visibility, and lifting load parameters in the real-time operating environment into the dynamic adjustment model for key area sensing weights to generate a predicted sensing weight adjustment strategy; and updating the preset sensing weights in the weight allocation rule base according to the sensing weight adjustment strategy to adapt to the monitoring needs of high-weight redundant protection zones under different operating environments.

[0018] In some embodiments, optimizing the rotation angle allocation of the dual radar modules based on the degree of overlap and high perception weight, so that the perception areas of the dual radar modules form overlapping monitoring of the high-weight redundant protection zones, includes: constructing a multi-objective optimization model containing the rotation angle range of the dual radar modules, the weight values ​​of key areas, and the quantified value of the degree of overlap, with the objective function being to maximize the weighted sum of the overlapping areas of the key areas; solving the multi-objective optimization model using a particle swarm optimization algorithm or a genetic algorithm, and generating the optimal combination of rotation angles under the condition of satisfying the mechanical rotation constraints of the dual radar modules; determining whether each key area under the optimal combination of rotation angles is simultaneously covered by the perception area of ​​the dual radar modules, and if not covered, adjusting the weight coefficients of the objective function and resolving until all high-weight redundant protection zones form overlapping monitoring.

[0019] In some embodiments, the real-time detection of the working status of the dual radar modules during the hoisting operation includes: sending a status query command to the dual radar modules through a preset detection cycle to obtain status feedback data including signal strength, data refresh rate, and error check code; performing real-time analysis on the status feedback data; and generating a corresponding abnormal warning signal when the signal strength is lower than a preset threshold, the data refresh rate fluctuation exceeds the allowable range, or the number of consecutive error check failures reaches a warning value. The abnormal warning signal includes the radar module identifier, the abnormality type, and the occurrence time.

[0020] In some embodiments, when an anomaly is detected in any radar module, determining the sensing area of ​​the anomaly radar module and rescheduling the overlapping radar field of view of another normally functioning radar module to the critical area to take over the sensing task of the anomaly radar module in the critical area includes: retrieving the fan-shaped detection area corresponding to the anomaly radar module before its failure from the radar field of view model database as the failure sensing area based on the current rotation angle of the anomaly radar module; for the normally functioning radar module, calculating the adjustable field of view range that can cover the critical area but is not covered by the failure sensing area within its remaining rotation angle range using a geometric transformation algorithm; preferentially selecting the rotation angle with the largest overlap area with the critical area within the adjustable field of view range as the scheduling angle, generating a control command containing the scheduling angle and scheduling time and sending it to the normally functioning radar module to adjust the rotation angle to cover the critical area.

[0021] Secondly, this application provides a tower crane sensing area redundancy overlap optimization system, applied to a controller, the system comprising:

[0022] The model building unit is used to initialize the dual radar modules of the tower crane, obtain the initial installation position, rotation angle range and sensing distance parameters of the dual radar modules, and build the radar field of view model of each dual radar module. The radar field of view model is used to characterize the sensing area of ​​the radar at different rotation angles.

[0023] The information acquisition unit is used to acquire hoisting path planning information when the tower crane is carrying out hoisting operations. Based on the radar field of view model, it dynamically calculates the degree of overlap between the sensing area corresponding to the dual radar modules and each area on the hoisting path according to the current rotation angle of the dual radar modules.

[0024] The overlapping monitoring unit is used to identify key areas in the hoisting operation and set them as high-weight redundant protection zones. The key areas include the vertical passage of the hook and the obstacle-prone area. A preset high perception weight is assigned to the high-weight redundant protection zone. The rotation angle allocation of the dual radar modules is optimized according to the degree of overlap and the high perception weight, so that the perception area of ​​the dual radar modules forms overlapping monitoring of the high-weight redundant protection zone.

[0025] The abnormal takeover unit is used to monitor the working status of the dual radar modules in real time during hoisting operations. When an abnormality is detected in either radar module, the sensing area of ​​the abnormal radar module is determined, and the overlapping radar field of view of the other normally functioning radar module is reassigned to the critical area to take over the sensing task of the abnormal radar module in the critical area, ensuring the continuity and safety of the hoisting operation.

[0026] This application provides a method and system for optimizing redundancy and overlap in the perception area of ​​a tower crane. By setting up a high-weight redundancy protection zone and monitoring with dual radar overlap, it ensures that core areas such as the vertical passage of the hook and high-obstacle areas are always under dual perception coverage, improving the accuracy and reliability of obstacle detection. Based on the degree of overlap and weight priority, the radar perception angle is dynamically allocated to maximize the monitoring efficiency of key areas under the premise of a fixed number of sensors, solving the contradiction of "blind redundancy" or "insufficient monitoring" in traditional solutions. By monitoring the working status of the radar in real time, the overlapping field of view of the normal radar is automatically dispatched to take over the perception task of key areas when abnormalities occur, avoiding monitoring interruptions caused by single sensor failure and ensuring the continuity and safety of hoisting operations. Combined with hoisting path planning, the perception strategy is dynamically adjusted to adapt to changes in high-risk areas under different hoisting scenarios, improving the system's intelligence level.

[0027] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1This is a schematic flowchart illustrating the steps of a method for optimizing redundancy overlap in the sensing area of ​​a tower crane, provided in an embodiment of this application.

[0030] Figure 2 This is a schematic diagram of the structure of a dual radar module provided in one embodiment of this application;

[0031] Figure 3 This is a schematic block diagram of a tower crane sensing area redundancy overlap optimization system provided in one embodiment of this application;

[0032] Figure 4 This is a schematic block diagram of the controller provided in one embodiment of this application.

[0033] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0034] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0035] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0036] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0037] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0038] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0039] In tower crane lifting operations, reliable sensing of critical areas (such as the vertical passageway of the hook and areas prone to obstacles) is a core requirement for ensuring operational safety. Traditional tower crane sensing systems typically employ a single radar or multiple radars operating independently at a fixed angle, which has the following technical shortcomings:

[0040] 1. Inefficient allocation of sensing resources: Existing solutions lack differentiated weight design for key areas, and cannot prioritize overlapping monitoring of high-risk areas when sensor resources are limited, resulting in monitoring blind spots or insufficient redundancy in some key areas.

[0041] 2. Insufficient dynamic adaptability: The hoisting path planning does not take into account the real-time overlap of the radar sensing area for angle optimization, and the radar field of view scheduling relies on preset rules, making it difficult to cope with changes in sensing requirements under complex working conditions.

[0042] 3. Weak fault tolerance: When the radar module is blocked, damaged, or the echo is abnormal, there is a lack of an automatic sensing task takeover mechanism, which can easily lead to the interruption of monitoring in critical areas and pose a risk of collision.

[0043] While existing technologies include monitoring schemes involving multi-sensor collaboration, none have developed a complete technical solution for the weighting and prioritization of critical areas of tower cranes, dynamic overlap calculation, and intelligent scheduling during faults. This invention fills the technical gap in "reliable perception and fault tolerance of critical areas in scenarios with limited sensor resources" by constructing a dual-radar field-of-view model, a high-weight redundancy protection strategy for critical areas, and an anomaly perception task takeover mechanism.

[0044] Please refer to Figure 1 This application provides a method for optimizing redundancy overlap in the sensing area of ​​a tower crane, applied to a controller. It should also be noted that all information involved in the method provided in this application is extracted with the authorization of the relevant user and in accordance with relevant regulations, and will not infringe on user privacy.

[0045] The provided method for optimizing redundancy overlap in the tower crane sensing area includes steps S101 to S104. Details are as follows:

[0046] Step S101. Initialize the dual radar modules of the tower crane, obtain the initial installation position, rotation angle range and sensing distance parameters of the dual radar modules, and construct the radar field of view model of each dual radar module. The radar field of view model is used to characterize the sensing area of ​​the radar at different rotation angles.

[0047] Specifically, for the dual radar modules (such as those mounted on tower cranes) Figure 2As shown, it can include a lidar installed under the vehicle, such as the TF-ALS-LIDAR-01 radar, with a single radar detection range of ≥150 meters, accuracy ≤3 cm, and support for angular resolution ≤0.05°. The parameters are initialized by dual radars scanning synchronously at a frequency of 10 Hz, and a perception area model is constructed under different rotation angles to provide a geometric basis for subsequent dynamic scheduling.

[0048] Parameter acquisition involves reading the physical installation parameters of the dual radar modules through the controller, including: initial installation position: the radar coordinates in the tower crane coordinate system (e.g., the tower top radar coordinates are (x1, y1, z1), and the jib radar coordinates are (x2, y2, z2)); rotation angle range: the horizontal angle range (e.g., 0 ≤ θ ≤ 270°) and pitch angle range (if supported); sensing distance parameters: the maximum effective detection range Dmax and the minimum detection range Dmin of the radar. A global coordinate system for the tower crane is established, with the tower crane's rotation center as the origin, the jib extension direction as the X-axis, and the vertical upward direction as the Z-axis.

[0049] The radar field of view model is constructed by defining the field of view of each radar module as a sector-shaped area (represented in polar coordinates) with the installation position as the vertex, the rotation angle as the sector angle, and the sensing distance as the radius. The mathematical expression is: Field of view area = {(r,θ)|Dmin≤r≤Dmax,θ∈[θmin,θmax]}; where θ is the real-time rotation angle, and [θmin,θmax] is the angle adjustment range of the radar.

[0050] By using three-dimensional spatial projection, the fan-shaped field of view is converted into a two-dimensional or three-dimensional perception area grid of the tower crane's operating area (such as the ground, under the boom, and the hook path) for subsequent overlapping calculations.

[0051] Step S102. When the tower crane is performing hoisting operations, obtain hoisting path planning information. Based on the radar field of view model, dynamically calculate the degree of overlap between the sensing area corresponding to the dual radar modules and each area on the hoisting path according to the current rotation angle of the dual radar modules.

[0052] Specifically, by combining the path planning information of the hoisting operation (such as the movement trajectory of the hook and the material transportation route), the geometric overlap between the current field of view of the dual radars and each area on the hoisting path (such as the starting point, the ending point, and the critical path segment) is calculated in real time, and the coverage effect of the sensing resources on the path is quantified.

[0053] The hoisting path planning information is obtained by acquiring the hoisting task path data from the tower crane control system or host computer, including: the vertical channel of the hook (i.e., the vertical space column through which the hook is raised and lowered, with a radius equal to the safe distance R hook and a height covering the tower crane's operating range); the horizontal path of material transportation (such as the horizontal movement trajectory from the storage yard to a certain floor of a building, defined as a polygon or a set of line segments); and the obstacle-prone area (an area preset based on historical accident data or the site environment, such as the area where surrounding buildings and overhead cables are located).

[0054] The overlap degree is calculated by geometrically intersecting the current field of view area of ​​the two radars (the fan-shaped grid constructed in step S101) with the hoisting path area, and the following indicators are used to quantify the overlap degree: Area overlap rate: the intersection area of ​​the sensing area and the path area / the total area of ​​the path area; Key node coverage: whether key positions in the path (such as the current position of the hook, the coordinates of obstacles) are included in the radar field of view; For three-dimensional scenes, the overlap degree can be converted into volume overlap rate or the inclusion judgment of the three-dimensional coordinates of key points. The result is output as a matrix C=[ci,j], where ci,j represents the overlap degree of the i-th radar in the j-th path area (0-1 normalized value).

[0055] Step S103. Determine the key areas in the hoisting operation and set them as high-weight redundant protection zones. The key areas include the vertical passage of the hook and the obstacle-prone area. Assign a preset high perception weight to the high-weight redundant protection zone. Optimize the rotation angle allocation of the dual radar modules according to the degree of overlap and the high perception weight, so that the perception areas of the dual radar modules overlap and monitor the high-weight redundant protection zone.

[0056] Specifically, by defining the vertical channel of the hook and the obstacle-prone area as high-weight redundancy protection zones, and by dynamically adjusting the rotation angle of the two radars, the field of view of the two radars overlaps and covers the key areas, thereby improving the perception accuracy and reliability.

[0057] The definition and weight settings for key areas include: Hook vertical channel: a cylinder with radius R1 centered on the hook wire rope, covering the maximum lifting height of the tower crane, with a weight set to the highest priority W hook = 1.0; Obstacle high-incidence area: a polygonal area (such as surrounding buildings or fixed obstacle locations) defined based on site surveys or historical data, with a weight set to Wobstacle = 0.8; Non-critical areas (such as open ground) with a weight set to Wnormal = 0.5. The rotation angle optimization algorithm includes: establishing an objective function to maximize the sum of the overlap degree of key areas and the weight product, while constraining the radar angle within the physically rotatable range. Gradient descent, genetic algorithms, or rule engines (such as priority-based angle compensation strategies) are used to solve for the optimal angle, ensuring that the dual radar views overlap in the key area (i.e., overlap rate > 50%). Example: When the hook vertical channel is located on the right side of the boom, the first radar is controlled to deflect 30 degrees to the right, and the second radar deflects 15 degrees to the right, forming cross-coverage of the channel by both radars.

[0058] Step S104. During the hoisting operation, the working status of the dual radar modules is monitored in real time. When an abnormality is detected in either radar module, the perception area of ​​the abnormal radar module is determined, and the overlapping radar field of view of the other normally functioning radar module is dispatched to the critical area to take over the perception task of the abnormal radar module in the critical area, so as to ensure the continuity and safety of the hoisting operation.

[0059] Specifically, by monitoring the working status of the two radars in real time, when one radar malfunctions, the overlapping field of view of the other radar takes over the perception task of the key area, thus enabling the normally operating radar to take over the monitoring of the key area.

[0060] Real-time monitoring of the working status employs a heartbeat mechanism or data verification method to detect radar anomalies: Heartbeat mechanism: periodically (e.g., every 100ms) sends a query command to the radar; if there is no response, it is determined to be a fault; Data verification: checks the rationality of radar echo data (e.g., distance jumps exceeding thresholds, abnormal point cloud density) to determine whether there is a hardware fault or signal interference.

[0061] The fault scheduling strategy calculates the overlap between the current field of view of the second radar and the critical area originally covered by the first radar when the first radar malfunctions. By adjusting the rotation angle of the second radar, its field of view is extended to cover the critical area of ​​the malfunctioning radar (using the overlap design of the initial field of view of the two radars, reserving at least 30% overlap). For example, if the first radar malfunctions and the area on the right side of the vertical channel of the hook originally covered by it is not covered, the second radar is controlled to deflect an additional 20 degrees to the right, using its remaining angle range to take over the area.

[0062] In some embodiments, constructing the radar field-of-view model for each of the two radar modules includes: establishing a global coordinate system with the tower crane's slewing center as the origin based on the initial installation position coordinates of the two radar modules; for each radar module, dividing the rotation angle range into discrete angle nodes according to a preset angle resolution; for each angle node, generating a fan-shaped detection area in the global coordinate system based on the sensing distance parameter, wherein the center of the fan-shaped detection area is the radar module installation position, the radius is the sensing distance, and the central angle is the radar beam angle; and storing the fan-shaped detection areas corresponding to all angle nodes to form a radar field-of-view model database containing angle and area mapping relationships.

[0063] By discretizing the radar rotation angle, a field-of-view model database containing angle-region mapping relationships is constructed, providing a geometric basis for dynamic sensing area calculation. A global coordinate system is established with the tower crane's rotation center as the origin O(0,0,0), defining the X-axis along the initial direction of the crane boom (e.g., due east), the Y-axis perpendicular to the X-axis (in the horizontal plane), and the Z-axis vertically upward, forming a three-dimensional global coordinate system. The installation position coordinates of the two radar modules are read: for example, radar A is installed at the top of the tower (xA,yA,zA), and radar B is installed at the end of the crane boom (xB,yB,zB).

[0064] Discrete angle node division divides the radar rotation angle range (e.g., 0°-270°) into discrete nodes θ1, θ2, ..., θn according to a preset angular resolution (e.g., 1° or 5°). For each node θi, a radar beam angle (e.g., a horizontal beam angle of 60°) is defined, forming a fan-shaped detection area with the installation position as the center, the sensing distance Dmax as the radius, and the central angle as the beam angle.

[0065] The sector region coordinates are generated by calculating the boundary coordinates of the sector region in the global coordinate system: starting angle: θi−β / 2, ending angle: θi+β / 2 (β is the beam angle); sector boundary point coordinates: (x+Dcosθ,y+Dsinθ,z), where D∈[Dmin,Dmax]. Convert the sector area into a polygonal mesh (such as a triangular mesh or a convex polygon) to facilitate subsequent overlapping calculations.

[0066] The vision model database is stored by establishing a key-value pair database, where the key is the rotation angle θi and the value is the set of polygon coordinates of the corresponding sector region, forming an "angle-region" mapping table that supports fast querying.

[0067] In some embodiments, the step of dynamically calculating the overlap degree between the sensing area corresponding to the dual radar modules and each area on the hoisting path based on the radar field-of-view model and the current rotation angle of the dual radar modules includes: dividing the hoisting path into path segment regions at preset intervals; extracting the polygon coordinate set of each path segment region in the global coordinate system; retrieving the corresponding fan-shaped detection area polygon from the radar field-of-view model database according to the current rotation angle of the dual radar modules; using a spatial geometric overlap algorithm to calculate the ratio of the overlap area of ​​the path segment region polygon and the fan-shaped detection area polygon to the total area of ​​the path segment region, as the quantized value of the overlap degree; and storing the quantized values ​​of the overlap degree of each dual radar module after normalization.

[0068] By discretizing the hoisting path into segmented regions, the overlapping area ratio between the radar field of view and the path region is calculated using a spatial geometric algorithm to quantify the sensing coverage effect.

[0069] The path segmentation and polygon modeling divides the hoisting path (such as the hook movement trajectory) into segmented regions at preset intervals (e.g., 1 meter). Each segment is represented as a line segment or polygon in three-dimensional space (projected onto a horizontal plane as a two-dimensional polygon). The vertex coordinate set of the segmented region is extracted. For example, a horizontal path segment is represented as {(x1,y1),(x2,y2),…,(xk,yk)}.

[0070] The radar field of view is retrieved based on the current rotation angles θA and θB of the two radars, and the corresponding sector polygons SA and SB are retrieved from the database provided in the above embodiment. The overlap area is calculated using a spatial overlap algorithm (such as polygon overlap calculation in the Shapely library for 2D scenes, converted to bounding box hierarchical traversal in 3D scenes), calculating the intersection area Ainter of the path segment region polygon P and the radar sector region S, and the total area AP of P. The overlap degree quantization value C = Ainter / AP (range 0-1).

[0071] Normalization is performed by normalizing the overlapping values ​​of the two radars (e.g., linearly mapping to 0-1) and storing them as a matrix C=[CA,CB] for subsequent weight allocation calculations.

[0072] In some embodiments, determining the key areas in the hoisting operation and setting them as high-weight redundancy protection zones includes: extracting the vertical motion trajectory of the hook based on hoisting path planning information, generating a cylindrical space centered on the trajectory and with a preset safety radius as the vertical channel of the hook; identifying areas where the frequency of obstacles during hoisting exceeds a preset threshold through statistical analysis of historical operation data, as high-incidence obstacle areas; fusing the spatial coordinate ranges of the vertical channel of the hook and the high-incidence obstacle areas to generate a set of key areas containing three-dimensional spatial coordinates, and marking each key area with a unique identifier.

[0073] By accurately defining the vertical channel of the hook and high-risk obstacle areas based on the hoisting trajectory and historical data, a set of key areas containing three-dimensional coordinates is generated.

[0074] The vertical channel of the hook is generated by extracting the vertical motion trajectory (i.e., the hook center coordinate sequence (xhook, yhook, z(t))) from the lifting path planning, using this trajectory as the central axis, and a preset safety radius R1 (e.g., 2 meters) to generate a cylindrical space. Its mathematical expression is: ,z∈[zmin,zmax]; where zmin and zmax are the lifting range of the hook. Obstacle high-incidence area identification is achieved by analyzing historical operation data (such as the location of obstacles in the past 100 lifting operations), statistically analyzing the frequency of obstacle occurrence in each area, and marking areas with a frequency exceeding a preset threshold (such as 30%) as obstacle high-incidence areas, which are represented by convex polygons or three-dimensional voxels.

[0075] Key region fusion and identification involves merging the coordinate ranges of the hook's vertical channel (cylinder) and obstacle-prone areas (polygons / voxels) to generate a set of key regions containing 3D coordinates (such as a coordinate list in JSON format). Each key region is assigned a unique identifier (such as a UUID) and associated with its spatial range and type (hook channel / obstacle area).

[0076] In some embodiments, allocating a preset high perception weight to a high-weight redundant protection zone includes: establishing a weight allocation rule base, wherein the perception weight of the hook vertical channel is preset to the highest level weight value, and the perception weight of the obstacle-prone area is dynamically assigned according to the grading results corresponding to the historical obstacle occurrence frequency; for each key area, a weight configuration table containing area identifiers, weight values, and monitoring priorities is generated according to its spatial coordinate range and the weight allocation rule base, and the weight configuration table is used to guide the perception resource scheduling of the dual radar modules.

[0077] By establishing dynamic weight allocation rules, key areas are assigned differentiated perceived priorities, and a weight configuration table is generated to guide resource scheduling.

[0078] The weight allocation rule base uses a preset weight of Whook=1.0 (highest level) for the vertical channel of the hook. Weights for high-obstacle areas are graded according to historical frequency: frequency ≥ 50%: Whigh=0.9; 30% ≤ frequency < 50%: Wmid=0.7; others are treated as non-critical areas (Wnormal=0.5). The weight configuration table is generated by matching weight values ​​and monitoring priorities (priority determines the resource allocation order during angle optimization) from the rule base for each critical area (the identified areas generated in Example 3) based on its type and historical frequency. The configuration table fields include: area ID, area type, 3D coordinate range, weight value W, and monitoring priority (levels 1-5, with 1 being the highest).

[0079] In some embodiments, the method further includes: collecting obstacle detection data, radar module scheduling records, and lifting operation risk event data for each key area in historical lifting operations to construct a historical dataset; extracting features from the historical dataset to obtain feature vectors containing key area spatial coordinates, obstacle occurrence frequency, radar sensing angle allocation scheme, and risk event type; training the feature vectors using a machine learning algorithm to establish a dynamic adjustment model for key area sensing weights, wherein the dynamic adjustment model for key area sensing weights is used to predict the optimal sensing weights for each key area based on real-time operating environment parameters; periodically inputting wind speed, visibility, and lifting load parameters in the real-time operating environment into the dynamic adjustment model for key area sensing weights to generate a predicted sensing weight adjustment strategy; and updating the preset sensing weights in the weight allocation rule base according to the sensing weight adjustment strategy to adapt to the monitoring needs of high-weight redundant protection zones under different operating environments.

[0080] Data from the tower crane management system over the past 12 months was collected, including: obstacle detection records (including detection time, obstacle type, and location coordinates) for key areas (such as hook vertical passages and high-obstacle areas), radar dispatch records (time, angle value, and triggering reason for each angle adjustment), and lifting risk event data (such as collision warnings and operation interruption events, time and location). After cleaning and structuring the data, a historical dataset was generated, with each record containing "key area ID, spatial coordinate range, obstacle occurrence frequency (monthly average), historical dispatch angle combinations, and risk event level (low / medium / high)," and stored in the database.

[0081] Feature extraction and model training include: extracting feature vectors: converting key area coordinates into 3D boundary values ​​in a global coordinate system, normalizing obstacle occurrence frequencies to a 0-1 range, converting scheduling angle combinations into radar A / B angle values, and encoding risk event types as 0-2 (0 = no risk, 1 = warning, 2 = accident). Machine learning algorithms such as random forests, gradient boosting trees, or neural networks are used, with "environmental parameters (wind speed, visibility, load) + historical features" as input and "optimal perception weights" as output, to train and dynamically adjust the model. During training, cross-validation is used to optimize model parameters, ensuring a negative correlation between predicted weights and the actual incidence rate of risk events (higher weights, lower risk).

[0082] The real-time weight adjustment strategy includes: real-time acquisition of operational environment parameters: current parameters (e.g., wind speed 10 m / s, visibility 50 meters, load 80% of rated load) are collected via wind speed sensors (accuracy ±0.5 m / s), visibility detectors (accuracy ±5 meters), and hoisting load sensors (accuracy ±1% of rated load). Real-time parameters are input into the trained model every 10 minutes, and the predicted weights for each key area are output (e.g., the weight of the vertical channel of the hook is adjusted from 10 to 12, and the weight of a high-obstacle area is adjusted from 8 to 9). Based on the prediction results, an adjustment strategy is generated, updating the preset weights in the weight allocation rule base, and triggering the reallocation of sensing resources (e.g., adjusting the radar angle to enhance the overlapping coverage of high-weight areas), ensuring that key areas receive higher priority monitoring resources in complex environments such as strong winds and low visibility.

[0083] In some embodiments, optimizing the rotation angle allocation of the dual radar modules based on the degree of overlap and high perception weight, so that the perception areas of the dual radar modules form overlapping monitoring of the high-weight redundant protection zones, includes: constructing a multi-objective optimization model containing the rotation angle range of the dual radar modules, the weight values ​​of key areas, and the quantified value of the degree of overlap, with the objective function being to maximize the weighted sum of the overlapping areas of the key areas; solving the multi-objective optimization model using a particle swarm optimization algorithm or a genetic algorithm, and generating the optimal combination of rotation angles under the condition of satisfying the mechanical rotation constraints of the dual radar modules; determining whether each key area under the optimal combination of rotation angles is simultaneously covered by the perception area of ​​the dual radar modules, and if not covered, adjusting the weight coefficients of the objective function and resolving until all high-weight redundant protection zones form overlapping monitoring.

[0084] By constructing a multi-objective optimization model and using intelligent algorithms to solve for the optimal angle combination, key areas are ensured to be covered by overlapping dual radars.

[0085] The construction of the multi-objective optimization model includes: Objective function: maximizing the weighted sum of overlapping areas of key regions.

[0086] ;

[0087] Where Wk is the weight of the key region k, , The degree of overlap between the two radars in region k. Constraints: radar rotation angle range θi∈[θmin,θmax], mechanical rotation speed limit (e.g., maximum rotation of 10° per second).

[0088] The Particle Swarm Optimization (PSO) algorithm can be used: Particles are positioned at (θA, θB), and the velocity update formula incorporates inertia weights and cognitive / social factors to iteratively find the global optimum. Alternatively, a genetic algorithm can be used: the encoding perspective is represented by genes, and the fitness function is the objective function value; evolution occurs through selection, crossover, and mutation operations.

[0089] Overlap coverage verification verifies whether each key area is simultaneously covered by the dual radar fields of view by combining the angles obtained from the solution (i.e., >0 and >0). If not covered, adjust the weight coefficient of that region in the objective function (e.g., increase by 10%), and iterate again until the overlap condition (overlap rate ≥ 30%) is met.

[0090] In some embodiments, the real-time detection of the working status of the dual radar modules during the hoisting operation includes: sending a status query command to the dual radar modules through a preset detection cycle to obtain status feedback data including signal strength, data refresh rate, and error check code; performing real-time analysis on the status feedback data; and generating a corresponding abnormal warning signal when the signal strength is lower than a preset threshold, the data refresh rate fluctuation exceeds the allowable range, or the number of consecutive error check failures reaches a warning value. The abnormal warning signal includes the radar module identifier, the abnormality type, and the occurrence time.

[0091] By periodically querying the status and verifying the data, radar anomalies can be identified in real time, and early warning signals containing detailed information can be generated.

[0092] The status query mechanism includes the controller sending status query commands (such as Modbus / TCP protocol packets) to the dual radars at a preset period (such as 200ms). The commands include radar ID and query fields (signal strength, data refresh rate, CRC check code).

[0093] The anomaly detection rules include: Signal strength: If it is lower than the preset threshold (e.g., -80dBm), it is judged as an abnormal signal attenuation; Data refresh rate: The normal range is 10±1Hz, and fluctuations exceeding 20% ​​are judged as communication anomalies; Checksum: If CRC checks fail 3 times consecutively, it is judged as a data transmission failure.

[0094] Anomaly warning generation includes: when an anomaly is detected, a warning signal is generated, containing: radar identifier (e.g., the serial number of radar A), anomaly type (signal / communication / transmission), occurrence timestamp, and actual value of the anomaly parameter (e.g., signal strength -85dBm). This is recorded both locally and remotely (e.g., sent to the tower crane monitoring platform).

[0095] In some embodiments, when an anomaly is detected in any radar module, determining the sensing area of ​​the anomaly radar module and rescheduling the overlapping radar field of view of another normally functioning radar module to the critical area to take over the sensing task of the anomaly radar module in the critical area includes: retrieving the fan-shaped detection area corresponding to the anomaly radar module before its failure from the radar field of view model database as the failure sensing area based on the current rotation angle of the anomaly radar module; for the normally functioning radar module, calculating the adjustable field of view range that can cover the critical area but is not covered by the failure sensing area within its remaining rotation angle range using a geometric transformation algorithm; preferentially selecting the rotation angle with the largest overlap area with the critical area within the adjustable field of view range as the scheduling angle, generating a control command containing the scheduling angle and scheduling time and sending it to the normally functioning radar module to adjust the rotation angle to cover the critical area.

[0096] By utilizing the remaining field of view of the normal radar, geometric calculations are used to take over the critical area perception tasks of the faulty radar, ensuring the continuity of monitoring.

[0097] The failure area is located by retrieving the corresponding sector area Sfail from the database provided in the above embodiment based on the rotation angle θfail of the last effective communication of the faulty radar, which is used as the failure detection area.

[0098] Adjustable field-of-view calculation includes: the remaining rotation range of the normal radar is [θmin, θmax]∖ the range occupied by the current angle. For the key region K, calculate the overlap area between the normal radar and K at different angles within the remaining range, and find the angle θbest that maximizes the overlap area.

[0099] ;

[0100] Geometric transformation algorithms (such as rotation matrix calculation of sector boundaries) are used to quickly evaluate the coverage effect at different angles.

[0101] The scheduling command generation generates control commands containing the scheduling angle θbest and execution time, which are then sent to the normal radar via a hardware interface (such as a CAN bus) to control its rotation to the target angle, ensuring that the angle adjustment is completed within 500ms.

[0102] This application provides a method and system for optimizing redundancy and overlap in the perception area of ​​a tower crane. By setting up a high-weight redundancy protection zone and monitoring with dual radar overlap, it ensures that core areas such as the vertical passage of the hook and high-obstacle areas are always under dual perception coverage, improving the accuracy and reliability of obstacle detection. Based on the degree of overlap and weight priority, the radar perception angle is dynamically allocated to maximize the monitoring efficiency of key areas under the premise of a fixed number of sensors, solving the contradiction of "blind redundancy" or "insufficient monitoring" in traditional solutions. By monitoring the working status of the radar in real time, the overlapping field of view of the normal radar is automatically dispatched to take over the perception task of key areas when abnormalities occur, avoiding monitoring interruptions caused by single sensor failure and ensuring the continuity and safety of hoisting operations. Combined with hoisting path planning, the perception strategy is dynamically adjusted to adapt to changes in high-risk areas under different hoisting scenarios, improving the system's intelligence level.

[0103] Please see Figure 3 As shown, Figure 3 This is a schematic diagram of the tower crane sensing area redundancy overlap optimization system 200 provided in this application embodiment. The tower crane sensing area redundancy overlap optimization system 200 is used to execute the steps of the tower crane sensing area redundancy overlap optimization method shown in the above embodiments. The tower crane sensing area redundancy overlap optimization system 200 can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, laptop computer, wearable device, or robot.

[0104] like Figure 3 As shown, the tower crane sensing area redundancy overlap optimization system 200 includes:

[0105] The model building unit 201 is used to initialize the dual radar modules of the tower crane, obtain the initial installation position, rotation angle range and sensing distance parameters of the dual radar modules, and build the radar field of view model of each dual radar module. The radar field of view model is used to characterize the sensing area of ​​the radar at different rotation angles.

[0106] The information acquisition unit 202 is used to acquire hoisting path planning information when the tower crane is carrying out hoisting operations. Based on the radar field of view model, it dynamically calculates the degree of overlap between the perception area corresponding to the dual radar modules and each area on the hoisting path according to the current rotation angle of the dual radar modules.

[0107] The overlapping monitoring unit 203 is used to identify key areas in the hoisting operation and set them as high-weight redundant protection zones. The key areas include the vertical channel of the hook and the obstacle-prone area. A preset high perception weight is assigned to the high-weight redundant protection zone. The rotation angle allocation of the dual radar modules is optimized according to the degree of overlap and the high perception weight, so that the perception area of ​​the dual radar modules forms overlapping monitoring of the high-weight redundant protection zone.

[0108] The abnormal takeover unit 204 is used to monitor the working status of the dual radar modules in real time during the hoisting operation. When an abnormality is detected in either radar module, the sensing area of ​​the abnormal radar module is determined, and the overlapping radar field of view of the other normally functioning radar module is dispatched to the critical area to take over the sensing task of the abnormal radar module in the critical area, so as to ensure the continuity and safety of the hoisting operation.

[0109] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the tower crane sensing area redundancy overlap optimization system and its modules described above can be found in the corresponding contents of the various embodiments of the tower crane sensing area redundancy overlap optimization method, and will not be repeated here.

[0110] The aforementioned method for optimizing redundancy overlap in the tower crane's sensing area can be implemented as a computer program, which can be used in various ways, such as... Figure 3 It runs on the device shown.

[0111] Please see Figure 4 , Figure 4 This is a schematic block diagram of the controller provided in an embodiment of this application. The controller includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0112] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any tower crane sensing area redundancy overlap optimization method.

[0113] The processor provides computing and control capabilities to support the operation of the entire controller.

[0114] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to perform any method for optimizing the redundancy overlap of the tower crane sensing area.

[0115] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal to which the present application is applied. The specific controller may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0116] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0117] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0118] The system initializes the dual radar modules of the tower crane, obtains the initial installation position, rotation angle range and sensing distance parameters of the dual radar modules, and constructs the radar field of view model of each dual radar module. The radar field of view model is used to characterize the sensing area of ​​the radar at different rotation angles.

[0119] When the tower crane is carrying out hoisting operations, the hoisting path planning information is obtained. Based on the radar field of view model, the degree of overlap between the sensing area corresponding to the dual radar modules and each area on the hoisting path is dynamically calculated according to the current rotation angle of the dual radar modules.

[0120] Key areas in the hoisting operation are identified and set as high-weight redundant protection zones. The key areas include the vertical passage of the hook and areas with high incidence of obstacles. Preset high perception weights are assigned to the high-weight redundant protection zones. The rotation angle allocation of the dual radar modules is optimized according to the degree of overlap and the high perception weights, so that the perception areas of the dual radar modules overlap and monitor the high-weight redundant protection zones.

[0121] During the hoisting operation, the working status of the dual radar modules is monitored in real time. When an abnormality is detected in either radar module, the perception area of ​​the abnormal radar module is determined, and the overlapping radar field of view of the other normally functioning radar module is reassigned to the critical area to take over the perception task of the abnormal radar module in the critical area, ensuring the continuity and safety of the hoisting operation.

[0122] In some embodiments, constructing the radar field-of-view model for each of the two radar modules includes: establishing a global coordinate system with the tower crane's slewing center as the origin based on the initial installation position coordinates of the two radar modules; for each radar module, dividing the rotation angle range into discrete angle nodes according to a preset angle resolution; for each angle node, generating a fan-shaped detection area in the global coordinate system based on the sensing distance parameter, wherein the center of the fan-shaped detection area is the radar module installation position, the radius is the sensing distance, and the central angle is the radar beam angle; and storing the fan-shaped detection areas corresponding to all angle nodes to form a radar field-of-view model database containing angle and area mapping relationships.

[0123] In some embodiments, the step of dynamically calculating the overlap degree between the sensing area corresponding to the dual radar modules and each area on the hoisting path based on the radar field-of-view model and the current rotation angle of the dual radar modules includes: dividing the hoisting path into path segment regions at preset intervals; extracting the polygon coordinate set of each path segment region in the global coordinate system; retrieving the corresponding fan-shaped detection area polygon from the radar field-of-view model database according to the current rotation angle of the dual radar modules; using a spatial geometric overlap algorithm to calculate the ratio of the overlap area of ​​the path segment region polygon and the fan-shaped detection area polygon to the total area of ​​the path segment region, as the quantized value of the overlap degree; and storing the quantized values ​​of the overlap degree of each dual radar module after normalization.

[0124] In some embodiments, determining the key areas in the hoisting operation and setting them as high-weight redundancy protection zones includes: extracting the vertical motion trajectory of the hook based on hoisting path planning information, generating a cylindrical space centered on the trajectory and with a preset safety radius as the vertical channel of the hook; identifying areas where the frequency of obstacles during hoisting exceeds a preset threshold through statistical analysis of historical operation data, as high-incidence obstacle areas; fusing the spatial coordinate ranges of the vertical channel of the hook and the high-incidence obstacle areas to generate a set of key areas containing three-dimensional spatial coordinates, and marking each key area with a unique identifier.

[0125] In some embodiments, allocating a preset high perception weight to a high-weight redundant protection zone includes: establishing a weight allocation rule base, wherein the perception weight of the hook vertical channel is preset to the highest level weight value, and the perception weight of the obstacle-prone area is dynamically assigned according to the grading results corresponding to the historical obstacle occurrence frequency; for each key area, a weight configuration table containing area identifiers, weight values, and monitoring priorities is generated according to its spatial coordinate range and the weight allocation rule base, and the weight configuration table is used to guide the perception resource scheduling of the dual radar modules.

[0126] In some embodiments, the method further includes: collecting obstacle detection data, radar module scheduling records, and lifting operation risk event data for each key area in historical lifting operations to construct a historical dataset; extracting features from the historical dataset to obtain feature vectors containing key area spatial coordinates, obstacle occurrence frequency, radar sensing angle allocation scheme, and risk event type; training the feature vectors using a machine learning algorithm to establish a dynamic adjustment model for key area sensing weights, wherein the dynamic adjustment model for key area sensing weights is used to predict the optimal sensing weights for each key area based on real-time operating environment parameters; periodically inputting wind speed, visibility, and lifting load parameters in the real-time operating environment into the dynamic adjustment model for key area sensing weights to generate a predicted sensing weight adjustment strategy; and updating the preset sensing weights in the weight allocation rule base according to the sensing weight adjustment strategy to adapt to the monitoring needs of high-weight redundant protection zones under different operating environments.

[0127] In some embodiments, optimizing the rotation angle allocation of the dual radar modules based on the degree of overlap and high perception weight, so that the perception areas of the dual radar modules form overlapping monitoring of the high-weight redundant protection zones, includes: constructing a multi-objective optimization model containing the rotation angle range of the dual radar modules, the weight values ​​of key areas, and the quantified value of the degree of overlap, with the objective function being to maximize the weighted sum of the overlapping areas of the key areas; solving the multi-objective optimization model using a particle swarm optimization algorithm or a genetic algorithm, and generating the optimal combination of rotation angles under the condition of satisfying the mechanical rotation constraints of the dual radar modules; determining whether each key area under the optimal combination of rotation angles is simultaneously covered by the perception area of ​​the dual radar modules, and if not covered, adjusting the weight coefficients of the objective function and resolving until all high-weight redundant protection zones form overlapping monitoring.

[0128] In some embodiments, the real-time detection of the working status of the dual radar modules during the hoisting operation includes: sending a status query command to the dual radar modules through a preset detection cycle to obtain status feedback data including signal strength, data refresh rate, and error check code; performing real-time analysis on the status feedback data; and generating a corresponding abnormal warning signal when the signal strength is lower than a preset threshold, the data refresh rate fluctuation exceeds the allowable range, or the number of consecutive error check failures reaches a warning value. The abnormal warning signal includes the radar module identifier, the abnormality type, and the occurrence time.

[0129] In some embodiments, when an anomaly is detected in any radar module, determining the sensing area of ​​the anomaly radar module and rescheduling the overlapping radar field of view of another normally functioning radar module to the critical area to take over the sensing task of the anomaly radar module in the critical area includes: retrieving the fan-shaped detection area corresponding to the anomaly radar module before its failure from the radar field of view model database as the failure sensing area based on the current rotation angle of the anomaly radar module; for the normally functioning radar module, calculating the adjustable field of view range that can cover the critical area but is not covered by the failure sensing area within its remaining rotation angle range using a geometric transformation algorithm; preferentially selecting the rotation angle with the largest overlap area with the critical area within the adjustable field of view range as the scheduling angle, generating a control command containing the scheduling angle and scheduling time and sending it to the normally functioning radar module to adjust the rotation angle to cover the critical area.

[0130] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the steps of the tower crane sensing area redundancy overlap optimization method provided in any embodiment of this application.

[0131] The computer-readable storage medium may be an internal storage unit of the controller as described in the foregoing embodiments, such as the hard disk or memory of the controller. Alternatively, the computer-readable storage medium may be an external storage device of the controller, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card.

[0132] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for optimizing redundancy overlap in the sensing area of ​​a tower crane, characterized in that, include: The system initializes the dual radar modules of the tower crane, obtaining the initial installation position, rotation angle range, and sensing distance parameters of the dual radar modules. It then constructs radar field-of-view models for each of the dual radar modules, including: establishing a global coordinate system with the tower crane's rotation center as the origin based on the initial installation position coordinates of the dual radar modules; for each radar module, dividing the rotation angle range into discrete angle nodes according to a preset angle resolution; for each angle node, generating a fan-shaped detection area in the global coordinate system based on the sensing distance parameters, where the center of the fan-shaped detection area represents the radar module's installation position, the radius represents the sensing distance, and the central angle represents the radar beam angle; and storing the fan-shaped detection areas corresponding to all angle nodes to form a radar field-of-view model database containing angle and area mapping relationships. This radar field-of-view model is used to characterize the radar's sensing area at different rotation angles. During tower crane hoisting operations, hoisting path planning information is acquired. Based on the radar field-of-view model, and according to the current rotation angle of the dual radar modules, the degree of overlap between the sensing area corresponding to the dual radar modules and various areas on the hoisting path is dynamically calculated. This includes: dividing the hoisting path into path segment regions at preset intervals; extracting the polygon coordinate set of each path segment region in the global coordinate system; retrieving the corresponding fan-shaped detection area polygon from the radar field-of-view model database according to the current rotation angle of the dual radar modules; using a spatial geometric overlap algorithm to calculate the ratio of the overlap area of ​​the path segment region polygon and the fan-shaped detection area polygon to the total area of ​​the path segment region, as a quantified value of the overlap degree; and storing the quantified values ​​of the overlap degree of each dual radar module after normalization. Key areas in the hoisting operation are identified and set as high-weight redundant protection zones. The key areas include the vertical passage of the hook and areas with high incidence of obstacles. Preset high perception weights are assigned to the high-weight redundant protection zones. The rotation angle allocation of the dual radar modules is optimized according to the degree of overlap and the high perception weights, so that the perception areas of the dual radar modules overlap and monitor the high-weight redundant protection zones. During the hoisting operation, the working status of the dual radar modules is monitored in real time. When an abnormality is detected in either radar module, the perception area of ​​the abnormal radar module is determined, and the overlapping radar field of view of the other normally functioning radar module is reassigned to the critical area to take over the perception task of the abnormal radar module in the critical area, ensuring the continuity and safety of the hoisting operation.

2. The method according to claim 1, characterized in that, The process of identifying critical areas in hoisting operations and setting them as high-weight redundancy protection zones includes: Based on the hoisting path planning information, the vertical motion trajectory of the hook is extracted, and a cylindrical space is generated with the trajectory as the center and a preset safety radius as the vertical channel of the hook. By statistically analyzing historical operation data, areas where the frequency of obstacles during hoisting exceeds a preset threshold are identified as high-risk obstacle areas. The spatial coordinate ranges of the vertical channel of the hook and the obstacle-prone area are merged to generate a set of key regions containing three-dimensional spatial coordinates, and each key region is marked with a unique identifier.

3. The method according to claim 1, characterized in that, The allocation of preset high-sensing weights to high-weight redundant protection zones includes: A weight allocation rule base is established, in which the perception weight of the vertical channel of the hook is preset to the highest level weight value, and the perception weight of the obstacle high-occurrence area is dynamically assigned according to the classification results corresponding to the frequency of historical obstacle occurrence. For each key area, a weight configuration table containing area identifiers, weight values, and monitoring priorities is generated based on its spatial coordinate range and weight allocation rule base. The weight configuration table is used to guide the scheduling of sensing resources for the dual radar modules.

4. The method according to claim 3, characterized in that, The method further includes: Collect obstacle detection data, radar module scheduling records, and lifting operation risk event data for key areas in historical lifting operations to construct a historical dataset; Feature extraction is performed on the historical dataset to obtain feature vectors containing key area spatial coordinates, obstacle occurrence frequency, radar sensing angle allocation scheme, and risk event type; The feature vector is trained using a machine learning algorithm to establish a dynamic adjustment model for the perception weight of key areas. The dynamic adjustment model for the perception weight of key areas is used to predict the optimal perception weight of each key area based on real-time operating environment parameters. Periodically input the wind speed, visibility and hoisting load parameters in the real-time working environment into the key area perception weight dynamic adjustment model to generate a predicted perception weight adjustment strategy; The preset perception weights in the weight allocation rule base are updated according to the perception weight adjustment strategy to adapt to the monitoring needs of high-weight redundant protection zones under different operating environments.

5. The method according to claim 1, characterized in that, The optimization of the rotation angle allocation of the dual radar modules based on the degree of overlap and high perception weight, so that the perception areas of the dual radar modules overlap and monitor the high-weight redundant protection zone, includes: A multi-objective optimization model is constructed, which includes the rotation angle range of dual radar modules, the weight value of key regions, and the quantification value of the degree of overlap. The objective function is to maximize the total weighted overlap area of ​​the key regions. The multi-objective optimization model is solved using a particle swarm optimization algorithm or a genetic algorithm, and the optimal combination of rotation angles is generated under the condition of satisfying the mechanical rotation constraints of the dual radar modules. Determine whether each key area under the optimal rotation angle combination is simultaneously covered by the sensing area of ​​the dual radar modules. If not covered, adjust the objective function weight coefficients and resolve until all high-weight redundant protection zones form overlapping monitoring.

6. The method according to claim 1, characterized in that, The real-time monitoring of the operating status of the dual radar modules during the hoisting operation includes: The system sends a status query command to the dual radar modules through a preset detection cycle to obtain status feedback data including signal strength, data refresh rate, and error check code. The status feedback data is analyzed in real time. When the signal strength is lower than the preset threshold, the data refresh rate fluctuates beyond the allowable range, or the number of consecutive error verification failures reaches the warning value, a corresponding abnormal warning signal is generated. The abnormal warning signal includes the radar module identifier, the abnormality type, and the occurrence time.

7. The method according to claim 1, characterized in that, When an anomaly is detected in any radar module, the sensing area of ​​the malfunctioning radar module is determined, and the overlapping radar field of view of another normally functioning radar module is redirected to the critical area to take over the sensing task of the malfunctioning radar module in the critical area, including: Based on the current rotation angle of the abnormal radar module, the corresponding sector detection area before its failure is retrieved from the radar field model database as the failure perception area. For a normally functioning radar module, within its remaining rotation angle range, the adjustable field of view range that can cover the critical area but is not covered by the failed sensing area is calculated using a geometric transformation algorithm. The rotation angle with the largest overlap with the key area within the adjustable field of view is selected as the scheduling angle. A control command containing the scheduling angle and scheduling time is generated and sent to the normally operating radar module to adjust the rotation angle to cover the key area.

8. A tower crane sensing area redundancy overlap optimization system, characterized in that, include: The model building unit is used to initialize the dual radar modules of the tower crane, obtain the initial installation position, rotation angle range, and sensing distance parameters of the dual radar modules, and construct the radar field-of-view models of each dual radar module. This includes: establishing a global coordinate system with the tower crane's rotation center as the origin based on the initial installation position coordinates of the dual radar modules; dividing the rotation angle range into discrete angle nodes according to a preset angle resolution for each radar module; generating a fan-shaped detection area in the global coordinate system for each angle node based on the sensing distance parameters, where the center of the fan-shaped detection area is the radar module installation position, the radius is the sensing distance, and the central angle is the radar beam angle; and storing the fan-shaped detection areas corresponding to all angle nodes to form a radar field-of-view model database containing angle and area mapping relationships. This radar field-of-view model is used to characterize the radar's sensing area at different rotation angles. The information acquisition unit is used to acquire hoisting path planning information during tower crane hoisting operations. Based on the radar field-of-view model, and according to the current rotation angle of the dual radar modules, it dynamically calculates the overlap degree between the sensing area corresponding to the dual radar modules and various areas on the hoisting path. This includes: dividing the hoisting path into path segment regions at preset intervals; extracting the polygon coordinate set of each path segment region in the global coordinate system; retrieving the corresponding fan-shaped detection area polygon from the radar field-of-view model database according to the current rotation angle of the dual radar modules; using a spatial geometric overlap algorithm to calculate the ratio of the overlap area of ​​the path segment region polygon and the fan-shaped detection area polygon to the total area of ​​the path segment region, as a quantified value of the overlap degree; and storing the quantified values ​​of the overlap degree of each dual radar module after normalization. The overlapping monitoring unit is used to identify key areas in the hoisting operation and set them as high-weight redundant protection zones. The key areas include the vertical passage of the hook and the obstacle-prone area. A preset high perception weight is assigned to the high-weight redundant protection zone. The rotation angle allocation of the dual radar modules is optimized according to the degree of overlap and the high perception weight, so that the perception area of ​​the dual radar modules forms overlapping monitoring of the high-weight redundant protection zone. The abnormal takeover unit is used to monitor the working status of the dual radar modules in real time during hoisting operations. When an abnormality is detected in either radar module, the sensing area of ​​the abnormal radar module is determined, and the overlapping radar field of view of the other normally functioning radar module is reassigned to the critical area to take over the sensing task of the abnormal radar module in the critical area, ensuring the continuity and safety of the hoisting operation.

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