Port hoisting equipment health monitoring method and system

Abstract

The invention discloses a health monitoring method and system for port hoisting equipment, and particularly relates to the technical field of port hoisting equipment. Comprising an unmanned aerial vehicle inspection module, a hoisting equipment appearance monitoring module, a hoisting equipment data acquisition module, a hoisting equipment health state feature processing module, a hoisting equipment health fusion analysis module, a hoisting equipment anomaly detection module and a digital twin modeling module. The comprehensive evaluation index, load fluctuation, height control error change rate and steel wire rope bending health degree evaluation characteristics of the appearance defects of the hoisting equipment are calculated, health state characteristic early warning is established, the health evaluation coefficient of the hoisting equipment is further obtained, comprehensive monitoring of the equipment appearance and the operation state is achieved, the coverage range of data acquisition is widened, and the safety of the equipment is improved. The quantitative evaluation and comparison mode makes the judgment of the health state of the equipment more objective, and makes the formulation of a maintenance plan more scientific and reasonable by performing analysis and judgment based on data.

Description

Technical Field

[0001] The present invention relates to the technical field of port lifting equipment, and more specifically, to a health monitoring method and system for port lifting equipment. Background Art

[0002] With the continuous expansion of the global trade scale and the popularization of containerized transportation modes, the quantity and usage frequency of port lifting equipment have been continuously increasing, which puts forward higher requirements for the reliability and health status of the equipment. As the core equipment in the modern port logistics system, port lifting equipment undertakes key tasks such as cargo loading, unloading, handling, and storage, and its operating efficiency and safety directly affect the overall operation ability of the port and the timeliness of cargo transportation.

[0003] In recent years, with the development of sensor technology, data acquisition and transmission technology, and intelligent analysis algorithms, health management based on equipment condition monitoring has gradually become a research hotspot. By installing a variety of sensors on the equipment to collect parameter information such as vibration, temperature, pressure, and current in real time, and combining big data analysis means to evaluate the operating state of the equipment, so as to achieve early fault warning and accurate maintenance decision-making.

[0004] However, when it is actually used, there are still some drawbacks. For example, the special working environment of port lifting equipment also brings various external interferences to health monitoring. The existing health monitoring methods for port lifting equipment still have deficiencies in data acquisition, lack of self-learning ability, and it is difficult to achieve the early monitoring accuracy and response speed for potential faults; In practical applications, due to being in a high-intensity and high-load working environment for a long time, traditional lifting equipment monitoring methods rely on regular maintenance, often requiring the suspension of equipment operation, which directly affects the production efficiency of the port. In addition, it is impossible to accurately evaluate the wear condition of key components of the equipment, and it is impossible to achieve early fault warning, thus triggering safety accidents. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a health monitoring method and system for port lifting equipment to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solution: A health monitoring system for port lifting equipment, comprising: An unmanned aerial vehicle (UAV) inspection module: demarcate a UAV inspection area in the port, adjust the shooting angle and distance according to the equipment structure through adaptive flight path planning technology, and collect equipment appearance images every t time periods.

[0007] Lifting equipment appearance monitoring module: Obtain all defect information in the appearance image of the lifting equipment collected in the i-th time period, calculate the comprehensive evaluation index of the appearance defects of the lifting equipment in the i-th time period through the defect area, and process it.

[0008] Lifting equipment data acquisition module: Collect the equipment operation parameters of each operation cycle of the lifting equipment through sensors. The operation cycle is one cycle for a single lifting, and the equipment operation parameters include the lifting weight, lifting height, and micro-bending degree of the steel wire rope.

[0009] Lifting equipment health status feature processing module: Used to obtain the equipment operation parameters of each operation cycle of the lifting equipment, calculate the load fluctuation, height control error change rate, and steel wire rope bending health assessment features of the lifting equipment, and establish a health status feature warning.

[0010] Lifting equipment health fusion analysis module: Used to obtain the comprehensive evaluation index of the appearance defects of the lifting equipment, load fluctuation, height control error change rate, and steel wire rope bending health assessment of the lifting equipment, and obtain the health assessment coefficient of the lifting equipment according to the equipment health assessment model.

[0011] Lifting equipment anomaly detection module: Obtain the health assessment coefficient of the lifting equipment, compare it with the preset health assessment coefficient, and classify the state level of the lifting equipment according to the deviation of the health assessment coefficient of the lifting equipment.

[0012] Digital twin modeling module: Build a virtual model based on the actual physical structure and operation parameters of the lifting equipment, and update the operation status and health status of the equipment by receiving equipment appearance images and equipment operation parameter data in real time.

[0013] Preferably, the implementation steps of the UAV inspection module are specifically as follows: S21: Design an inspection route for each lifting equipment through adaptive flight path planning technology in the port area designated for UAV inspection, and set the take-off point, inspection path, and return point. S22: Equip the UAV with a high-resolution camera, collect the appearance images of the lifting equipment every t time periods, and at the same time monitor the ambient light intensity through the light sensor in the environmental perception unit of the UAV. According to the preset light intensity - light source adjustment strategy, flexibly adjust the light source angle and intensity, and sequentially number each time period as 1, 2,... i,... n. S23: Establish a normal state image database and a defect state image database, match the collected equipment appearance images with the normal state image database and the defect state image database, traverse all defect information in the lifting equipment appearance images, and perform defect data annotation.

[0014] Preferably, the adaptive flight path planning technology is based on the distribution of key monitoring points of the equipment structure, and combines the navigation function of the three-dimensional geographic information system to plan the inspection flight route of the UAV, ensuring comprehensive and efficient inspection coverage.

[0015] Preferably, the implementation steps of the lifting equipment appearance monitoring module are specifically as follows: S41: Obtain all defect information in the lifting equipment appearance image collected in the i-th time period, and extract the area of each defect and the position of the lifting equipment in the image; S42: Based on the area of each defect in the lifting equipment appearance image in the i-th time period, calculate the comprehensive evaluation index of the lifting equipment appearance defect in the i-th time period according to the set appearance defect evaluation model; S43: Obtain the comprehensive evaluation index of the lifting equipment appearance defect in the i-th time period, and compare it with the preset comprehensive evaluation index of the lifting equipment appearance defect. If the comprehensive evaluation index of the lifting equipment appearance defect at this moment is greater than the preset comprehensive evaluation index of the lifting equipment appearance defect, it indicates that the appearance defect of the lifting equipment is serious at this moment. Extract the position of the lifting equipment corresponding to the lifting equipment appearance image at this moment, and send the lifting equipment appearance image to the maintenance personnel for maintenance operations. Otherwise, it indicates that the appearance monitoring of the lifting equipment is better at this moment.

[0016] Preferably, the lifting equipment data acquisition module is specifically as follows: Through the strain type weighing sensor installed on the lifting equipment, the lifting weight borne by the equipment during operation is collected every t time periods according to the operation cycle; through the position sensor installed on the lifting equipment, the vertical displacement during the equipment operation is collected every t time periods according to the operation cycle to obtain the lifting height; through the wire rope micro-bending detection laser sensor installed on the lifting equipment, the wire rope micro-bending during the equipment operation is collected every t time periods according to the operation cycle.

[0017] Preferably, the implementation steps of the lifting equipment health status feature processing module are specifically as follows: S61: Load fluctuation feature: By calculating the mean and standard deviation of the lifting weight of each operation cycle of the lifting equipment, and according to the ratio of the standard deviation to the mean of the lifting weight, calculate the load fluctuation of each operation cycle of the lifting equipment. By obtaining the load fluctuation of each operation cycle of the lifting equipment and comparing it with the preset load fluctuation, if the load fluctuation of a certain operation cycle is greater than the preset load fluctuation, it indicates that the load of the lifting equipment fluctuates abnormally, and the management personnel should be notified to check the equipment. Otherwise, it indicates that the load of the lifting equipment has no abnormality; S62: Feature of height control error change rate: Calculate the height control error change rate of each operation cycle of the lifting equipment based on the lifting height and the target height of each operation cycle of the lifting equipment. By obtaining the height control error change rate of each operation cycle of the lifting equipment and comparing it with the preset height control error change rate, if the height control error change rate of a certain operation cycle is greater than the preset height control error change rate, it indicates a fault in the control system of the lifting equipment, and the management personnel should be notified to check the equipment. Otherwise, it indicates that there is no abnormality in the control system of the lifting equipment; S63: Feature of wire rope bending health assessment: Collect the micro-bending degree of the wire rope during the operation of the equipment every t time periods according to the operation cycle. Calculate the wire rope bending health assessment of the lifting equipment based on the micro-bending degree and the critical bending degree of the wire rope of the lifting equipment. By obtaining the wire rope bending health assessment of the lifting equipment and comparing it with the preset wire rope bending health assessment, if the wire rope bending health assessment of a certain time period is less than the preset wire rope bending health assessment and greater than or equal to 0, it indicates that the wire rope of the lifting equipment is damaged, and the management personnel should be notified to check the equipment. If the wire rope bending health assessment of a certain time period is less than 0, it indicates that the wire rope bending health of the lifting equipment exceeds the safety limit, and an emergency stop should be carried out immediately. Otherwise, it indicates that there is no abnormality in the wire rope bending health assessment of the lifting equipment.

[0018] Preferably, the implementation steps of the lifting equipment health fusion analysis module are specifically as follows: S71: Obtain the comprehensive evaluation index of the appearance defects of the lifting equipment in each time period, the load fluctuation in each operation cycle, the height control error change rate in each operation cycle, and the wire rope bending health assessment in each operation cycle time period, and perform parameter normalization processing; S72: Obtain the comprehensive evaluation index of the appearance defects of the lifting equipment, the load fluctuation, the height control error change rate, and the wire rope bending health assessment after normalization processing, and obtain the health evaluation coefficient of the lifting equipment based on the set equipment health evaluation model.

[0019] Preferably, the lifting equipment anomaly detection module is specifically as follows: Obtain the health evaluation coefficient of the lifting equipment, compare it with the preset health evaluation coefficient to obtain the deviation of the health evaluation coefficient of the lifting equipment, match the deviation value of the health evaluation coefficient with the lifting equipment status level division standard, divide the lifting equipment into four equipment status levels: healthy, warning, abnormal, and faulty, and push the equipment status level of the lifting equipment to the relevant personnel for maintenance of the lifting equipment.

[0020] Preferably, a port lifting equipment health monitoring method includes the following steps: Step S01: Define a drone inspection area at the port. Adjust the shooting angle and distance according to the equipment structure through adaptive flight path planning technology, and collect equipment appearance images every t time periods. Step S02: Obtain all defect information in the appearance image of the lifting equipment collected in the i-th time period. Calculate the comprehensive evaluation index of the appearance defects of the lifting equipment in the i-th time period through the defect area, and process it. Step S03: Collect the equipment operation parameters of each operation cycle of the lifting equipment through sensors. The operation cycle is one cycle for a single lift, and the equipment operation parameters include the lifting weight, lifting height, and micro-bending degree of the wire rope. Step S04: Used to obtain the equipment operation parameters of each operation cycle of the lifting equipment, calculate the load fluctuation, height control error change rate, and wire rope bending health assessment characteristics of the lifting equipment, and establish a health status characteristic warning. Step S05: Used to obtain the comprehensive evaluation index of the appearance defects of the lifting equipment, load fluctuation, height control error change rate, and wire rope bending health assessment of the lifting equipment, and obtain the health assessment coefficient of the lifting equipment according to the equipment health assessment model. Step S06: Obtain the health assessment coefficient of the lifting equipment, compare it with the preset health assessment coefficient, and classify the state level of the lifting equipment according to the deviation of the health assessment coefficient of the lifting equipment. Step S07: Construct a virtual model based on the actual physical structure and operation parameters of the lifting equipment, and update the operation status and health status of the equipment by receiving equipment appearance images and equipment operation parameter data in real time.

[0021] The technical effects and advantages of the present invention: 1. The present invention provides a method and system for health monitoring of port lifting equipment. By demarcating a drone inspection area in the port, using adaptive flight path planning technology and adjusting the shooting angle and distance according to the equipment structure, the appearance images of the equipment are collected every t time periods. Based on all the defect information in the appearance images of the lifting equipment, the comprehensive evaluation index of the appearance defects of the lifting equipment in the i-th time period is calculated through the defect area. By comparing it with the preset comprehensive evaluation index of the appearance defects of the lifting equipment, if the comprehensive evaluation index of the appearance defects of the lifting equipment at this moment is greater than the preset comprehensive evaluation index of the appearance defects of the lifting equipment, it indicates that the appearance defects of the lifting equipment at this moment are serious. The position of the lifting equipment corresponding to the appearance image of the lifting equipment at this moment is extracted, and the appearance image of the lifting equipment is sent to the maintenance personnel for the maintenance personnel to perform maintenance operations. Otherwise, it indicates that the appearance monitoring of the lifting equipment at this moment is better. The equipment operation parameters of each operation cycle of the lifting equipment are collected through sensors, the load fluctuation, the change rate of height control error and the evaluation characteristics of wire rope bending health of the lifting equipment are calculated, and a health status characteristic early warning is established. Through the fusion of drone inspection and sensor data, the comprehensive monitoring of the equipment appearance and operation status is realized, the accuracy and coverage of data collection are improved, and based on the analysis and judgment of the data, the formulation of the maintenance plan is made more scientific and reasonable; 2. The present invention provides a method and system for health monitoring of port lifting equipment. Obtain the comprehensive evaluation index of the appearance defects of the lifting equipment, the load fluctuation, the change rate of height control error and the evaluation of wire rope bending health of the lifting equipment, perform parameter normalization processing, and further obtain the health evaluation coefficient of the lifting equipment based on the set equipment health evaluation model. Obtain the health evaluation coefficient of the lifting equipment, compare it with the preset health evaluation coefficient to obtain the deviation of the health evaluation coefficient of the lifting equipment. According to the deviation value of the health evaluation coefficient and the classification standard of the equipment state level of the lifting equipment, the lifting equipment is divided into four equipment state levels: healthy, warning, abnormal, and faulty, and the equipment state level of the lifting equipment is pushed to relevant personnel for maintenance of the lifting equipment. Construct a virtual model, and receive the appearance image of the equipment and the equipment operation parameter data in real time to update the operation state and health status of the equipment, realizing the multi-dimensional comprehensive evaluation of the lifting equipment. Based on the equipment health evaluation model, the health evaluation coefficient is obtained, and the quantitative evaluation and comparison method makes the judgment of the equipment health state more objective and intuitive, and can accurately locate the degree of deviation of the equipment from the normal state, so as to achieve precise maintenance. Introduce the digital twin technology, which can map the equipment operation state in real time and provide precise support for fault warning and maintenance decision-making; BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 It is a schematic structural diagram of a port lifting equipment health monitoring system of the present invention.

[0022] Figure 2 It is a schematic flow diagram of a port lifting equipment health monitoring method of the present invention. Detailed implementation manners

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0024] Please refer to Figure 1 As shown, the present invention provides a health monitoring system for port lifting equipment, including an unmanned aerial vehicle (UAV) inspection module, a lifting equipment appearance monitoring module, a lifting equipment data acquisition module, a lifting equipment health status feature processing module, a lifting equipment health fusion analysis module, a lifting equipment anomaly detection module, and a digital twin modeling module.

[0025] The UAV inspection module is connected to the lifting equipment appearance monitoring module, the lifting equipment data acquisition module is connected to the lifting equipment health status feature processing module, the lifting equipment appearance monitoring module and the lifting equipment health status feature processing module are connected to the lifting equipment health fusion analysis module, the lifting equipment health fusion analysis module is connected to the lifting equipment anomaly detection module, and the lifting equipment anomaly detection module is connected to the digital twin modeling module.

[0026] The UAV inspection module: demarcates a UAV inspection area in the port, adjusts the shooting angle and distance according to the equipment structure through the adaptive flight path planning technology, and collects equipment appearance images every t time periods.

[0027] In a possible design, the implementation steps of the UAV inspection module are specifically as follows: S01: Demarcate a UAV inspection area in the port, design an inspection route for each lifting equipment through the adaptive flight path planning technology, and set the take-off point, inspection path and return point; S02: Equip the UAV with a high-resolution camera, collect the appearance images of the lifting equipment every t time periods, and at the same time monitor the environmental light intensity through the light sensor in the environmental perception unit of the UAV. According to the preset light intensity-light source adjustment strategy, flexibly adjust the light source angle and intensity, and sequentially number each time period as 1, 2,... i,... n; S03: Establish a normal state image database and a defect state image database, match the collected equipment appearance images with the normal state image database and the defect state image database, traverse all defect information in the lifting equipment appearance images, and perform defect data annotation.

[0028] In a possible design, the adaptive flight path planning technology plans the inspection flight route of the unmanned aerial vehicle based on the distribution of key monitoring points of the device structure and combines the navigation function of the three-dimensional geographic information system to ensure comprehensive and efficient inspection coverage.

[0029] The lifting equipment appearance monitoring module: obtains all defect information in the lifting equipment appearance image collected in the i-th time period, calculates the comprehensive evaluation index of the lifting equipment appearance defect in the i-th time period through the defect area, and processes it.

[0030] In a possible design, the implementation steps of the lifting equipment appearance monitoring module are specifically as follows: S01: Obtain all defect information in the lifting equipment appearance image collected in the i-th time period, and extract the area of each defect in the image and the position of the lifting equipment. S02: Based on the areas of the defects in the lifting equipment appearance image in the i-th time period, calculate the comprehensive evaluation index of the lifting equipment appearance defect in the i-th time period based on the set appearance defect evaluation model. S03: Obtain the comprehensive evaluation index of the lifting equipment appearance defect in the i-th time period, and compare it with the preset comprehensive evaluation index of the lifting equipment appearance defect. If the comprehensive evaluation index of the lifting equipment appearance defect at this moment is greater than the preset comprehensive evaluation index of the lifting equipment appearance defect, it indicates that the appearance defect of the lifting equipment is serious at this moment. Extract the position of the lifting equipment corresponding to the lifting equipment appearance image at this moment, and send the lifting equipment appearance image to the maintenance personnel for the maintenance personnel to perform maintenance operations. Otherwise, it indicates that the appearance monitoring of the lifting equipment at this moment is better.

[0031] In this embodiment, it should be specifically noted that the calculation formula of the comprehensive evaluation index of the lifting equipment appearance defect is: , where represents the comprehensive evaluation index of the lifting equipment appearance defect in the i-th time period, represents the area of the q-th defect in the i-th time period, represents the weight factor of the q-th defect, and m represents the total number of defects.

[0032] The lifting equipment data acquisition module: collects the equipment operation parameters of each operation cycle of the lifting equipment through sensors. The operation cycle is a single hoisting as a cycle, and the equipment operation parameters include the lifting weight, the lifting height, and the micro-bending degree of the steel wire rope.

[0033] In a possible design, the lifting equipment data acquisition module is specifically as follows: Through the strain - type weighing sensor installed on the lifting equipment, the lifting weight borne by the equipment during operation is collected every t time periods according to the operation cycle; through the position sensor installed on the lifting equipment, the vertical displacement during the operation of the equipment is collected every t time periods according to the operation cycle to obtain the lifting height; through the wire rope micro - bend detection laser sensor installed on the lifting equipment, the wire rope micro - bend during the operation of the equipment is collected every t time periods according to the operation cycle.

[0034] The health - state feature processing module of the lifting equipment: It is used to obtain the equipment operation parameters of each operation cycle of the lifting equipment, calculate the load fluctuation, the change rate of height control error and the evaluation feature of wire rope bending health of the lifting equipment, and establish a health - state feature early warning.

[0035] In a possible design, the implementation steps of the health - state feature processing module of the lifting equipment are specifically as follows: S01: Load - fluctuation feature: By calculating the mean value and standard deviation of the lifting weight of each operation cycle of the lifting equipment, according to the ratio of the standard deviation to the mean value of the lifting weight, calculate the load fluctuation of each operation cycle of the lifting equipment. By obtaining the load fluctuation of each operation cycle of the lifting equipment and comparing it with the preset load fluctuation, if the load fluctuation of a certain operation cycle is greater than the preset load fluctuation, it indicates that the load of the lifting equipment fluctuates abnormally, and the management personnel should be notified to check the equipment. Otherwise, it indicates that the load of the lifting equipment has no abnormality. S02: Change - rate feature of height control error: By using the lifting height and the target height of each operation cycle of the lifting equipment, calculate the change rate of height control error of each operation cycle of the lifting equipment. By obtaining the change rate of height control error of each operation cycle of the lifting equipment and comparing it with the preset change rate of height control error, if the change rate of height control error of a certain operation cycle is greater than the preset change rate of height control error, it indicates that the control system of the lifting equipment fails, and the management personnel should be notified to check the equipment. Otherwise, it indicates that the control system of the lifting equipment has no abnormality. S03: Evaluation feature of wire - rope bending health: Collect the wire - rope micro - bend during the operation of the equipment every t time periods according to the operation cycle. By using the wire - rope micro - bend and the critical bend of the lifting equipment, calculate the evaluation of the wire - rope bending health of the lifting equipment. By obtaining the evaluation of the wire - rope bending health of the lifting equipment and comparing it with the preset evaluation of the wire - rope bending health, if the evaluation of the wire - rope bending health in a certain time period is less than the preset evaluation of the wire - rope bending health and greater than or equal to 0, it indicates that the wire rope of the lifting equipment is damaged, and the management personnel should be notified to check the equipment. If the evaluation of the wire - rope bending health in a certain time period is less than 0, it indicates that the wire - rope bending health of the lifting equipment exceeds the safety limit, and an emergency shutdown should be carried out immediately. Otherwise, it indicates that the evaluation of the wire - rope bending health of the lifting equipment has no abnormality.

[0036] In this embodiment, it should be specifically noted that the lifting equipment health status feature processing module further includes: The load fluctuation feature: By calculating the mean value and standard deviation of the load weight for each operation cycle of the lifting equipment, and according to the ratio of the standard deviation of the load weight to the mean value, calculate the load fluctuation for each operation cycle of the lifting equipment. Specifically: , where represents the mean value of the load weight for the j-th operation cycle, represents the load weight for the j-th operation cycle in the i-th time period, and n represents the number of time periods; , where represents the standard deviation of the load weight for the j-th operation cycle; The calculation formula for the load fluctuation is: , where represents the load fluctuation for the j-th operation cycle, reflecting the dynamic change degree of the load during the lifting process, The larger the value, the more unstable the load.

[0037] The height control error change rate feature: By calculating the hoisting height and the target height for each operation cycle of the lifting equipment, calculate the height control error change rate for each operation cycle of the lifting equipment. Specifically: , where represents the height control error change rate for the j-th operation cycle, represents the hoisting height for the j-th operation cycle in the i-th time period, represents the target height for the i-th time period, represents the allowable error between the hoisting height and the target height, reflecting the position control accuracy during the lifting process.

[0038] The wire rope bending health assessment feature: According to the operation cycle, collect the wire rope micro-bending degree during the equipment operation every t time periods. By calculating the wire rope micro-bending degree and the critical bending degree of the lifting equipment, calculate the wire rope bending health assessment of the lifting equipment. Specifically: , where represents the wire rope bending health assessment for the j-th operation cycle in the i-th time period, represents the wire rope micro-bending degree for the j-th operation cycle in the i-th time period, represents the critical bending degree, reflecting the health status of the wire rope at the current moment during the lifting process.

[0039] The lifting equipment health integration analysis module: It is used to obtain the comprehensive evaluation index of the appearance defects of the lifting equipment, the load fluctuation, the change rate of the height control error, and the health evaluation of the wire rope bending, and obtain the health evaluation coefficient of the lifting equipment according to the equipment health evaluation model.

[0040] In a possible design, the implementation steps of the lifting equipment health integration analysis module are specifically as follows: S01: Obtain the comprehensive evaluation index of the appearance defects of the lifting equipment in each time period, the load fluctuation in each operation cycle, the change rate of the height control error in each operation cycle, and the health evaluation of the wire rope bending in each operation cycle time period, and perform parameter normalization processing.

[0041] S02: Obtain the comprehensive evaluation index of the appearance defects of the lifting equipment, the load fluctuation, the change rate of the height control error, and the health evaluation of the wire rope bending after normalization processing, and obtain the health evaluation coefficient of the lifting equipment based on the set equipment health evaluation model.

[0042] In this embodiment, it should be specifically noted that the lifting equipment health integration analysis module further includes: S01: Parameter normalization processing, specifically:

[0043] Among them, represents the normalized value of the comprehensive evaluation index of the appearance defects of the lifting equipment, represents the comprehensive evaluation index of the appearance defects of the lifting equipment in the i-th time period, represents the maximum value of the comprehensive evaluation index of the appearance defects of the lifting equipment, represents the minimum value of the comprehensive evaluation index of the appearance defects of the lifting equipment;

[0044] Among them, represents the normalized value of the load fluctuation, represents the load fluctuation in the j-th operation cycle, represents the minimum value of the load fluctuation, represents the maximum value of the load fluctuation;

[0045] Among them, represents the normalized value of the change rate of the height control error, represents the change rate of the height control error in the j-th operation cycle, represents the minimum value of the change rate of the height control error, represents the maximum value of the change rate of the height control error.

[0046] , where is expressed as the normalized value of the wire rope bending health assessment, represents the wire rope bending health assessment for the j-th operation cycle in the i-th time period, represents the minimum value of the wire rope bending health assessment, represents the maximum value of the wire rope bending health assessment.

[0047] S02: The calculation formula for the health assessment coefficient is:

[0048] where is expressed as the health assessment coefficient, , , , respectively represent the weight factors of each normalized value, and + + + = 1.

[0049] The lifting equipment anomaly detection module: Obtains the health assessment coefficient of the lifting equipment, compares it with the preset health assessment coefficient, and classifies the status level of the lifting equipment according to the deviation of the health assessment coefficient of the lifting equipment.

[0050] In a possible design, the lifting equipment anomaly detection module is specifically: Obtains the health assessment coefficient of the lifting equipment, compares it with the preset health assessment coefficient, obtains the deviation of the health assessment coefficient of the lifting equipment, matches the deviation value of the health assessment coefficient with the lifting equipment status level classification standard, classifies the lifting equipment into four equipment status levels: healthy, warning, abnormal, and faulty, and pushes the equipment status level of the lifting equipment to relevant personnel for maintenance of the lifting equipment.

[0051] The digital twin modeling module: Constructs a virtual model based on the actual physical structure and operating parameters of the lifting equipment, and updates the operating status and health status of the equipment by receiving device appearance images and device operating parameter data in real time.

[0052] Please refer to Figure 2 shown, a method for health monitoring of port lifting equipment includes the following steps: Step S01: Demarcate the UAV inspection area in the port, adjust the shooting angle and distance according to the equipment structure through adaptive flight path planning technology, and collect device appearance images every t time periods; Step S02: Obtain all defect information in the appearance image of the lifting equipment collected in the i-th time period, calculate the comprehensive evaluation index of the appearance defects of the lifting equipment in the i-th time period through the defect area, and process it; Step S03: Collect the equipment operation parameters of each operation cycle of the lifting equipment through sensors. The operation cycle is one cycle for a single lifting, and the equipment operation parameters include the lifting weight, lifting height, and micro-bending degree of the wire rope; Step S04: Used to obtain the equipment operation parameters of each operation cycle of the lifting equipment, calculate the load fluctuation, height control error change rate, and wire rope bending health assessment characteristics of the lifting equipment, and establish a health status characteristic warning; Step S05: Used to obtain the comprehensive evaluation index of the appearance defects of the lifting equipment, load fluctuation, height control error change rate, and wire rope bending health assessment of the lifting equipment, and obtain the health assessment coefficient of the lifting equipment according to the equipment health assessment model; Step S06: Obtain the health assessment coefficient of the lifting equipment, compare it with the preset health assessment coefficient, and classify the status level of the lifting equipment according to the deviation of the health assessment coefficient of the lifting equipment; Step S07: Construct a virtual model based on the actual physical structure and operation parameters of the lifting equipment, and update the operation status and health status of the equipment by receiving the equipment appearance image and equipment operation parameter data in real time.

[0053] In this embodiment, it should be specifically noted that the present invention delimits a drone inspection area in the port, adjusts the shooting angle and distance according to the equipment structure through the adaptive flight path planning technology, collects the equipment appearance image every t time periods, calculates the comprehensive evaluation index of the appearance defects of the lifting equipment in the i-th time period through all the defect information in the appearance image of the lifting equipment, and compares it with the preset comprehensive evaluation index of the appearance defects of the lifting equipment. If the comprehensive evaluation index of the appearance defects of the lifting equipment at this moment is greater than the preset comprehensive evaluation index of the appearance defects of the lifting equipment, it indicates that the appearance defects of the lifting equipment are serious at this moment. Extract the position of the lifting equipment corresponding to the appearance image of the lifting equipment at this moment, and send the appearance image of the lifting equipment to the maintenance personnel for the maintenance personnel to perform maintenance operations. Otherwise, it indicates that the appearance monitoring of the lifting equipment is better at this moment. Collect the equipment operation parameters of each operation cycle of the lifting equipment through sensors, calculate the load fluctuation, height control error change rate, and wire rope bending health assessment characteristics of the lifting equipment, and establish a health status characteristic warning. Through the integration of drone inspection and sensor data, the comprehensive monitoring of the equipment appearance and operation status is realized, the accuracy and coverage of data collection are improved, and the analysis and judgment are based on the data, making the formulation of the maintenance plan more scientific and reasonable; The present invention obtains the comprehensive evaluation index of the appearance defects of the lifting equipment, the load fluctuation, the change rate of the height control error, and the evaluation of the bending health of the steel wire rope, performs parameter normalization processing, and further obtains the health evaluation coefficient of the lifting equipment based on the set equipment health evaluation model. The health evaluation coefficient of the lifting equipment is obtained, compared with the preset health evaluation coefficient, and the deviation of the health evaluation coefficient of the lifting equipment is obtained. According to the deviation value of the health evaluation coefficient and the classification standard of the lifting equipment state level, the lifting equipment is divided into four equipment state levels: healthy, warning, abnormal, and faulty. The equipment state level of the lifting equipment is pushed to relevant personnel for maintenance of the lifting equipment. A virtual model is constructed to receive the equipment appearance image and equipment operation parameter data in real time to update the operation state and health status of the equipment, realizing the multi-dimensional comprehensive evaluation of the lifting equipment. Based on the equipment health evaluation model, the health evaluation coefficient is obtained, and the quantitative evaluation and comparison method makes the judgment of the equipment health state more objective and intuitive, and can accurately locate the degree of deviation of the equipment from the normal state, so as to achieve precise maintenance. The introduction of digital twin technology can map the equipment operation state in real time and provide precise support for fault warning and maintenance decision-making.

[0054] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A health monitoring system for port lifting equipment, characterized in that, Including: UAV inspection module: Define the UAV inspection area in the port. Through adaptive flight path planning technology and according to the equipment structure, adjust the shooting angle and distance, and collect the equipment appearance images every t time periods. Lifting equipment appearance monitoring module: Obtain all defect information in the lifting equipment appearance images collected in the i-th time period, calculate the comprehensive evaluation index of the lifting equipment appearance defects in the i-th time period through the defect area, and process it. Lifting equipment data collection module: Collect the equipment operation parameters of each operation cycle of the lifting equipment through sensors. The operation cycle is one cycle for a single lifting, and the equipment operation parameters include the lifting weight, lifting height, and micro-bending degree of the wire rope. Lifting equipment health status feature processing module: Used to obtain the equipment operation parameters of each operation cycle of the lifting equipment, calculate the load fluctuation, height control error change rate, and wire rope bending health evaluation features of the lifting equipment, and establish a health status feature warning. Lifting equipment health fusion analysis module: Used to obtain the comprehensive evaluation index of the lifting equipment appearance defects, load fluctuation, height control error change rate, and wire rope bending health evaluation of the lifting equipment, and obtain the health evaluation coefficient of the lifting equipment according to the equipment health evaluation model. Lifting equipment anomaly detection module: Obtain the health evaluation coefficient of the lifting equipment, compare it with the preset health evaluation coefficient, and classify the state level of the lifting equipment according to the deviation of the health evaluation coefficient of the lifting equipment. Digital twin modeling module: Build a virtual model based on the actual physical structure and operation parameters of the lifting equipment, and update the operation state and health status of the equipment by receiving the equipment appearance images and equipment operation parameter data in real time.

2. The health monitoring system for a port hoisting device according to claim 1, wherein: The implementation steps of the UAV inspection module are specifically as follows: S21: Define the UAV inspection area in the port, design the inspection route for each lifting equipment through adaptive flight path planning technology, and set the take-off point, inspection path, and return point. S22: Equip the UAV with a high-resolution camera, collect the lifting equipment appearance images every t time periods. At the same time, monitor the ambient light intensity through the light sensor in the UAV's environmental perception unit, flexibly adjust the light source angle and intensity according to the preset light intensity-light source adjustment strategy, and sequentially number each time period as 1, 2,... i,... n. S23: Establish a normal state image database and a defect state image database, match the collected equipment appearance images with the normal state image database and the defect state image database, traverse all defect information in the lifting equipment appearance images, and perform defect data annotation.

3. The health monitoring system for a port hoisting device according to claim 1, characterized in that: The adaptive flight path planning technology is based on the distribution of key monitoring points of the equipment structure, and combines the navigation function of the three-dimensional geographic information system to plan the UAV inspection flight route to ensure comprehensive and efficient inspection coverage.

4. The health monitoring system for a port hoisting device according to claim 1, wherein: The implementation steps of the lifting equipment appearance monitoring module are specifically as follows: S41: Obtain all defect information in the lifting equipment appearance images collected in the i-th time period, and extract the area of each defect and the position of the lifting equipment in the image. S42: Based on the areas of the defects in the appearance image of the lifting equipment in the \(i\)-th time period, calculate the comprehensive evaluation index of the appearance defects of the lifting equipment in the \(i\)-th time period according to the set appearance defect evaluation model; S43: Obtain the comprehensive evaluation index of the appearance defects of the lifting equipment in the \(i\)-th time period, and compare it with the preset comprehensive evaluation index of the appearance defects of the lifting equipment. If the comprehensive evaluation index of the appearance defects of the lifting equipment at this moment is greater than the preset comprehensive evaluation index of the appearance defects of the lifting equipment, it indicates that the appearance defects of the lifting equipment are serious at this moment. Extract the position of the lifting equipment corresponding to the appearance image of the lifting equipment at this moment, and send the appearance image of the lifting equipment to the maintenance personnel for maintenance operations. Otherwise, it indicates that the appearance monitoring of the lifting equipment at this moment is better.

5. The health monitoring system for a port hoisting device according to claim 1, characterized in that: The lifting equipment data acquisition module is specifically: Through the strain type weighing sensor installed on the lifting equipment, collect the lifting weight borne by the equipment during operation every \(t\) time periods according to the operation cycle; through the position sensor installed on the lifting equipment, collect the vertical displacement during the operation of the equipment every \(t\) time periods according to the operation cycle to obtain the lifting height; Through the wire rope micro-bending detection laser sensor installed on the lifting equipment, collect the wire rope micro-bending during the operation of the equipment every \(t\) time periods according to the operation cycle.

6. The health monitoring system for a port lifting device according to claim 1, characterized in that: The implementation steps of the lifting equipment health status feature processing module are specifically: S61: Load fluctuation feature: Calculate the mean value and standard deviation of the lifting weight of each operation cycle of the lifting equipment. According to the ratio of the standard deviation to the mean value of the lifting weight, calculate the load fluctuation of each operation cycle of the lifting equipment. By obtaining the load fluctuation of each operation cycle of the lifting equipment and comparing it with the preset load fluctuation, if the load fluctuation of a certain operation cycle is greater than the preset load fluctuation, it indicates that the load of the lifting equipment fluctuates abnormally, and the management personnel should be notified to check the equipment. Otherwise, it indicates that the load of the lifting equipment has no abnormality; S62: Height control error change rate feature: Calculate the height control error change rate of each operation cycle of the lifting equipment through the lifting height and the target height of each operation cycle of the lifting equipment. By obtaining the height control error change rate of each operation cycle of the lifting equipment and comparing it with the preset height control error change rate, if the height control error change rate of a certain operation cycle is greater than the preset height control error change rate, it indicates that the control system of the lifting equipment fails, and the management personnel should be notified to check the equipment. Otherwise, it indicates that the control system of the lifting equipment has no abnormality; S63: Characteristics of wire rope bending health assessment: According to the operation cycle, the micro-bending degree of the wire rope during the operation of the equipment is collected every t time periods. By comparing the micro-bending degree of the wire rope of the lifting equipment with the critical bending degree, the health assessment of the wire rope bending of the lifting equipment is calculated. By obtaining the health assessment of the wire rope bending of the lifting equipment and comparing it with the preset health assessment of the wire rope bending, if the health assessment of the wire rope bending in a certain time period is less than the preset health assessment of the wire rope bending and greater than or equal to 0, it indicates that the wire rope of the lifting equipment is damaged, and the management personnel should be notified to check the equipment. If the health assessment of the wire rope bending in a certain time period is less than 0, it indicates that the health degree of the wire rope bending of the lifting equipment exceeds the safety limit, and an emergency shutdown should be carried out immediately. Otherwise, it indicates that the health assessment of the wire rope bending of the lifting equipment is normal.

7. The health monitoring system for a port hoisting device according to claim 1, wherein: The implementation steps of the lifting equipment health integration analysis module are specifically as follows: S71: Obtain the comprehensive evaluation index of the appearance defects of the lifting equipment in each time period, the load fluctuation in each operation cycle, the change rate of the height control error in each operation cycle, and the health assessment of the wire rope bending in each operation cycle time period, and perform parameter normalization processing; S72: Obtain the comprehensive evaluation index of the appearance defects of the lifting equipment, the load fluctuation, the change rate of the height control error, and the health assessment of the wire rope bending after normalization processing, and obtain the health assessment coefficient of the lifting equipment based on the set equipment health assessment model.

8. The health monitoring system for a port hoisting device according to claim 1, characterized in that: The specific lifting equipment anomaly detection module is as follows: Obtain the health assessment coefficient of the lifting equipment, compare it with the preset health assessment coefficient, obtain the deviation of the health assessment coefficient of the lifting equipment, match it according to the deviation value of the health assessment coefficient and the classification standard of the lifting equipment status level, classify the lifting equipment into four equipment status levels: healthy, warning, abnormal, and faulty, and push the equipment status level of the lifting equipment to relevant personnel for maintenance of the lifting equipment.

9. A health monitoring method for port lifting equipment, using a health monitoring system for port lifting equipment according to any one of claims 1-8, characterized in that: It includes the following steps: Step S01: Designate a drone inspection area in the port, adjust the shooting angle and distance according to the equipment structure through adaptive flight path planning technology, and collect equipment appearance images every t time periods; Step S02: Obtain all defect information in the appearance image of the lifting equipment collected in the i-th time period, calculate the comprehensive evaluation index of the appearance defects of the lifting equipment in the i-th time period through the defect area, and process it; Step S03: Collect the equipment operation parameters of each operation cycle of the lifting equipment through sensors. The operation cycle is a single lifting as a cycle, and the equipment operation parameters include the lifting weight, the lifting height, and the micro-bending degree of the wire rope; Step S04: Used to obtain the equipment operation parameters of each operation cycle of the lifting equipment, calculate the load fluctuation, the change rate of the height control error, and the characteristics of the wire rope bending health assessment of the lifting equipment, and establish a health status characteristic warning; Step S05: Used to obtain the comprehensive evaluation index of the appearance defects of the lifting equipment, the load fluctuation, the change rate of the height control error, and the health assessment of the wire rope bending of the lifting equipment, and obtain the health assessment coefficient of the lifting equipment according to the equipment health assessment model; Step S06: Obtain the health assessment coefficient of the lifting equipment, compare it with the preset health assessment coefficient, and classify the status level of the lifting equipment according to the deviation of the health assessment coefficient of the lifting equipment; Step S07: Construct a virtual model based on the actual physical structure and operating parameters of the lifting equipment, and update the operating status and health condition of the equipment by receiving device appearance images and device operating parameter data in real time.

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