An intelligent assisted positioning system, method and hook for rigging

Abstract

The present application relates to the technical field of automatic positioning, in particular to an intelligent auxiliary positioning system for rigging, a method and a hook. The auxiliary positioning system periodically collects positioning data, activates the corresponding base station by means of a WiFi module to construct an activation area, then synchronizes the clock of the base station with a rigging clock module, and determines the accurate coordinates of the hook by constructing a multi-edge positioning geometric model with an ultra-wideband tag module, while judging the accuracy of the accurate coordinates. When the judgment is inaccurate, the optimization module is used to determine the cause of the inaccuracy and correct the corresponding parameters. By reasonably integrating the advantages of different positioning technologies and introducing an automated abnormality judgment and parameter correction mechanism, the present application significantly improves the accuracy and stability of rigging positioning, enhances the system's adaptability to complex environments, provides reliable technical support for precise operation of rigging, and effectively improves the safety and efficiency of work.

Description

Technical Field

[0001] This invention relates to the field of automatic positioning technology, and more specifically to an intelligent auxiliary positioning system, method and hook for rigging. Background Technology

[0002] In complex operating environments such as construction sites or ports, accurate positioning of rigging such as hooks is crucial for ensuring operational safety, improving efficiency, reducing equipment wear, and supporting intelligent operations. Ports, in particular, place extremely high demands on rigging positioning accuracy due to their complex environment. The dense metal structures, strong electromagnetic interference, and variable weather conditions in ports present numerous challenges to rigging positioning; both endpoint positioning and path positioning require high precision to ensure smooth operation.

[0003] However, while existing rigging positioning technologies can achieve positioning to some extent, they mostly rely on fixed parameters, making it difficult to adapt to the complex and ever-changing realities of ports. Environmental factors such as the multipath effect of signals, electromagnetic interference, and changes in weather conditions, as well as the dynamic complexity of rigging, including its swinging, vertical movement, and nonlinear motion trajectories caused by load changes, all make it difficult for fixed parameters to accurately predict rigging positions.

[0004] Therefore, the current research challenge lies in how to make the positioning system adaptive, so that it can automatically adjust parameters according to different environments and dynamic conditions, thereby achieving precise positioning of rigging under various complex conditions and meeting the high-precision requirements of complex operating environments such as ports.

[0005] Chinese patent CN108821120B discloses a hook positioning system, comprising: a GNSS rover, a laser signal transmitter, a yaw measurement device, and a data processing device. The GNSS rover includes a navigation and positioning device and a coordinate calculation device for calculating the three-dimensional spatial coordinates of the GNSS rover's antenna phase center. The laser signal transmitter senses the sway of the second suspension rope segment and sends a laser signal vertically upwards when the second suspension rope segment sways. The yaw measurement device receives the laser signal and measures and calculates the yaw parameters of the laser signal transmitter based on the laser signal. The data processing device calculates the three-dimensional spatial coordinates of the hook based on the three-dimensional spatial coordinates of the movable pulley center obtained from the GNSS rover's antenna phase center and the yaw parameters of the laser signal transmitter. This system accurately positions the hook. While this patent can achieve hook positioning to a certain extent, its fixed-parameter positioning method cannot fully guarantee positioning accuracy in the face of many interference factors in actual application. How to endow rigging with precise positioning capabilities and enable it to locate the cause of abnormal situations in a timely manner, and at the same time construct positioning parameters specifically for the cause of abnormalities, so as to achieve more accurate and reliable rigging positioning, has become a difficult problem in current research. Summary of the Invention

[0006] This invention provides an intelligent auxiliary positioning system for rigging, which overcomes the problems of existing rigging positioning systems being weakly resistant to environmental interference during use, having difficulty automatically identifying the cause of abnormalities and making targeted corrections to positioning parameters, resulting in insufficient positioning accuracy and easy errors.

[0007] In a first aspect, the present invention provides an intelligent assisted positioning system for rigging, comprising,

[0008] The data acquisition module is used to periodically collect rigging fingerprints, planned paths, coordinates of activated ultra-wideband base stations, and corresponding rigging-base station distance datasets.

[0009] A preprocessing module, connected to the acquisition module, is used to reduce noise in the rigging-base station distance dataset based on the absolute value of the effective range threshold.

[0010] A WiFi module, connected to the acquisition module, is used to determine the rigging fingerprint and initial activation radius in the WiFi fingerprint database based on the rigging fingerprint and a radius selection threshold.

[0011] An activation module, which is connected to the WiFi module, is used to construct an activation range and activate the corresponding ultra-wideband base station based on the activation radius and the rigging fingerprint. The activation radius is the product of the initial activation radius and the preset radius multiple.

[0012] A clock module, which is connected to the activation module, is used to activate the ultra-wideband base station clock based on the rigging master clock.

[0013] An ultra-wideband tag module, which is connected to the preprocessing module and the clock module, is used to construct a polygonal positioning geometric model and determine precise coordinates based on the rigging-base station distance dataset;

[0014] The accuracy evaluation module, which is connected to the ultra-wideband tag module, is used to determine the deviation distance based on the minimum distance from the precise coordinates to the planned path, and to determine whether the rigging has deviated based on the deviation distance, and to issue a correction notification command when a deviation is determined, or to maintain the parameters when no deviation is determined.

[0015] An optimization module, connected to an accuracy evaluation module, is used to determine whether the positioning is qualified based on the deviation distance after correction, and, when the positioning is unqualified, to construct a wide activation range based on 3 times the initial activation radius, and to activate the ultra-wideband base station within the wide activation range to redetermine the corresponding deviation distance as the wide range deviation, to determine the reason for the unqualification based on the ratio of the wide range deviation to the deviation distance, and to correct the activation range construction parameters, acquisition cycle and preprocessing parameters based on the reason for the unqualification and to issue a corresponding correction command.

[0016] A control module, which is connected to the optimization module, the acquisition module, the preprocessing module and the WiFi module, is used to correct the corresponding module based on the instructions of the optimization module;

[0017] The notification module, which is connected to the accuracy evaluation module, is used to issue notifications based on correction notification instructions.

[0018] Furthermore, the optimization module is used to determine the correction-deviation distance based on the deviation distance after correction, and to determine whether the positioning is qualified based on the correction-deviation distance, and to maintain the system parameters when qualified, or to construct a wide activation range based on 3 times the initial activation radius when unqualified, and to activate the ultra-wideband base station within the wide activation range to redetermine the corresponding deviation distance as the wide range deviation, and to determine the reason for unqualification based on the ratio of the wide range deviation to the deviation distance;

[0019] Wherein, the initial activation radius refers to the maximum distance between the selected fingerprint and the rigging fingerprint among several selected fingerprints selected based on the radius selection threshold in the WiFi fingerprint database;

[0020] The selected fingerprint refers to a fingerprint in the WiFi fingerprint database whose similarity to the rigging fingerprint is greater than or equal to the radius selection threshold.

[0021] The radius selection threshold refers to the minimum similarity that needs to be achieved between the fingerprint in the WiFi fingerprint database and the rigging fingerprint when the fingerprint in the WiFi fingerprint database is determined to be the selected fingerprint.

[0022] The WiFi fingerprint database refers to a database of WiFi signal feature sets corresponding to various location points that have been pre-collected and stored.

[0023] The rigging fingerprint refers to the set of WiFi signal features collected corresponding to the rigging.

[0024] Furthermore, the optimization module is also used to construct a wide activation range based on 3 times the activation radius, and to activate the ultra-wideband base stations within the wide activation range to redetermine the wide range deviation. It also determines the extended deviation range ratio based on the ratio of the wide range deviation to the deviation distance, and judges the reasons for non-compliance based on the extended deviation range ratio. Additionally, when the WiFi signal environment is determined to be unstable, it corrects the radius selection threshold based on the ratio of the preset extended deviation range ratio to the extended deviation range ratio, or constructs a triangle between the rigging and any two activated ultra-wideband base stations, and judges the cause of the anomaly based on the proportion of triangles that do not satisfy the triangle inequality among all triangles.

[0025] Furthermore, the optimization module is also used to determine the expansion deviation range ratio based on the ratio of the preset expansion deviation range ratio to the expansion deviation range ratio, and to reduce the radius selection threshold based on the expansion deviation range ratio, wherein the reduction of the radius selection threshold is proportional to the expansion deviation range ratio.

[0026] Furthermore, the optimization module is also used to construct the triangle with the rigging and any two of the activated ultra-wideband base stations, and to determine the proportion of abnormal triangles based on the proportion of triangles that do not satisfy the triangle inequality in all triangles, and to determine the cause of the abnormality based on the proportion of abnormal triangles, and when it is determined that dynamic occlusion is frequent, to correct the preset radius multiplier based on the ratio of the preset abnormal triangle proportion to the abnormal triangle proportion, or, when it is determined that clock drift is caused, to correct the absolute value of the effective range threshold based on the ratio of the preset deviation distance to the deviation distance;

[0027] The absolute value of the effective range threshold refers to the absolute value of the maximum deviation of the distance value relative to the average distance value in the rigging-base station distance dataset when the distance value in the rigging-base station distance dataset is determined to be valid, based on the average distance value.

[0028] The rigging-base station distance dataset refers to the dataset collected by the acquisition module, which includes distance results between the rigging and the activated ultra-wideband base station at multiple time points. Further,

[0029] Furthermore, the optimization module is also used to determine the abnormal triangle ratio based on the ratio of the preset abnormal triangle ratio to the bathing area triangle ratio, and to increase the preset radius multiple based on the abnormal triangle ratio, wherein the increase in the preset radius multiple is proportional to the abnormal triangle ratio.

[0030] Furthermore, the optimization module is also used to determine the multiplier difference based on the difference in the increase of the preset radius multiplier before and after the correction, and to extend the sampling period based on the multiplier difference, wherein the extension of the sampling period is proportional to the multiplier difference.

[0031] Furthermore, the optimization module is also used to determine the deviation distance ratio based on the preset deviation distance and the correction-deviation distance ratio, and to reduce the absolute value of the effective range threshold based on the deviation distance ratio, wherein the reduction in the absolute value of the effective range threshold is proportional to the deviation distance ratio.

[0032] Secondly, the present invention also provides an intelligent assisted positioning method for rigging, comprising,

[0033] Periodically collect rigging fingerprints, planned paths, and several rigging-base station distance datasets;

[0034] Denoising the rigging-base station distance dataset based on the absolute value of the effective range threshold;

[0035] Based on the rigging fingerprint and the radius selection threshold, determine the rigging fingerprint and the initial activation radius in the WiFi fingerprint database;

[0036] The activation range is constructed based on the activation radius and the rigging fingerprint, and the corresponding ultra-wideband base station is activated. The activation radius is the product of the initial activation radius and the preset radius multiple.

[0037] Ultra-wideband base station clock based on rigging master clock synchronization activation;

[0038] Based on the rigging-base station distance dataset, a polygonal positioning geometric model is constructed and precise coordinates are determined.

[0039] The deviation distance is determined based on the minimum distance from the precise coordinates to the planned path, and the deviation distance is used to determine whether the rigging has deviated, and a correction notification instruction is issued when a deviation is determined, or the parameters are maintained when no deviation is determined.

[0040] The positioning is determined to be qualified based on the deviation distance after correction. If it is determined to be unqualified, a wide activation range is constructed based on 3 times the initial activation radius, and the ultra-wideband base station within the wide activation range is activated to redetermine the corresponding deviation distance, which is recorded as the wide range deviation. The reason for the unqualification is determined based on the ratio of the wide range deviation to the deviation distance. The activation range construction parameters, clock synchronization parameters, and preprocessing parameters are corrected based on the reason for the unqualification.

[0041] Thirdly, the present invention also provides a lifting hook, including a lifting ring, a control body, a hook body, and a safety buckle;

[0042] The lifting ring is disposed above the control body and is fixedly connected to the control body for external connection of the hook;

[0043] The control unit is equipped with a safety buckle at its lower part and is fixedly connected to the safety buckle. The control unit includes the acquisition module, the preprocessing module, the WiFi module, the activation module, the clock module, the ultra-wideband tag module, the accuracy evaluation module, the optimization module, the control module, and the notification module.

[0044] The acquisition module is used to periodically acquire hook fingerprints, planned paths, and several hook-base station distance datasets;

[0045] The preprocessing module, which is connected to the acquisition module, is used to reduce noise in the hook-base station distance dataset based on the absolute value of the effective range threshold.

[0046] The WiFi module is connected to the acquisition module and is used to determine the hook fingerprint and the initial activation radius in the WiFi fingerprint database based on the hook fingerprint and the radius selection threshold.

[0047] The activation module is connected to the WiFi module and is used to construct an activation range and activate the corresponding ultra-wideband base station based on the activation radius and the hook fingerprint. The activation radius is the product of the initial activation radius and the preset radius multiple.

[0048] The clock module, which is connected to the activation module, is used to activate the ultra-wideband base station clock based on the hook master clock.

[0049] The ultra-wideband tag module is connected to the preprocessing module and is used to construct a polygonal positioning geometric model and determine precise coordinates based on the hook-base station distance dataset.

[0050] The accuracy evaluation module is connected to the ultra-wideband tag module and is used to determine the deviation distance based on the minimum distance from the precise coordinates to the planned path, and to determine whether the hook has deviated based on the deviation distance, and to issue a correction notification command when a deviation is determined, or to maintain the parameters when no deviation is determined.

[0051] The optimization module, connected to the accuracy evaluation module, is used to determine whether the positioning is qualified based on the deviation distance after correction, and when it is determined to be unqualified, to construct a wide activation range based on 3 times the initial activation radius, and to activate the ultra-wideband base station within the wide activation range to redetermine the corresponding deviation distance as the wide range deviation, to determine the reason for the unqualification based on the ratio of the wide range deviation to the deviation distance, and to correct the activation range construction parameters, acquisition cycle and preprocessing parameters based on the reason for the unqualification and to issue a corresponding correction command.

[0052] The control module is connected to the optimization module, the preprocessing module, the WiFi module, the activation module, and the clock module, and is used to modify the corresponding module based on the instructions of the optimization module.

[0053] The notification module, which is connected to the accuracy evaluation module, is used to issue a notification based on the correction notification instruction;

[0054] The safety buckle is located at the end of the hook body to fasten the hook body to the control body and prevent slippage.

[0055] Compared with existing technologies, the beneficial effects of this invention are as follows: The intelligent assisted positioning system for rigging proposed in this invention periodically acquires key positioning data through a data acquisition module, and utilizes preprocessing, WiFi fingerprint matching, ultra-wideband base station activation, clock synchronization, polygon positioning geometric model construction, and accuracy evaluation modules to work collaboratively, achieving high-precision positioning of the rigging. In this invention, firstly, WiFi technology and ultra-wideband technology are combined, which reduces the computational burden on the rigging while achieving accurate positioning, improving computational speed and accuracy. Secondly, the optimization module determines whether the positioning is qualified based on the deviation distance after correction, and when the positioning is unqualified, it redetermines the deviation distance by constructing a wide activation range, thereby determining the reason for the unqualified positioning. It can also automatically correct the activation range construction parameters, acquisition cycle, and preprocessing parameters according to different reasons for unqualified positioning, enabling the system to dynamically adjust positioning parameters according to changes in the external environment and the actual operating state of the rigging, effectively improving the stability and reliability of rigging positioning, thereby ensuring adaptive and accurate positioning of the rigging under different working conditions.

[0056] Furthermore, by determining whether the positioning is qualified based on the deviation distance after correction, and re-determining the deviation distance using a wider activation range when it is unqualified, the system can quickly identify whether there is a problem with the positioning. This method of dynamically adjusting the activation range enables the system to automatically adjust the positioning strategy when facing complex signal environments or dynamic changes, thereby improving the adaptability and accuracy of positioning.

[0057] Furthermore, by constructing a wide activation range and redefining the wide range deviation, the accuracy and stability of positioning can be evaluated more comprehensively. Determining the extended deviation range ratio based on the ratio of the wide range deviation to the deviation distance provides a more precise quantitative basis for identifying the causes of non-compliance. When the WiFi signal environment is determined to be unstable, the positioning strategy can be dynamically adjusted by modifying the radius and selecting a threshold to adapt to changes in the signal environment, improving the anti-interference capability and stability of positioning. In addition, by constructing triangles and judging the cause of anomalies based on the proportion of triangles that do not satisfy the triangle inequality, the system can more accurately identify anomalies such as frequent dynamic occlusion or clock drift and take corresponding corrective measures. This geometrically constrained anomaly detection method not only improves the system's adaptability and robustness but also enhances its versatility and adaptability, enabling it to better cope with complex and changing working environments, ensuring that the rigging always runs along the planned path, and improving the safety and efficiency of operations.

[0058] Furthermore, the extended deviation range ratio is determined by comparing the preset extended deviation range ratio with the actual extended deviation range ratio, and the radius selection threshold is reduced accordingly, allowing the system to adjust positioning parameters more precisely. The reduction in the radius selection threshold is proportional to the extended deviation range ratio. This dynamic adjustment method ensures that the system can automatically and flexibly adjust its positioning strategy to achieve optimal positioning results when facing different signal environment changes. This refined parameter adjustment mechanism not only improves the system's adaptability and robustness but also further optimizes resource utilization, enhances the system's versatility and adaptability, enabling it to better cope with complex environments, ensure that the rigging always runs along the planned path, and improve the safety and efficiency of operations.

[0059] Furthermore, by constructing a triangle between the rigging and any two activated ultra-wideband base stations, and determining the proportion of abnormal triangles based on the proportion of triangles that do not satisfy the triangle inequality, the system can more accurately identify anomalies such as frequent dynamic occlusion or clock drift. This enables it to better cope with complex environments, ensuring that the rigging always runs along the planned path, thus improving the safety and efficiency of operations. In addition, the detailed definitions of the absolute value of the effective range threshold and the rigging-base station distance dataset provide clear technical guidance for system implementation, contributing to improved system stability and repeatability.

[0060] Furthermore, by determining the abnormal triangle ratio based on the ratio of the preset abnormal triangle ratio to the actual abnormal triangle ratio, and increasing the preset radius multiplier accordingly, the system can more flexibly adjust positioning parameters. The increase in the preset radius multiplier is proportional to the abnormal triangle ratio. This dynamic adjustment method ensures that the system can automatically and flexibly adjust its positioning strategy to achieve optimal positioning results when facing different dynamic occlusion situations. This refined parameter adjustment mechanism not only improves the system's adaptability and robustness but also further optimizes resource utilization, enhances the system's versatility and adaptability, enabling it to better cope with complex environments, ensure that the rigging always runs along the planned path, and improve the safety and efficiency of operations.

[0061] Furthermore, by determining the multiplier difference based on the difference in the increase of the preset radius multiplier before and after correction, and extending the sampling period accordingly, the system can more flexibly adjust the data acquisition time interval. The magnitude of the sampling period extension is proportional to the multiplier difference. This dynamic adjustment method ensures that the system has sufficient time to acquire reliable and complete data when activating more ultra-wideband base stations, avoiding positioning errors caused by incomplete or conflicting data. This refined parameter adjustment mechanism not only improves the system's adaptability and robustness but also further optimizes resource utilization, enhances the system's versatility and adaptability, enabling it to better cope with complex environments, ensure that the rigging always runs along the planned path, and improve the safety and efficiency of operations.

[0062] Furthermore, by determining the deviation distance ratio based on the preset deviation distance and the correction-deviation distance ratio, and thereby reducing the absolute value of the effective range threshold, the system can adjust positioning parameters more precisely. The reduction in the absolute value of the effective range threshold is proportional to the deviation distance ratio. This dynamic adjustment method ensures that the system can automatically and flexibly adjust its positioning strategy to achieve optimal positioning results when facing different clock drift conditions. This refined parameter adjustment mechanism not only improves the system's adaptability and robustness but also further optimizes resource utilization, enhances the system's versatility and adaptability, enabling it to better cope with complex environments, ensure that the rigging always runs along the planned path, and improve the safety and efficiency of operations.

[0063] Furthermore, this invention provides an intelligent assisted positioning method for rigging. By periodically collecting key data and combining it with a series of steps including preprocessing, WiFi fingerprint matching, ultra-wideband base station activation, clock synchronization, construction of a polygonal positioning geometric model, accuracy evaluation, and optimization, high-precision positioning of the rigging is achieved. This method not only adapts to complex and changing working environments but also automatically adjusts positioning strategies to cope with different abnormal situations. By dynamically adjusting parameters such as the ratio of extended deviation range, the proportion of abnormal triangles, the sampling period, and the effective range threshold, the system can optimize resource utilization and improve positioning accuracy and stability. This systematic positioning method not only improves the accuracy and stability of rigging positioning but also enhances the system's adaptability and robustness, providing reliable technical support for precise rigging operation and effectively improving operational safety and efficiency.

[0064] Furthermore, this invention provides a lifting hook that integrates a data acquisition module, a preprocessing module, a WiFi module, an activation module, a clock module, an ultra-wideband tag module, a precision evaluation module, an optimization module, a control module, and a notification module. This enables the hook itself to possess high-precision positioning capabilities, providing reliable technical support for the precise operation of the hook and effectively improving the safety and efficiency of the operation. Simultaneously, the hook design also includes structures such as a lifting ring, a control body, a hook body, and a safety buckle. These structures not only ensure the normal use of the hook but also prevent slippage through the safety buckle design, further enhancing the safety and reliability of the hook. Attached Figure Description

[0065] Figure 1 This is a structural diagram of the hook in an embodiment of the present invention;

[0066] Figure 2 This is a block diagram of an intelligent assisted positioning system for rigging in an embodiment of the present invention;

[0067] Figure 3 This is a flowchart illustrating the intelligent assisted positioning method for rigging in an embodiment of the present invention.

[0068] Figure 4 This is a flowchart illustrating the process of determining the cause of anomalies based on the proportion of an anomaly triangle in an embodiment of the present invention. Detailed Implementation

[0069] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0070] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0071] Please see Figure 1 As shown, this is a structural diagram of a hook in an embodiment of the present invention. The hook of the present invention includes,

[0072] 1. Lifting ring; 2. Control body; 3. Hook; and 4. Safety buckle.

[0073] The lifting ring 1 is disposed above the control body 2 and is fixedly connected to the control body 2 for external connection of the hook;

[0074] The control body 2 is provided with a safety buckle 4 at its lower part and is fixedly connected to the safety buckle 4. The control body 2 includes the acquisition module, the preprocessing module, the WiFi module, the activation module, the clock module, the ultra-wideband tag module, the accuracy evaluation module, the optimization module, the control module, and the notification module.

[0075] The acquisition module is used to periodically acquire hook fingerprints, planned paths, coordinates of activated ultra-wideband base stations, and corresponding hook-base station distance datasets.

[0076] The preprocessing module, which is connected to the acquisition module, is used to reduce noise in the hook-base station distance dataset based on the absolute value of the effective range threshold.

[0077] The WiFi module is connected to the acquisition module and is used to determine the hook fingerprint and the initial activation radius in the WiFi fingerprint database based on the hook fingerprint and the radius selection threshold.

[0078] The activation module is connected to the WiFi module and is used to construct an activation range and activate the corresponding ultra-wideband base station based on the activation radius and the hook fingerprint. The activation radius is the product of the initial activation radius and the preset radius multiple.

[0079] The clock module, which is connected to the activation module, is used to activate the ultra-wideband base station clock based on the hook master clock.

[0080] The ultra-wideband tag module is connected to the preprocessing module and the clock module, and is used to construct a polygonal positioning geometric model and determine precise coordinates based on the hook-base station distance dataset.

[0081] The accuracy evaluation module is connected to the ultra-wideband tag module and is used to determine the deviation distance based on the minimum distance from the precise coordinates to the planned path, and to determine whether the hook has deviated based on the deviation distance, and to issue a correction notification command when a deviation is determined, or to maintain the parameters when no deviation is determined.

[0082] The optimization module is connected to the accuracy evaluation module. It is used to determine whether the positioning is qualified based on the deviation distance after correction. When the positioning is not qualified, it activates the ultra-wideband base station within N times the initial activation radius and redetermines the corresponding deviation distance as the wide range deviation. It determines the reason for the failure based on the ratio of the wide range deviation to the deviation distance. It also corrects the activation range construction parameters, acquisition cycle and preprocessing parameters based on the reason for the failure and issues the corresponding correction command.

[0083] The control module is connected to the optimization module, the acquisition module, the preprocessing module and the WiFi module, and is used to correct the corresponding module based on the instructions of the optimization module;

[0084] The notification module, which is connected to the accuracy evaluation module, is used to issue a notification based on the correction notification instruction;

[0085] The safety buckle 4 is located at the end of the hook body 3 to fasten the hook body 3 to the control body 2 and prevent slippage.

[0086] Further, please refer to Figure 2 As shown in the diagram, this invention provides a block diagram of an intelligent assisted positioning system for rigging. The invention also provides an intelligent assisted positioning system for rigging, comprising: a data acquisition module, a preprocessing module, a WiFi module, an activation module, a clock module, an ultra-wideband tag module, an accuracy evaluation module, an optimization module, a control module, and a notification module.

[0087] The acquisition module is used to periodically collect rigging fingerprints, planned paths, coordinates of activated ultra-wideband base stations, and corresponding rigging-base station distance datasets.

[0088] A preprocessing module, connected to the acquisition module, is used to reduce noise in the rigging-base station distance dataset based on the absolute value of the effective range threshold.

[0089] A WiFi module, connected to the acquisition module, is used to determine the rigging fingerprint and initial activation radius in the WiFi fingerprint database based on the rigging fingerprint and a radius selection threshold.

[0090] An activation module, which is connected to the WiFi module, is used to construct an activation range and activate the corresponding ultra-wideband base station based on the activation radius and the rigging fingerprint. The activation radius is the product of the initial activation radius and the preset radius multiple.

[0091] A clock module, which is connected to the activation module, is used to activate the ultra-wideband base station clock based on the rigging master clock.

[0092] An ultra-wideband tag module, which is connected to the preprocessing module and the clock module, is used to construct a polygonal positioning geometric model and determine precise coordinates based on the rigging-base station distance dataset;

[0093] The accuracy evaluation module, which is connected to the ultra-wideband tag module, is used to determine the deviation distance based on the minimum distance from the precise coordinates to the planned path, and to determine whether the rigging has deviated based on the deviation distance, and to issue a correction notification command when a deviation is determined, or to maintain the parameters when no deviation is determined.

[0094] An optimization module, connected to an accuracy evaluation module, is used to determine whether the positioning is qualified based on the deviation distance after correction, and, when the positioning is unqualified, to construct a wide activation range based on 3 times the initial activation radius, and to activate the ultra-wideband base station within the wide activation range to redetermine the corresponding deviation distance as the wide range deviation, to determine the reason for the unqualification based on the ratio of the wide range deviation to the deviation distance, and to correct the activation range construction parameters, acquisition cycle and preprocessing parameters based on the reason for the unqualification and to issue a corresponding correction command.

[0095] A control module, which is connected to the optimization module, the acquisition module, the preprocessing module and the WiFi module, is used to correct the corresponding module based on the instructions of the optimization module;

[0096] The notification module, which is connected to the accuracy evaluation module, is used to issue notifications based on correction notification instructions.

[0097] Wherein, the initial activation radius refers to the maximum distance between the selected fingerprint and the rigging fingerprint among several selected fingerprints selected based on the radius selection threshold in the WiFi fingerprint database;

[0098] The selected fingerprint refers to a fingerprint in the WiFi fingerprint database whose similarity to the rigging fingerprint is greater than or equal to the radius selection threshold.

[0099] The radius selection threshold refers to the minimum similarity between the fingerprint in the WiFi fingerprint database and the rigging fingerprint when the fingerprint is determined to be the selected fingerprint in the WiFi fingerprint database. The radius selection threshold ranges from 0.7 to 0.9.

[0100] The WiFi fingerprint database refers to a database of WiFi signal feature sets corresponding to various location points that have been pre-collected and stored.

[0101] The rigging fingerprint refers to the set of WiFi signal features collected corresponding to the rigging.

[0102] The process by which the WiFi module determines the rigging fingerprint and the initial activation radius in the WiFi fingerprint database based on the rigging fingerprint and the radius selection threshold includes:

[0103] The WiFi module compares the collected rigging fingerprint with the fingerprints recorded in the WiFi fingerprint database, calculates the similarity, and records fingerprints with a similarity greater than or equal to a radius selection threshold as selected fingerprints. Then, it calculates the distance between each selected fingerprint value and the rigging fingerprint, and sets the maximum distance between the selected fingerprint and the rigging fingerprint as the initial activation radius. Then, it determines the activation radius based on the product of the initial activation radius and the preset radius multiplier, and constructs the activation range based on the activation radius. Then, it activates the corresponding ultra-wideband base station, where the preset radius multiplier is between 3 and 10.

[0104] The absolute value of the effective range threshold refers to the absolute value of the maximum deviation of the distance value relative to the average distance value in the rigging-base station distance dataset when the distance value in the rigging-base station distance dataset is determined to be valid, based on the average distance value.

[0105] The rigging-base station distance dataset refers to the dataset collected by the acquisition module that includes distance results between the rigging and the activated ultra-wideband base station at multiple time points.

[0106] In the ultra-wideband tag module, the process of constructing a polygonal positioning geometric model based on the rigging-base station distance dataset and calculating precise coordinates using the least squares method includes:

[0107] Obtain the rigging-base station distance dataset and the corresponding coordinates of the active UWB base stations for n active UWB base stations, denoted as Bi = {(x i ,y i ,z i )}, i=1,2,3,··· ···,n;

[0108] The collected dataset of rigging-base station distances corresponding to activated ultra-wideband base stations is D. i =

[0109] Where M is the number of sampling times, It is time t j The distance from the rigging to the i-th base station, i = 1, 2, 3, ..., n, j = 1, 2, 3, ..., n;

[0110] For each time t j Based on the distance data of its corresponding base station, a corresponding distance equation is independently constructed:

[0111] ,i=1,2,3,··· ···,n;

[0112] Iterate through the rigging-to-base station distance dataset corresponding to each activated ultra-wideband base station and construct t. j The distance equation at time t yields the result. j The polygonal positioning geometric model corresponding to the given time;

[0113] Find time t j The corresponding rigging coordinates P(t) j )=(x(t j ),y(t j ),z(t j ));

[0114] Construct t jError function J(t) at time j )= ;

[0115] Wherein, error e i ( )=

[0116] Solve for the rigging coordinates P(t) j )=(x(t j ),y(t j ),z(t j This minimizes the error function.

[0117] Among them, the coordinates of the rigging P(t) are solved. j )=(x(t j ),y(t j ),z(t j The method for minimizing the error function is not limited, and technicians can solve it according to their needs, such as based on the Levenberg-Marquardt algorithm, Gauss-Newton algorithm, gradient descent algorithm or conjugate gradient method, which will not be elaborated here.

[0118] After solving, we obtain t. j To obtain the rigging coordinates at any given time, repeat the above steps to obtain the rigging coordinates at all times. Then, calculate the arithmetic mean of the coordinates at all times to obtain the precise rigging coordinates.

[0119] The method for determining precise coordinates based on a polygonal positioning geometric model is not limited in principle. Technicians can choose to solve nonlinear equations to obtain coordinates or optimize using the least squares method, which will not be elaborated here.

[0120] Specifically, the accuracy evaluation module determines the deviation distance based on the minimum distance from the precise coordinates to the planned path, and the process of determining whether the rigging has deviated based on the deviation distance includes:

[0121] The accuracy evaluation module determines the deviation distance DD based on the minimum distance from the precise coordinates to the planned path, and compares the deviation distance DD with the preset deviation distance DD1. In scenarios with high accuracy requirements, such as dock operations, the accuracy requirements of rigging vary depending on the specific application, but are usually required to be within ±10 cm. Therefore, the preset deviation distance DD1 is set to a value range of 5-10 cm.

[0122] If the deviation distance DD is less than or equal to the preset deviation distance DD1, the rigging is considered not to have deviated, and the system parameters are maintained.

[0123] If the deviation distance DD is greater than the preset deviation distance DD1, the rigging is considered to be deviated, the rigging is accurately positioned, and a correction notification command is issued.

[0124] In practical applications, WiFi deployment is typically simple and well-established. However, WiFi positioning often suffers from insufficient accuracy, with positioning errors typically reaching the meter level, making it unsuitable for applications requiring high-precision positioning. In contrast, ultra-wideband (UWB) positioning offers extremely high accuracy, often achieving centimeter-level positioning. However, this high precision comes with a massive computational burden. While this may not be a problem when the computing engine is located on a computer or other terminal device, it can cause severe delays or even system crashes when deployed as a system on a rigging system. Therefore, this invention first uses the computationally simple WiFi to determine the initial activation radius and construct the activation range. Then, UWB base stations within the activation range are activated, significantly reducing the burden of UWB positioning. Furthermore, this invention incorporates parameters from an optimization module to evaluate positioning accuracy. When insufficient accuracy is detected, the cause of the anomaly is automatically identified, and parameters are adjusted accordingly to overcome external environmental interference with the positioning system. This effectively improves the positioning accuracy of the rigging system and its stability in the face of external influencing factors.

[0125] Further, please refer to Figure 3 The diagram shown is a flowchart of an intelligent assisted positioning method for rigging according to an embodiment of the present invention. The intelligent assisted positioning method for rigging according to an embodiment of the present invention includes:

[0126] S1: The acquisition module receives the planned path and periodically acquires the rigging fingerprint, the planned path, the coordinates of the activated ultra-wideband base station, and the corresponding rigging-base station distance dataset.

[0127] S2: The preprocessing module reduces noise in the rigging-base station distance dataset based on the absolute value of the effective range threshold;

[0128] S3: The WiFi module determines the rigging fingerprint and initial activation radius in the WiFi fingerprint database based on the rigging fingerprint and the radius selection threshold;

[0129] S4: The activation module constructs an activation range based on the activation radius and the rigging fingerprint and activates the corresponding ultra-wideband base station. The activation radius is the product of the initial activation radius and the preset radius multiple.

[0130] S5: The clock module is an ultra-wideband base station clock that is synchronized and activated based on the rigging master clock;

[0131] S6: The ultra-wideband tag module constructs a polygonal positioning geometric model and determines precise coordinates based on the rigging-base station distance dataset;

[0132] S7: The accuracy evaluation module determines the deviation distance based on the minimum distance from the precise coordinates to the planned path, and determines whether the rigging has deviated based on the deviation distance, and issues a correction notification instruction when a deviation is determined, or maintains the parameters when no deviation is determined.

[0133] S8: The optimization module determines whether the positioning is qualified based on the deviation distance after correction, and when it is determined to be unqualified, it constructs a wide activation range based on 3 times the initial activation radius, and activates the ultra-wideband base station within the wide activation range to redetermine the corresponding deviation distance as the wide range deviation. It determines the reason for the unqualification based on the ratio of the wide range deviation to the deviation distance, and corrects the activation range construction parameters, acquisition cycle and preprocessing parameters based on the reason for the unqualification and issues the corresponding correction command.

[0134] S9: The control module corrects the corresponding module based on the instructions from the optimization module;

[0135] S10: The notification module issues a notification based on the correction notification instruction.

[0136] Furthermore, the optimization module is used to determine the correction-deviation distance based on the deviation distance after correction, and to determine whether the positioning is qualified based on the correction-deviation distance, and to maintain the system parameters when qualified, or to construct a wide activation range based on 3 times the initial activation radius when unqualified, and to activate the ultra-wideband base station within the wide activation range to redetermine the corresponding deviation distance as the wide range deviation, and to determine the reason for unqualification based on the ratio of the wide range deviation to the deviation distance;

[0137] The correction-deviation distance refers to the minimum distance between the actual position of the rigging and the planned path after the rigging position has been corrected. It reflects the positioning accuracy after correction. When the correction-deviation distance is large, it indicates that even after correction, the positioning accuracy of the rigging is still not up to standard. At this time, external interference may cause the initial accurate coordinates to be inaccurate, thus affecting the correction and subsequent judgment. In this case, the system needs to take further measures, such as expanding the activation range, resynchronizing the clock, and adjusting the preprocessing parameters, to optimize the positioning accuracy and ensure that the rigging can run accurately according to the planned path.

[0138] Specifically, the process by which the optimization module determines whether the positioning is qualified based on the deviation distance after correction includes:

[0139] The optimization module obtains the deviation distance after correction and determines the correction-deviation distance (CDD).

[0140] The corrected deviation distance CDD is compared with the preset deviation distance DD1;

[0141] If the correction-deviation distance CDD is less than or equal to the preset deviation distance DD1, the optimization module determines that the positioning accuracy is qualified and maintains the system parameters;

[0142] If the corrected deviation distance CDD is greater than the preset deviation distance DD1, the optimization module determines that the positioning accuracy is unqualified. The optimization module constructs a wide activation range based on 3 times the initial activation radius, and activates the ultra-wideband base station within the wide activation range to redetermine the corresponding deviation distance, which is recorded as the wide range deviation. The reason for the unqualification is determined based on the ratio of the wide range deviation to the deviation distance.

[0143] Furthermore, the optimization module is also used to construct a wide activation range based on 3 times the initial activation radius, and to activate the ultra-wideband base stations within the wide activation range to redetermine the wide range deviation. It also determines the extended deviation range ratio based on the ratio of the wide range deviation to the deviation distance, and judges the reasons for non-compliance based on the extended deviation range ratio. Additionally, when the WiFi signal environment is determined to be unstable, it corrects the radius selection threshold based on the ratio of the preset extended deviation range ratio to the extended deviation range ratio, or constructs a triangle between the rigging and any two activated ultra-wideband base stations, and judges the cause of the anomaly based on the proportion of triangles that do not satisfy the triangle inequality among all triangles.

[0144] The extended deviation range ratio is determined by comparing the ratio of the wide-range deviation to the deviation distance. It reflects the relative change between the recalculated deviation distance and the original deviation distance after expanding the activation range. When the activation range is expanded, that is, when more ultra-wideband base stations are connected, the extended deviation range ratio decreases, indicating that the positioning accuracy has been significantly improved after expanding the activation range. It can be inferred that the unstable WiFi signal environment is the main reason for the unqualified positioning accuracy. By using the extended deviation range ratio, the root cause of the problem can be accurately located, improving positioning accuracy and reliability, ensuring that the rigging always runs according to the planned path, and improving the safety and efficiency of the operation.

[0145] Specifically, the process by which the optimization module determines the cause of non-compliance based on the ratio of the extended deviation range includes:

[0146] The optimization module constructs a wide activation range based on 3 times the initial activation radius, and activates the ultra-wideband base station within the wide activation range to redetermine the wide range deviation;

[0147] The optimization module determines the extended deviation range ratio ED based on the ratio of the wide-range deviation to the deviation distance;

[0148] The optimization module compares the extended deviation range ratio ED with the preset extended deviation range ratio ED1, and sets the preset extended deviation range ratio ED1 ∈ [0.9.1].

[0149] If the extended deviation range ratio ED is less than or equal to the preset extended deviation range ratio ED1, the optimization module determines that the WiFi signal environment is unstable, and the optimization module corrects the radius selection threshold based on the ratio of the preset extended deviation range ratio to the extended deviation range ratio.

[0150] If the ratio of the extended deviation range ED is greater than the preset ratio of the extended deviation range ED1, the optimization module will construct a triangle with the rigging and any two activated ultra-wideband base stations, and determine the cause of the anomaly based on the proportion of triangles that do not satisfy the triangle inequality among all triangles.

[0151] Furthermore, the optimization module is also used to determine the expansion deviation range ratio based on the ratio of the preset expansion deviation range ratio to the expansion deviation range ratio, and to reduce the radius selection threshold based on the expansion deviation range ratio, wherein the reduction of the radius selection threshold is proportional to the expansion deviation range ratio.

[0152] By adjusting the radius selection threshold when the wireless signal environment is unstable, the system can expand the range of the selected fingerprint, constructing a wider activation area. This reduces positioning errors caused by signal instability and improves positioning accuracy. The expanded deviation range ratio reflects the relative difference between the improved positioning accuracy after expanding the activation range and the preset target. The improvement in positioning accuracy after expanding the activation range is evaluated by calculating the expanded deviation range ratio, and the radius selection threshold is dynamically adjusted based on this ratio to ensure that the adjustment range matches environmental changes, further optimizing positioning performance. This design improves the system's adaptability and robustness, optimizes resource utilization, enhances the system's versatility and adaptability, enables the system to better cope with complex environments, ensures that the rigging always runs along the planned path, and improves operational safety and efficiency.

[0153] Specifically, the optimization module's process of reducing the radius selection threshold based on the ratio of the expanded deviation range includes:

[0154] The optimization module determines the expansion deviation range ratio (EDR) based on the ratio of the preset expansion deviation range ratio to the expansion deviation range ratio.

[0155] The optimization module compares the extended deviation range ratio EDR with the set first preset extended deviation range ratio EDR1 and second preset extended deviation range ratio EDR2, wherein the first preset extended deviation range ratio EDR1 is set to [1.2, 1.5] and the second preset extended deviation range ratio EDR2 is set to (1.5, 2].

[0156] If the expanded deviation range ratio EDR is less than or equal to the first preset expanded deviation range ratio EDR1, then the optimization module uses a first selected correction threshold α1 to correct the radius selection threshold RST. The corrected radius selection threshold RST' = RST × α1, where the first selected correction threshold α1 is set to 0.97.

[0157] If the expanded deviation range ratio EDR is greater than the first preset expanded deviation range ratio EDR1 and less than or equal to the second preset expanded deviation range ratio EDR2, then the optimization module uses a second selected correction threshold α2 to correct the radius selection threshold RST. The corrected radius selection threshold RST' = RST × α2, where the second selected correction threshold α2 is set to 0.92.

[0158] If the extended deviation range ratio EDR is greater than the second preset extended deviation range ratio EDR2, the optimization module uses a third selected correction threshold α3 to correct the radius selection threshold RST. The corrected radius selection threshold RST' = RST × α3, where the third selected correction threshold α3 is set to 0.95.

[0159] Furthermore, the optimization module is also used to construct the triangle with the rigging and any two of the activated ultra-wideband base stations, and to determine the proportion of abnormal triangles based on the proportion of triangles that do not satisfy the triangle inequality in all triangles, and to determine the cause of the abnormality based on the proportion of abnormal triangles, and when it is determined that dynamic occlusion is frequent, to correct the preset radius multiplier based on the ratio of the preset abnormal triangle proportion to the abnormal triangle proportion, or, when it is determined that clock drift is caused, to correct the absolute value of the effective range threshold based on the ratio of the preset deviation distance to the deviation distance;

[0160] The triangle inequality is a fundamental physical constraint in geometric positioning. The distance between any three points must satisfy this condition. If the measured data does not satisfy the triangle inequality, it indicates that there may be an error. The proportion of abnormal triangles reflects the proportion of data that does not conform to the geometric constraints. A large proportion usually means that the current error is a global error, which may be caused by clock drift. A small proportion may indicate a local error, i.e., due to frequent dynamic occlusion by objects around the rigging. By judging the cause of the abnormality through the proportion of abnormal triangles, the system can accurately locate the root cause of the problem, distinguish different types of error sources, and take targeted corrective measures. This method improves the system's adaptability and robustness, optimizes resource utilization, enhances the system's versatility and adaptability, enables the system to better cope with complex environments, ensures that the rigging always runs along the planned path, and improves the safety and efficiency of the operation.

[0161] Please see Figure 4 As shown, this is a flowchart illustrating the process of determining the cause of anomalies based on the proportion of the anomaly triangle in an embodiment of the present invention. The process of the optimization module in the present invention determining the cause of anomalies based on the proportion of the anomaly triangle includes:

[0162] The optimization module constructs the triangle with the rigging and any two of the activated ultra-wideband base stations, and determines the proportion of abnormal triangles (ATP) based on the proportion of triangles that do not satisfy the triangle inequality among all triangles.

[0163] The abnormal triangle ratio ATP is compared with the preset abnormal triangle ratio ATP1, wherein the preset abnormal triangle ratio ATP1 is set to [0.7, 0.9].

[0164] If the abnormal triangle ratio ATP is less than or equal to the preset abnormal triangle ratio ATP1, the optimization module determines that dynamic occlusion is frequent, and the optimization module corrects the preset radius ratio based on the ratio of the preset abnormal triangle ratio to the abnormal triangle ratio.

[0165] If the percentage of abnormal triangles (ATP) is greater than the preset percentage of abnormal triangles (ATP1), the optimization module determines that it is a clock drift, and the optimization module corrects the absolute value of the effective range threshold based on the ratio of the preset deviation distance to the deviation distance.

[0166] Furthermore, the optimization module is also used to determine the abnormal triangle ratio based on the ratio of the preset abnormal triangle ratio to the bathing area triangle ratio, and to increase the preset radius multiple based on the abnormal triangle ratio, wherein the increase in the preset radius multiple is proportional to the abnormal triangle ratio.

[0167] Dynamic occlusion refers to the frequent passage of moving objects such as people, machinery, and containers between the rigging and the ultra-wideband (UWB) base station, causing obstruction or reflection of the signal propagation path, thus affecting the stability and accuracy of signal ranging. Dynamic occlusion can make the signals received by some base stations unstable or even lost, resulting in abnormal ranging data. By increasing the preset radius multiplier, the system can activate more base stations, increasing ranging redundancy and diversity, thereby improving anti-interference capability. The abnormal triangle ratio reflects the severity of current environmental interference and serves as the basis for adjusting the preset radius multiplier, ensuring the system's response to abnormal conditions. By making the adjustment range of the preset radius multiplier proportional to the abnormal triangle ratio, the system achieves dynamic self-adaptation based on interference conditions, ensuring a balance between positioning accuracy and resource utilization. This design improves the system's adaptability and robustness, optimizes resource utilization, enhances the system's versatility and adaptability, enables the system to better cope with complex environments, ensures that the rigging always runs along the planned path, and improves the safety and efficiency of operations.

[0168] Specifically, the process by which the optimization module increases the preset radius ratio based on the abnormal triangle ratio includes:

[0169] The optimization module determines the abnormal triangle ratio ATPR based on the ratio of the preset abnormal triangle ratio to the bathing area triangle ratio.

[0170] The anomalous triangle ratio ATPR is compared with a first preset anomalous triangle ratio ATPR1 and a second preset anomalous triangle ratio ATPR2, wherein the first preset anomalous triangle ratio ATPR1 is set to [1, 1, 2] and the second preset anomalous triangle ratio ATPR2 is set to (2, 3).

[0171] If the abnormal triangle ratio ATPR is less than or equal to the first preset abnormal triangle ratio ATPR1, then the optimization module uses a first ratio correction threshold β1 to correct the preset radius ratio AR. The corrected preset radius ratio AR' = AR × β1, where the first ratio correction threshold β1 is set to 1.05.

[0172] If the abnormal triangle ratio ATPR is greater than the first preset abnormal triangle ratio ATPR1 and less than or equal to the second preset abnormal triangle ratio ATPR2, then the optimization module uses a second ratio correction threshold β2 to correct the preset radius ratio AR. The corrected preset radius ratio AR' = AR × β2, where the second ratio correction threshold β2 is set to 1.11.

[0173] If the abnormal triangle ratio ATPR is greater than the second preset abnormal triangle ratio ATPR2, the optimization module uses the third ratio correction threshold β3 to correct the preset radius ratio AR. The corrected preset radius ratio AR' = AR × β3, where the third ratio correction threshold β3 is set to 1.2.

[0174] Furthermore, the optimization module is also used to determine the multiplier difference based on the difference in the increase of the preset radius multiplier before and after the correction, and to extend the sampling period based on the multiplier difference, wherein the extension of the sampling period is proportional to the multiplier difference.

[0175] As the preset radius multiplier increases, the system simultaneously activates more ultra-wideband (UWB) base stations to expand the positioning coverage and improve the robustness and accuracy of positioning. However, with more base stations activated, the acquisition module needs to process more distance measurement data in each sampling period, significantly increasing the data volume. Extending the sampling period allows the system sufficient time to acquire reliable and complete data from all activated base stations, avoiding positioning errors caused by incomplete or conflicting data. The multiplier difference reflects the change in the activation range. By extending the sampling period based on the multiplier difference, and with the extension of the sampling period being proportional to the multiplier difference, sufficient data acquisition time is achieved when expanding the positioning range, ensuring positioning accuracy and system stability, while also considering response speed and resource utilization efficiency. This design reflects the system's adaptive adjustment and optimization capabilities, avoiding insufficient acquisition time due to a surge in data volume caused by the expansion of the activation range, improving data integrity and accuracy, reducing system resource waste, enhancing positioning accuracy and stability, and strengthening the system's robustness and adaptability, enabling the system to better cope with complex and changing signal environments.

[0176] Specifically, the process by which the optimization module extends the sampling period based on the multiplier difference includes:

[0177] The optimization module determines the multiplier difference RD based on the difference in the increase of the preset radius multiplier before and after the correction.

[0178] The magnification difference RD is compared with the set first preset magnification difference RD1 and second preset magnification difference RD2, wherein the first preset magnification difference RD1 is set to [0.15, 1] ​​and the second preset magnification difference RD2 is set to (1, 2].

[0179] If the magnification difference RD is less than or equal to the first preset magnification difference RD1, the optimization module uses a first period correction threshold θ1 to correct the sampling period SP, and the corrected sampling period SP' = SP × θ1, wherein the first period correction threshold θ1 is set to 1.03;

[0180] If the magnification difference RD is greater than the first preset magnification difference RD1 and less than or equal to the second preset magnification difference RD2, then the optimization module uses the second period correction threshold θ2 to correct the sampling period SP. The corrected sampling period SP' = SP × θ2, where the second period correction threshold θ2 is set to 1.08.

[0181] If the magnification difference RD is greater than the second preset magnification difference RD2, the optimization module uses the third period correction threshold θ3 to correct the sampling period SP. The corrected sampling period SP' = SP × θ3, where the third period correction threshold θ3 is set to 1.15.

[0182] Furthermore, the optimization module is also used to determine the deviation distance ratio based on the preset deviation distance and the correction-deviation distance ratio, and to reduce the absolute value of the effective range threshold based on the deviation distance ratio, wherein the reduction in the absolute value of the effective range threshold is proportional to the deviation distance ratio.

[0183] In ultra-wideband (UWB) positioning systems, distance measurement between base stations and tags is highly dependent on clock synchronization accuracy. Clock drift can lead to inaccurate measurement timestamps, resulting in ranging errors. These errors manifest as noise in the rigging-base station distance data. The deviation-distance ratio reflects the amplification of the positioning error relative to the desired reference. By reducing the absolute value of the effective range threshold based on the deviation-distance ratio, and with the reduction in the absolute value of the effective range threshold being proportional to the deviation-distance ratio, effective filtering of ranging anomalies and adaptive assurance of positioning accuracy are achieved. This design not only ensures the stability and accuracy of the system but also improves the system's intelligent adjustment capability in complex environments, reduces manual intervention and maintenance costs, lowers the risk of misdiagnosis, and enhances data reliability and positioning accuracy.

[0184] Specifically, the optimization module's process of reducing the absolute value of the effective range threshold based on the deviation distance ratio includes:

[0185] The optimization module determines the deviation distance ratio DDR based on the preset deviation distance and the correction-deviation distance ratio.

[0186] The deviation distance ratio DDR is compared with the set first preset deviation distance ratio DDR1 and second preset deviation distance ratio DDR2, wherein the first preset deviation distance ratio DDR1 is set to (1, 2] and the second preset deviation distance ratio DDR2 is set to (2, 4].

[0187] If the deviation distance ratio DDR is less than or equal to the first preset deviation distance ratio DDR1, the optimization module uses a first effective range correction threshold η1 to correct the effective range threshold ERT. The corrected effective range threshold ERT' = ERT × η1, where the first effective range correction threshold η1 is set to 0.98.

[0188] If the deviation distance ratio DDR is greater than the first preset deviation distance ratio DDR1 and less than or equal to the second preset deviation distance ratio DDR2, then the optimization module uses a second effective range correction threshold η2 to correct the effective range threshold ERT. The corrected effective range threshold ERT' = ERT × η2, where the second effective range correction threshold η2 is set to 0.95.

[0189] If the deviation distance ratio DDR is greater than the second preset deviation distance ratio DDR2, the optimization module uses a third effective range correction threshold η3 to correct the effective range threshold ERT. The corrected effective range threshold ERT' = ERT × η3, where the third effective range correction threshold η3 is set to 0.91.

[0190] In summary, this invention proposes an intelligent assisted positioning system for rigging. It achieves high-precision positioning by periodically collecting rigging fingerprints, planned paths, and rigging-base station distance datasets, and combining multiple modules (such as a preprocessing module, WiFi module, activation module, clock module, ultra-wideband tag module, accuracy evaluation module, optimization module, control module, and notification module). The system optimizes resource utilization and improves positioning accuracy and stability by dynamically adjusting parameters such as the ratio of extended deviation ranges, the proportion of abnormal triangulation, sampling period, and effective range threshold. This design not only enhances the system's adaptability and robustness but also strengthens its versatility and adaptability, making it suitable for various complex environments and providing reliable technical support for the precise positioning of rigging.

[0191] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An intelligent assisted positioning system for rigging, characterized in that, include, The data acquisition module is used to periodically collect rigging fingerprints, planned paths, coordinates of activated ultra-wideband base stations, and corresponding rigging-base station distance datasets. A preprocessing module, connected to the acquisition module, is used to reduce noise in the rigging-base station distance dataset based on the absolute value of the effective range threshold. A WiFi module, connected to the acquisition module, is used to determine the rigging fingerprint and initial activation radius in the WiFi fingerprint database based on the rigging fingerprint and a radius selection threshold. An activation module, which is connected to the WiFi module, is used to construct an activation range and activate the corresponding ultra-wideband base station based on the activation radius and the rigging fingerprint. The activation radius is the product of the initial activation radius and the preset radius multiple. A clock module, which is connected to the activation module, is used to activate the ultra-wideband base station clock based on the rigging master clock. An ultra-wideband tag module, which is connected to the preprocessing module and the clock module, is used to construct a polygonal positioning geometric model and determine precise coordinates based on the rigging-base station distance dataset; The accuracy evaluation module, which is connected to the ultra-wideband tag module, is used to determine the deviation distance based on the minimum distance from the precise coordinates to the planned path, and to determine whether the rigging has deviated based on the deviation distance, and to issue a correction notification command when a deviation is determined, or to maintain the parameters when no deviation is determined. An optimization module, connected to an accuracy evaluation module, is used to determine whether the positioning is qualified based on the deviation distance after correction. When the positioning is deemed unqualified, a wide activation range is constructed based on 3 times the initial activation radius, and the ultra-wideband base station within the wide activation range is activated to redetermine the wide range deviation. The optimization module determines the extended deviation range ratio ED based on the ratio of the wide range deviation to the deviation distance, and compares the extended deviation range ratio ED with the preset extended deviation range ratio ED1. If the extended deviation range ratio ED is less than or equal to the preset extended deviation range ratio ED1, the optimization module determines that the WiFi signal environment is unstable, and the optimization module adjusts the radius selection threshold based on the ratio of the preset extended deviation range ratio to the extended deviation range ratio. If the ratio of the extended deviation range ED is greater than the preset ratio of the extended deviation range ED1, the optimization module will construct a triangle with the rigging and any two activated ultra-wideband base stations, and determine the cause of the anomaly based on the proportion of triangles that do not satisfy the triangle inequality in all triangles. The optimization module is also used to determine the cause of the anomaly based on the proportion of the abnormal triangle, including: the optimization module constructs the triangle with the rigging and any two of the activated ultra-wideband base stations; and determines the proportion of the abnormal triangle ATP based on the proportion of triangles that do not satisfy the triangle inequality in all triangles, and compares the proportion of the abnormal triangle ATP with the preset proportion of the abnormal triangle ATP1. If the abnormal triangle ratio ATP is less than or equal to the preset abnormal triangle ratio ATP1, the optimization module determines that dynamic occlusion is frequent, and the optimization module corrects the preset radius ratio based on the ratio of the preset abnormal triangle ratio to the abnormal triangle ratio. If the percentage of abnormal triangles ATP is greater than the preset percentage of abnormal triangles ATP1, the optimization module determines that it is a clock drift, and the optimization module corrects the absolute value of the effective range threshold based on the ratio of the preset deviation distance to the deviation distance. The optimization module is also used to correct the collection cycle based on the reasons for non-compliance and issue corresponding correction instructions; A control module, which is connected to the optimization module, the acquisition module, the preprocessing module and the WiFi module, is used to correct the corresponding module based on the instructions of the optimization module; The notification module, which is connected to the accuracy evaluation module, is used to issue a notification based on the correction notification instruction; Wherein, the initial activation radius refers to the maximum distance between the selected fingerprint and the rigging fingerprint among several selected fingerprints selected based on the radius selection threshold in the WiFi fingerprint database; The rigging fingerprint refers to the set of WiFi signal features collected corresponding to the rigging; The radius selection threshold refers to the minimum similarity that needs to be achieved between the fingerprint in the WiFi fingerprint database and the rigging fingerprint when the fingerprint is determined to be the selected fingerprint in the WiFi fingerprint database.

2. The intelligent assisted positioning system according to claim 1, characterized in that, The optimization module is used to determine the correction-deviation distance based on the deviation distance after correction, and to determine whether the positioning is qualified based on the correction-deviation distance, and to maintain the system parameters when qualified, or to determine the reason for the failure based on the ratio of the wide range deviation to the deviation distance when unqualified. The selected fingerprint refers to a fingerprint in the WiFi fingerprint database whose similarity to the rigging fingerprint is greater than or equal to the radius selection threshold. The WiFi fingerprint database refers to a database of WiFi signal feature sets corresponding to various location points that have been pre-collected and stored.

3. The intelligent assisted positioning system according to claim 1, characterized in that, The optimization module is also used to construct the triangle with the rigging and any two of the activated ultra-wideband base stations, and to determine the proportion of abnormal triangles based on the proportion of triangles that do not satisfy the triangle inequality in all triangles, and to determine the cause of the abnormality based on the proportion of abnormal triangles, and when it is determined that dynamic occlusion is frequent, to correct the preset radius multiplier based on the ratio of the preset abnormal triangle proportion to the abnormal triangle proportion, or, when it is determined that clock drift is caused, to correct the absolute value of the effective range threshold based on the ratio of the preset deviation distance to the deviation distance; The absolute value of the effective range threshold refers to the absolute value of the maximum deviation of the distance value relative to the average distance value in the rigging-base station distance dataset when the distance value in the rigging-base station distance dataset is determined to be valid, based on the average distance value. The rigging-base station distance dataset refers to the dataset collected by the acquisition module that includes distance results between the rigging and the activated ultra-wideband base station at multiple time points.

4. The intelligent assisted positioning system according to claim 3, characterized in that, The optimization module is also used to determine the abnormal triangle ratio based on the ratio of the preset abnormal triangle ratio to the abnormal triangle ratio, and to increase the preset radius multiple based on the abnormal triangle ratio, wherein the increase in the preset radius multiple is proportional to the abnormal triangle ratio.

5. The intelligent assisted positioning system according to claim 4, characterized in that, The optimization module is also used to determine the multiplier difference based on the difference in the increase of the preset radius multiplier before and after the correction, and to extend the sampling period based on the multiplier difference, wherein the extension of the sampling period is proportional to the multiplier difference.

6. The intelligent assisted positioning system according to claim 3, characterized in that, The optimization module is also used to determine the deviation distance ratio based on the preset deviation distance and the correction-deviation distance ratio, and to reduce the absolute value of the effective range threshold based on the deviation distance ratio, wherein the reduction in the absolute value of the effective range threshold is proportional to the deviation distance ratio.

7. An intelligent assisted positioning method for rigging based on the intelligent assisted positioning system according to any one of claims 1-6, characterized in that, include, Periodically collect rigging fingerprints, planned paths, and several rigging-base station distance datasets; Denoising the rigging-base station distance dataset based on the absolute value of the effective range threshold; Based on the rigging fingerprint and the radius selection threshold, determine the rigging fingerprint and the initial activation radius in the WiFi fingerprint database; The activation range is constructed based on the activation radius and the rigging fingerprint, and the corresponding ultra-wideband base station is activated. The activation radius is the product of the initial activation radius and the preset radius multiple. Ultra-wideband base station clock based on rigging master clock synchronization activation; Based on the rigging-base station distance dataset, a polygonal positioning geometric model is constructed and precise coordinates are determined. The deviation distance is determined based on the minimum distance from the precise coordinates to the planned path, and the deviation distance is used to determine whether the rigging has deviated, and a correction notification instruction is issued when a deviation is determined, or the parameters are maintained when no deviation is determined. The positioning is determined to be qualified based on the deviation distance after correction. If it is determined to be unqualified, a wide activation range is constructed based on 3 times the initial activation radius, and the ultra-wideband base station within the wide activation range is activated to redetermine the corresponding deviation distance, which is recorded as the wide range deviation. The reason for the unqualification is determined based on the ratio of the wide range deviation to the deviation distance. The activation range construction parameters, clock synchronization parameters, and preprocessing parameters are corrected based on the reason for the unqualification.

8. A hook based on the intelligent assisted positioning system according to any one of claims 1-6, characterized in that, include, The lifting ring (1), the control body (2), the hook (3), and the safety buckle (4); The lifting ring (1) is disposed above the control body (2) and is fixedly connected to the control body (2) for external connection of the hook; The control body (2) is provided with a safety buckle (4) below and is fixedly connected to the safety buckle (4). The control body (2) includes the acquisition module, the preprocessing module, the WiFi module, the activation module, the clock module, the ultra-wideband tag module, the accuracy evaluation module, the optimization module, the control module and the notification module. The acquisition module is used to periodically acquire hook fingerprints, planned paths, and several hook-base station distance datasets; The preprocessing module, which is connected to the acquisition module, is used to reduce noise in the hook-base station distance dataset based on the absolute value of the effective range threshold. The WiFi module is connected to the acquisition module and is used to determine the hook fingerprint and the initial activation radius in the WiFi fingerprint database based on the hook fingerprint and the radius selection threshold. The activation module is connected to the WiFi module and is used to construct an activation range and activate the corresponding ultra-wideband base station based on the activation radius and the hook fingerprint. The activation radius is the product of the initial activation radius and the preset radius multiple. The clock module, which is connected to the activation module, is used to activate the ultra-wideband base station clock based on the hook master clock. The ultra-wideband tag module is connected to the preprocessing module and the clock module, and is used to construct a polygonal positioning geometric model and determine precise coordinates based on the hook-base station distance dataset. The accuracy evaluation module is connected to the ultra-wideband tag module and is used to determine the deviation distance based on the minimum distance from the precise coordinates to the planned path, and to determine whether the hook has deviated based on the deviation distance, and to issue a correction notification command when a deviation is determined, or to maintain the parameters when no deviation is determined. The optimization module, connected to the accuracy evaluation module, is used to determine whether the positioning is qualified based on the deviation distance after correction, and when it is determined to be unqualified, to construct a wide activation range based on 3 times the initial activation radius, and to activate the ultra-wideband base station within the wide activation range to redetermine the corresponding deviation distance as the wide range deviation, to determine the reason for the unqualification based on the ratio of the wide range deviation to the deviation distance, and to correct the activation range construction parameters, acquisition cycle and preprocessing parameters based on the reason for the unqualification and to issue a corresponding correction command. The control module is connected to the optimization module, the preprocessing module, the WiFi module, the activation module, and the clock module, and is used to modify the corresponding module based on the instructions of the optimization module. The notification module, which is connected to the accuracy evaluation module, is used to issue a notification based on the correction notification instruction; The safety buckle (4) is located at the end of the hook body (3) to fasten the hook body (3) to the control body (2) and prevent slippage.

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