Management method and system of wharf crane, control device, storage medium and computer program product

Abstract

The invention provides a wharf crane management method and system, a control device, a storage medium and a computer program product. The management method of the wharf crane comprises the following steps: acquiring real-time data information of the crane; according to the real-time data information, steel structure fault diagnosis, important mechanism fault diagnosis and electrical system fault diagnosis are carried out on all the cranes, and fault diagnosis results are obtained. And carrying out maintenance processing on the crane according to a fault diagnosis result. And in combination with the real-time data information and the fault diagnosis result of each crane, obtaining a wharf operation state and a stand-alone crane operation state. And establishing a crane digital twinborn model, mapping the wharf operation state and the single crane operation state to the crane digital twinborn model, and displaying the crane digital twinborn model. The management method can improve the wharf operation efficiency.

Description

Technical Field

[0001] The present invention relates to automated dock control, and in particular to a management method, system, control device, storage medium and computer program product for dock cranes. Background Art

[0002] With the acceleration of globalization, the transformation or construction of automated container terminals has become a key trend to improve port throughput and transportation efficiency. Quay cranes are crucial equipment in docks. They are mainly used for cargo loading and unloading operations, especially the handling of large containers, bulk cargo and heavy cargo. Due to the huge amount of cargo handled by modern ports, the efficiency of cranes directly determines the speed of cargo loading and unloading and the throughput capacity of ports. Efficient cranes can significantly reduce the time that cargo ships stay in ports, thereby improving ship turnover efficiency and reducing transportation costs.

[0003] However, the traditional post-maintenance model can no longer meet the needs of modern automated terminals. Although fault diagnosis technology is constantly developing, the fault diagnosis system for integrated equipment in automated terminals is still missing, which hinders the further development of automated terminals and the efficiency of terminal operations cannot be improved. Summary of the invention

[0004] The object of the present invention is to provide a management method, system, control device, storage medium and computer program product for a dock crane, which can improve the efficiency of dock operations.

[0005] One aspect of the present invention provides a management method for terminal cranes, comprising: acquiring real-time data information of cranes; performing steel structure fault diagnosis, important mechanism fault diagnosis and electrical system fault diagnosis on each crane based on the real-time data information to obtain fault diagnosis results; performing maintenance on the cranes based on the fault diagnosis results; obtaining the terminal operation status and the single crane operation status by combining the real-time data information of each crane and the fault diagnosis results; establishing a crane digital twin model, mapping the terminal operation status and the single crane operation status to the crane digital twin model, and displaying the crane digital twin model.

[0006] In one embodiment, the steel structure fault diagnosis of each crane includes: obtaining the radial displacement of the stress release hole at the key point of the steel structure; if the radial displacement is greater than the deformation threshold, determining that the key point has a potential structural damage risk; using the key point with potential structural damage risk as an alarm point, and determining that the alarm point is in a fault state.

[0007] In one embodiment, the steel structure fault diagnosis of each crane further includes: obtaining the vibration amplitude in the direction of the trolley and the natural frequency in the direction of the trolley; if the vibration amplitude in the direction of the trolley is greater than the vibration amplitude threshold or the natural frequency in the direction of the trolley is greater than the natural frequency threshold, it is determined that the steel structure is in a fault state.

[0008] In one embodiment, the important mechanism fault diagnosis of each crane includes: determining the vibration severity of the important mechanism; wherein the important mechanism includes a lifting mechanism, a trolley mechanism, and a pitching mechanism; if the vibration severity of the important mechanism is greater than a severity threshold, then the important mechanism is determined to be in a fault state; wherein the vibration severity of the important mechanism satisfies:

[0009]

[0010] The vibration of this important mechanism is composed of n simple harmonic vibrations of different frequencies, n≥1, v(t) is the vibration velocity signal of this important mechanism, V rms It is the vibration intensity of this important mechanism.

[0011] In one embodiment, the important mechanism fault diagnosis of each crane also includes: data preprocessing through noise reduction and order analysis algorithms; extracting time domain features, frequency domain features and time-frequency domain analysis to establish a vibration feature parameter library; reducing the amount of calculation in the fault diagnosis process and the transmission and storage pressure of related data platforms through PCA principal component analysis method; training and updating the model through classification model; extracting the effective feature vector of the real-time vibration signal at the terminal central control as the input of the trained / updated model to obtain the model classification result; the technical personnel confirm whether the diagnosis result is correct on site. If it is wrong, the diagnosis result is manually corrected, and this part of the data and the correct result are entered into the database and sent to the remote center to re-update the training model.

[0012] In one embodiment, the electrical system fault diagnosis of each crane includes: establishing a fault tree model based on historical fault data and expert experience; based on the established fault tree model, automatically searching for matching top events according to the input electrical system fault symptoms to generate a top event fault tree; finding the minimum cut set of the top event fault tree; calculating the importance of each minimum cut set; generating a sequential detection process according to the principle of giving priority to the detection of the minimum cut set with greater importance; and testing in the order of the detection process to find the source of the fault.

[0013] In one embodiment, the maintenance processing of the crane according to the fault diagnosis result includes: if the fault diagnosis result is a fault state, the crane is subjected to emergency maintenance processing; wherein, the emergency maintenance of the crane includes: recording the fault information of the crane and forming an emergency maintenance work order; determining the priority of the emergency maintenance work order according to the fault information corresponding to each emergency maintenance work order; arranging equipment maintenance personnel to go to the site to handle the fault and record the relevant information of the on-site maintenance; if the emergency maintenance work order is completed, the emergency maintenance work order is set to be completed; if the emergency maintenance work order is not completed, the emergency maintenance work order is reassigned or the work order is transferred; if the completed emergency maintenance work order requires subsequent maintenance, the emergency maintenance work order is maintained.

[0014] In one embodiment, the maintenance processing of the crane according to the fault diagnosis result also includes: if the fault diagnosis result is a non-fault state, the crane is maintained; wherein the maintenance processing of the crane includes: setting a maintenance inspection item definition table for the crane; using a maintenance service program running in the background, according to the equipment operation statistical data interface, combined with the maintenance inspection item definition table, automatically updating the maintenance inspection task item list; obtaining the prediction result through the interface of the pre-defined crane mechanism fault prediction module, and generating related maintenance inspection task items to the list; obtaining the task transferred from the emergency maintenance module to the daily maintenance module through the interface of the emergency maintenance module, and automatically generating related maintenance inspection task items to the list according to the predefined format; obtaining the production arrangement plan from the production planning system through the interface of the production planning system and updating the equipment available time schedule, and arranging the equipment maintenance time plan according to the production plan gap; generating an equipment inspection plan and an inspection and maintenance plan work order; generating a formal work order after confirmation and approval.

[0015] In one embodiment, the acquisition of real-time data information of the crane includes: arranging sensor subsystems in the crane's lifting mechanism, trolley driving mechanism, carriage traveling mechanism, driver's cab and machine room; and acquiring data of the sensor subsystem as the real-time data information of the crane.

[0016] In one embodiment, the sensor subsystem includes a radial vibration sensor, a liquid level sensor, a liquid temperature sensor, a rotation speed sensor, an acceleration sensor and a sound sensor; the sensor subsystem is arranged in the lifting mechanism, trolley mechanism, carriage travel mechanism, driver's cab and machine room of the crane, including: arranging a motor vertical radial vibration sensor and a motor horizontal radial vibration sensor on the motor output shaft of the lifting mechanism of the crane, arranging the radial vibration sensor on the reduction box input shaft, reduction box output shaft and bearing seat of the lifting mechanism, arranging the liquid level sensor and the liquid temperature sensor on the end face of the high-speed shaft of the reduction box of the lifting mechanism, and arranging the The speed sensor is arranged at the high-speed coupling of the lifting mechanism; the motor radial vibration sensor is arranged on the motor output shaft of the trolley mechanism of the crane, the radial vibration sensor is arranged on the reducer input shaft, the reducer output shaft and the bearing seat of the trolley mechanism, the liquid level sensor and the liquid temperature sensor are arranged on the end face of the high-speed shaft of the reducer of the trolley mechanism, the speed sensor is arranged at the high-speed coupling of the trolley mechanism, the acceleration sensor is arranged on the bearing seat of the trolley wheel of the trolley mechanism; the acceleration sensor is arranged on the bearing seat of the trolley wheel of the trolley traveling mechanism; the sound sensor is arranged in the driver's cab and the machine room.

[0017] In one embodiment, the terminal operation status includes system KPI indicators, machine overall parameter information, meteorological information and yard information; wherein, the system KPI indicators include the total number of various types of cranes, the number of online cranes, the number of faults and the number of standard containers loaded and unloaded; the machine overall parameter information includes the machine number, machine status, current task type and machine running time; the yard information is used to display all crane models that need to be monitored on the current terminal; and / or the machine crane operation status includes the current machine's task information, operating box quantity statistics, real-time working condition simulation of the whole machine, sling information, the working status of each subsystem and the number of failures of the mechanism, running time statistics, speed, operation permission status and wind speed information; wherein, the sling information includes opening and closing locks, box landing signals and twist lock systems.

[0018] Another aspect of the present invention provides a management system for terminal cranes, comprising: a data resource subsystem for acquiring real-time data information of cranes; an equipment resource subsystem for performing steel structure fault diagnosis, important mechanism fault diagnosis and electrical system fault diagnosis on each crane based on the real-time data information to obtain fault diagnosis results, and performing maintenance on the cranes based on the fault diagnosis results; an operation control subsystem for combining the real-time data information of each crane and the fault diagnosis results to obtain the terminal operation status and the single crane operation status; a safety supervision subsystem for establishing a crane digital twin model, mapping the terminal operation status and the single crane operation status to the crane digital twin model, and displaying the crane digital twin model.

[0019] Yet another aspect of the present invention provides a control device, comprising: a memory; and a processor, wherein the processor is connected to the memory and configured to implement the management method of the dock crane as described in any one of the above embodiments.

[0020] Yet another aspect of the present invention provides a storage medium for storing non-transitory computer instructions. When the non-transitory computer instructions are executed, the method for managing a dock crane as described in any one of the above embodiments is executed.

[0021] Yet another aspect of the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method for managing a dock crane as described in any one of the above embodiments.

[0022] The management method of the dock crane of the present invention performs fault diagnosis through three aspects: steel structure fault diagnosis, important mechanism fault diagnosis and electrical system fault diagnosis, and can obtain comprehensive and accurate fault diagnosis results. It predicts the maintenance needs of the equipment based on data, prevents downtime caused by sudden faults, thereby improving the availability and life of the equipment, and significantly improving the operational efficiency, safety and service quality of the terminal. It is finally displayed through a digital twin model to achieve visualization of terminal supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other features, properties and advantages of the present invention will become more apparent through the following description in conjunction with the accompanying drawings and embodiments, in which:

[0024] Figure 1 is a flow chart of an embodiment of a method for managing a quay crane according to the present invention;

[0025] Figure 2 FIG. 1 is a schematic diagram of an embodiment of a management system for a quay crane according to the present invention. DETAILED DESCRIPTION

[0026] In terms of information systems, applications in automated terminal management are mainly focused on data collection, storage and basic query, with the goal of realizing the collection and simple query of various types of terminal data. However, due to the lack of efficient data integration and analysis functions, traditional information systems are difficult to meet the terminal's needs for real-time and multi-dimensional data analysis. This has led to deficiencies in data-driven decision support, production scheduling and risk warning. At the same time, existing information systems usually lack flexible modular design and are difficult to adapt to changes in terminal business needs. Realizing the deep integration of information systems with terminal equipment and operating processes has become an important direction for the development of smart terminals.

[0027] In terms of equipment control, in the automated terminal scenario, equipment control involves centralized scheduling and status monitoring of yard equipment, loading and unloading equipment, and transportation equipment. Current equipment control mostly adopts independent control methods, which makes it difficult to achieve coordination and dynamic scheduling among multiple devices. At the same time, the use status, operating efficiency, and health status information of the equipment are scattered, making it difficult to timely warn and respond to equipment failures, which in turn affects the overall operating efficiency of the terminal. In addition, due to differences in technical standards of different equipment manufacturers and systems, there are still obstacles to intercommunication between equipment. The intelligent development of equipment control technology requires the real-time collection and centralized management of equipment data to achieve the best utilization efficiency and full life cycle management of equipment.

[0028] In terms of automated terminal operation and management technology, the operation and management technology of automated terminals mainly focuses on optimizing the terminal's operating procedures and resource scheduling to improve loading and unloading efficiency and resource utilization. However, existing operation and management technologies are mostly limited to fixed operation modes and static scheduling methods, which are difficult to adapt to the complex and dynamic operation needs in the port. Faced with the high concurrency of multiple tasks such as ship arrival, loading and unloading operations, cargo stacking and transportation, traditional operation and management technologies are difficult to effectively coordinate equipment resources and human resources. In addition, the existing management system lacks adaptability and real-time optimization capabilities when dealing with emergencies and changes. The intelligent improvement of automated terminal operation and management requires the further introduction of data-driven dynamic decision-making and scheduling technologies to meet the needs of multi-task concurrency and efficient resource allocation, laying the foundation for the overall intelligent operation of the terminal.

[0029] Reference will now be made in detail to embodiments of the present invention, one or more examples of which are illustrated in the accompanying drawings. Each example is provided to explain the present invention, not to limit the present invention. In fact, it will be apparent to those skilled in the art that various modifications and variations may be made in the present invention without departing from the scope or spirit of the present invention. For example, a feature illustrated or described as part of one embodiment may be used together with another embodiment to produce yet another embodiment. Therefore, the present invention is intended to cover these modifications and variations within the scope of the appended claims and their equivalents.

[0030] Figure 1 The management method of the quay crane of the present invention is shown. Figure 1 As shown, the management method of the quay crane of the present invention includes steps S100 to S500:

[0031] In step S100, real-time data information of the crane is obtained.

[0032] In step S200, according to the real-time data information, steel structure fault diagnosis, important mechanism fault diagnosis and electrical system fault diagnosis are performed on each crane to obtain fault diagnosis results.

[0033] In step S300, maintenance is performed on the crane according to the fault diagnosis result.

[0034] In step S400, the terminal operation status and the single crane operation status are obtained by combining the real-time data information and the fault diagnosis results of each crane.

[0035] In step S500, a crane digital twin model is established, the terminal operation status and the single crane operation status are mapped to the crane digital twin model, and the crane digital twin model is displayed.

[0036] The management method of the dock crane of the present invention performs fault diagnosis through three aspects: steel structure fault diagnosis, important mechanism fault diagnosis and electrical system fault diagnosis, and can obtain comprehensive and accurate fault diagnosis results. It predicts the maintenance needs of the equipment based on data, prevents downtime caused by sudden faults, thereby improving the availability and life of the equipment, and significantly improving the operational efficiency, safety and service quality of the terminal. It is finally displayed through a digital twin model to achieve visualization of terminal supervision.

[0037] Figure 2 FIG. 2 shows a management system for a quay crane according to the present invention. Figure 2 As shown, the dock crane management system of the present invention includes a data resource subsystem 100 , an equipment resource subsystem 200 , an operation control subsystem 300 and a safety supervision subsystem 400 .

[0038] The data resource subsystem 100 is used to collect, integrate and manage multi-dimensional data of the terminal to support upper-level intelligent decision-making. In combination with the above-mentioned management method of the terminal crane, specifically, the data resource subsystem 100 is used to obtain real-time data information of the crane.

[0039] The equipment resource subsystem 200 is used to build a resource pool of automated terminal equipment for unified management and to formulate maintenance strategies for the equipment. In combination with the above-mentioned management method of terminal cranes, specifically, the equipment resource subsystem 200 is used to perform steel structure fault diagnosis, important mechanism fault diagnosis, and electrical system fault diagnosis on each crane based on real-time data information, obtain fault diagnosis results, and perform maintenance processing on the crane based on the fault diagnosis results.

[0040] The operation control subsystem 300 is used to realize efficient and intelligent dispatching and operation plan management of equipment and resources in the terminal. In combination with the above-mentioned management method of the terminal crane, specifically, the operation control subsystem 300 is used to combine the real-time data information and fault diagnosis results of each crane to obtain the terminal operation status and the single crane operation status.

[0041] The safety supervision subsystem 400 builds a terminal safety monitoring and early warning system based on real-time monitoring and digital twin technology to ensure the safety and stability of the operating environment. In combination with the above-mentioned management method of the terminal crane, specifically, the safety supervision subsystem 400 is used to establish a crane digital twin model, map the terminal operation status and the single crane operation status to the crane digital twin model, and display the crane digital twin model.

[0042] In the present invention, the data resource subsystem 100 is first run to automatically collect multi-dimensional data in the terminal, and the data is integrated and managed to form a unified data source to provide data support for upper-level intelligent services. Then, the operation control subsystem 300 intelligently allocates and dynamically optimizes the terminal's resources, equipment, and operation plans to ensure that the equipment works efficiently and collaboratively in different operation scenarios. Finally, the safety supervision subsystem 400 is used to rely on digital twin technology to monitor the entire field equipment and operation processes in real time, realize risk warning and emergency response, and ensure the safety of terminal operations.

[0043] In one embodiment, the data resource subsystem 100 includes a data integration module 110 and a data management module 120. The data integration module 110 collects multi-dimensional data in the terminal through sensors and monitoring equipment, converts it into digital signals and stores it uniformly. The data management module 120 relies on the data resource platform and API interface to connect the terminal production control knowledge graph platform and the global data governance platform, thereby building an efficient terminal data management system.

[0044] Specifically, the data integration module 110 is used to collect and integrate various multi-source heterogeneous data of terminal operations, realize the comprehensive collection and unified storage of multi-dimensional data in the port, ensure the real-time and accuracy of the data, and provide a basis for subsequent intelligent analysis. The data integration module 110 collects multi-dimensional information such as environmental parameters, equipment status and cargo flow in the port through sensors and monitoring equipment, and converts it into digital signals and stores it in a unified database. The module supports the integration of equipment data, business data, monitoring data and third-party data, and provides real-time and accurate data sources. Among them, equipment data includes the core operation data of equipment in the production scene, such as the operation data of yard cranes and container trucks, business data covers the records of business activities such as gates, yards, and unpacking and loading containers, and monitoring data involves video data and personnel trajectories in the scene, while third-party data comes from the outside, such as frontier ports and local container handling platforms.

[0045] Generally speaking, the mechanism of a crane includes a lifting mechanism, a trolley mechanism, and a traveling mechanism. In addition to the above mechanisms, there are also key mechanism components such as brakes, wire ropes, and pulleys.

[0046] In the management method of the present invention, step S100 further includes the following steps:

[0047] Sensor subsystems are arranged in the crane's lifting mechanism, trolley driving mechanism, carriage traveling mechanism, driver's cab and machine room.

[0048] Acquire data from the sensor subsystem as real-time data information of the crane.

[0049] Furthermore, the sensor subsystem includes a radial vibration sensor, a liquid level sensor, a liquid temperature sensor, a rotation speed sensor, an acceleration sensor and a sound sensor.

[0050] On the hoisting mechanism of the crane, a motor vertical radial vibration sensor (with temperature integrated) and a motor horizontal radial vibration sensor are arranged on the motor output shaft of the hoisting mechanism, a radial vibration sensor is arranged on the reducer input shaft, reducer output shaft and bearing seat of the hoisting mechanism, a liquid level sensor and a liquid temperature sensor are arranged on the end face of the high-speed shaft of the reducer of the hoisting mechanism, and a speed sensor is arranged on the high-speed coupling of the hoisting mechanism. On the trolley mechanism of the crane, a motor radial vibration sensor (with temperature integrated) is arranged on the motor output shaft of the trolley mechanism, a radial vibration sensor is arranged on the reducer input shaft, reducer output shaft and bearing seat of the trolley mechanism, a liquid level sensor and a liquid temperature sensor are arranged on the end face of the high-speed shaft of the reducer of the trolley mechanism, a speed sensor is arranged on the high-speed coupling of the trolley mechanism, and an acceleration sensor is arranged on the bearing seat of the trolley wheel of the trolley mechanism. On the trolley traveling mechanism, an acceleration sensor is arranged on the bearing seat of the trolley wheel of the trolley traveling mechanism. Sound sensors are arranged in the driver's cab and the machine room.

[0051] The measurement point arrangement of the above sensors is summarized in Table 1:

[0052] Table 1 Crane sensor layout

[0053]

[0054]

[0055] After completing the arrangement of the sensors according to Table 1, the data of the above sensors are obtained in real time as the real-time data information of the crane.

[0056] Among them, the sound sensors need to be arranged according to the actual situation on site and the optimal spatial position for sound positioning. Since the sound signal collection needs to be carried out under the working conditions of the on-site crane, it is inevitable that the sound signal will be affected by the external background noise during the collection process. Sound signals containing background noise are difficult to extract, which affects the monitoring effect. Therefore, it is necessary to pre-process the sound signal in order to extract useful sound signals from the complex background noise. The specific steps to remove the background noise of the sound signal are as follows:

[0057] The background noise and sound signals under the working conditions of the crane trolley on site are directly collected, and their spectrograms are compared.

[0058] Through comparative analysis, find out the area where the background noise energy is most concentrated in the frequency domain.

[0059] The data of the collected sound signal in the above-mentioned area is set to zero to reconstruct the sound signal.

[0060] In the data resource subsystem 100, the data management module 120 provides multi-level data management and processing functions, and supports dynamic processing and storage of a large amount of heterogeneous data. The data management module 120 relies on the API interface to connect the knowledge graph platform and the big data governance platform of the terminal production control. It mainly includes the data transmission layer, access layer, message layer, scheduling layer, cache layer, storage layer, computing layer and data preprocessing layer. The transmission layer ensures the stability of data during transmission through functions such as heartbeat mechanism, reconnection mechanism and whitelist. The access layer includes relationship analysis, routing and cache function modules to ensure efficient and reliable data access. The message layer queues and schedules the data flow. The scheduling layer manages data scheduling, task scheduling and distributed locks to ensure efficient execution of tasks. The cache layer and storage layer respectively realize storage cache, read-write separation, time series data storage and other functions. The data preprocessing layer is responsible for data deduplication, screening, cleaning and enhancement processing to ensure data quality and consistency, and provide strong support for intelligent analysis and decision-making at the upper level.

[0061] Specifically, the data management module 120 realizes the local monitoring function of the crane through the onboard black box, which is responsible for collecting the monitoring signals of the crane mechanism, performing time domain analysis, frequency domain analysis and time-frequency analysis locally on the crane, and monitoring and warning the identified fault types through threshold over-limit detection, model matching identification, etc.

[0062] The onboard black box can communicate with the crane PLC (Programmable Logic Controller) to obtain more operating data of the crane and monitor the status according to the current working condition of the crane. When a fault is detected, it will send out a warning and alarm signal to notify the crane control PLC, and then take appropriate countermeasures.

[0063] The data resource subsystem 100 collects and integrates multi-dimensional data within the terminal in real time to form a unified data source to support upper-level intelligent services; through automated data processing and integration, the accuracy and real-time nature of data management can be effectively improved, providing data support for intelligent decision-making and efficient operations of the terminal.

[0064] In one embodiment, the equipment resource subsystem 200 includes a terminal integrated resource intelligent management module 210 and a terminal equipment intelligent maintenance module 220. The subsystem realizes intelligent maintenance of equipment through unified management of terminal equipment and based on new generation artificial intelligence technologies such as machine learning, thereby supporting efficient use of equipment.

[0065] Specifically, the terminal integrated resource intelligent management module 210 supports unified resource scheduling of various operation plans through resource integration and sharing. The terminal integrated resource intelligent management module 210 is used to integrate and share scattered resources such as operation equipment, yard space, lanes, and manpower, realize reasonable allocation of resources through a unified resource pool, and support resource coordination between different operation plans.

[0066] In the terminal integrated resource intelligent management module 210, equipment management mainly focuses on the file management of crane equipment and various large-scale maintenance equipment. Spare parts management realizes various management functions for major spare parts, and can regularly check the inventory of spare parts through the intelligent diagnosis and prediction results of the previous system, and make procurement suggestions. Other asset management functions mainly realize the information management of other related assets of the terminal. Other assets here refer to other equipment other than crane equipment and maintenance equipment, such as office equipment, tools and supplies, labor protection supplies and other material assets.

[0067] Specifically, the terminal equipment intelligent maintenance module 220 provides intelligent maintenance and management of equipment through equipment status monitoring and fault prediction functions. The terminal equipment intelligent maintenance module 220 is used to monitor the status and diagnose faults of terminal equipment, and supports intelligent maintenance. It performs fault prediction and equipment health assessment through online monitoring of equipment operation data, and performs preventive maintenance management in combination with real-time data, supporting continuous monitoring and maintenance of equipment.

[0068] For crane mechanisms and their parts, common faults include gear wear, eccentricity, local wear cracks, broken teeth of gearboxes, roller failure, inner and outer ring failure of bearings, stator failure, rotor failure of motors, and mechanical imbalance failure.

[0069] In step S200 of the management method of the present invention, the steel structure fault diagnosis includes key point safety diagnosis and macroscopic state diagnosis. Specifically, the steel structure fault diagnosis is mainly carried out on the deformation of the stress release holes at the key points of the steel structure, the vibration amplitude in the direction of the large and small vehicles, and the natural frequency in the direction of the large and small vehicles, so as to realize the monitoring of the macroscopic state safety of the steel structure and whether the key points are cracked. The fault diagnosis of the steel structure is mainly based on the expert experience threshold and the physical model limitation. Considering the real-time safety assessment of the steel structure, it is deployed on a single system machine, which belongs to the equipment-level fault diagnosis.

[0070] In one embodiment, the steel structure fault diagnosis for each crane in step S200 further includes steps S211 to S212:

[0071] In step S211, the radial displacement of the stress release hole at the key point of the steel structure is obtained. If the radial displacement is greater than the deformation threshold, it is determined that the key point has a potential risk of structural damage.

[0072] In step S212, key points with potential structural damage risks are used as alarm points, and the alarm points are determined to be in a fault state.

[0073] Steps S211 to S212 are safety diagnoses for key points in steel structure fault diagnosis. The fault characteristic parameters for safety diagnosis of key points of steel structures are the deformation of the stress release holes at each key point, which is mainly described by the radial displacement of the stress release holes. When the displacement is too large, it means that the stress release hole has undergone irreversible plastic deformation, and the key point is potentially at risk of fracture and structural damage, so it is necessary to strictly monitor the relevant data. The fatal node displacement should at least include the displacement of the fatigue dangerous position of the front tie rod, the radial displacement of the stress release hole of the rear support tube, and the radial displacement of the stress release hole of the rear tie rod. Therefore, it is necessary to strictly monitor the relevant data. The fault characteristic quantities for early warning of cracking of key points mainly include the radial displacement of the stress release hole at the fatigue dangerous position of the front tie rod, the radial displacement of the stress release hole of the rear support tube, and the radial displacement of the stress release hole of the rear tie rod.

[0074] A physical model of key point cracking is formed by combining fracture mechanics and specific specification parameters of steel structures, thereby determining the warning threshold for cracking of key nodes of steel structures. When the deformation of the stress release holes at each key point exceeds the corresponding threshold, it is considered that there is a greater risk of key node cracking, and an alarm signal is generated to prompt timely maintenance.

[0075] In one embodiment, the important mechanism fault diagnosis of each crane in step S200 further includes steps S213 to S214:

[0076] In step S213, the vibration amplitude in the direction of the vehicle and the natural frequency in the direction of the vehicle and the vehicle are obtained.

[0077] In step S214, if the vibration amplitude in the direction of the vehicle is greater than the vibration amplitude threshold or the natural frequency in the direction of the vehicle is greater than the natural frequency threshold, it is determined that the steel structure is in a fault state.

[0078] Steps S213 to S214 are for the diagnosis of macroscopic states in steel structure fault diagnosis. The fault characteristic parameters of the macroscopic state diagnosis of steel structures mainly include the vibration amplitude in the direction of the trolleys and the natural frequency in the direction of the trolleys. The vibration amplitude in the direction of the trolleys and the trolleys mainly describes the overall vibration state of the steel structure during the operation of the trolleys and the trolleys. When the vibration amplitude is too large, it will accelerate the fatigue of the related structures and cause irreversible damage to the related operating parts of the trolleys and the trolleys; at the same time, it also shows to a certain extent that there are certain safety hazards in the related steel structures, resulting in the increase of the vibration amplitude of the trolleys and the trolleys under the same working conditions. The vibration amplitude in the direction of the trolleys and the trolleys mainly includes the vibration amplitude in the direction of the trolleys in the front reach, the vibration amplitude in the direction of the trolleys in the middle of the upper beam span on the sea side, the vibration amplitude in the direction of the trolleys in the middle of the upper beam span on the sea side, the vibration amplitude in the direction of the trolleys in the middle of the upper beam span on the land side, and the vibration amplitude in the direction of the trolleys in the rear reach.

[0079] The natural frequency in the direction of large and small vehicles mainly reflects the change in the performance of the steel structure itself. If the steel structure is fault-free, its natural frequency in the direction of large and small vehicles should be maintained within a certain range.

[0080] Based on user technical specifications and expert experience, the thresholds of the vibration amplitude and natural frequency in the direction of the large / small car of the steel structure are specified. When the vibration amplitude or the natural frequency in the direction of the large / small car exceeds the corresponding threshold, it is considered that there is a fault in the macroscopic state of the steel structure, and the key welds and connections of the corresponding structure should be inspected and repaired in time.

[0081] In step S200 of the management method of the present invention, important mechanism fault diagnosis includes threshold-based mechanism overall fault diagnosis and mechanism fault pattern recognition based on machine learning / deep learning.

[0082] In one embodiment, the important mechanism fault diagnosis of each crane in step S200 further includes steps S221 to S222:

[0083] In step S221, the vibration severity of important mechanisms is determined, wherein the important mechanisms include a lifting mechanism, a trolley mechanism, and a pitch mechanism.

[0084] In step S222, if the vibration intensity of the important mechanism is greater than the intensity threshold, it is determined that the important mechanism is in a fault state.

[0085] Among them, the vibration intensity of important mechanisms meets the following requirements:

[0086]

[0087] The vibration of this important mechanism is composed of n simple harmonic vibrations of different frequencies, n≥1, v(t) is the vibration velocity signal of this important mechanism, V rms It is the vibration intensity of this important mechanism.

[0088] Steps S221 to S222 are threshold-based overall fault diagnosis of the mechanism in the diagnosis of important mechanism faults. When the vibration intensity of the mechanism exceeds the set threshold, it is considered that the mechanism has failed and further fault identification and maintenance should be performed.

[0089] The threshold-based overall fault diagnosis of the mechanism is deployed on a single system machine due to its small amount of calculation and real-time considerations, and belongs to device-level fault diagnosis.

[0090] In one embodiment, the steel structure fault diagnosis of each crane in step S200 further includes steps S223 to S228:

[0091] In step S223, data preprocessing is performed through noise reduction and order analysis algorithms to suppress noise interference and highlight fault characteristics.

[0092] In step S224, time domain features, frequency domain features and time-frequency domain analysis are extracted to establish a vibration feature parameter library. The time domain features include RMS, peak value, kurtosis, kurtosis index and pulse index; the frequency domain features include characteristic frequencies and energy functions such as bearing outer ring fault, bearing inner ring fault and bearing rolling element fault; the time-frequency domain analysis includes wavelet transform, EMD decomposition, HHT analysis and Gabor transform.

[0093] In step S225, the amount of computation in the fault diagnosis process and the transmission and storage pressure of the relevant data platform are reduced by the PCA principal component analysis method. Among them, the PCA principal component analysis method is a multivariate statistical analysis method. Its main purpose is to transform multiple variables of the original data into a set of new, mutually independent (orthogonal) variables through linear transformation. These new variables are called principal components. The principal components are linear combinations of the original variables and are arranged in order according to the size of the variance.

[0094] In step S226, the model is trained and updated through the classification model, wherein the classification model includes SVM (support vector machine), neural network and LDA linear discriminator.

[0095] In step S227, the effective feature vector of the real-time vibration signal is extracted in the terminal central control as the input of the trained / updated model to obtain the model classification result.

[0096] In step S228, a technician confirms on-site whether the diagnosis result is correct. If it is wrong, the diagnosis result is manually corrected, and this part of the data and the correct result are entered into the database and sent to the remote center to re-update the training model.

[0097] In this embodiment, feature extraction is performed using time domain features, spectrum features, time-frequency features, inverse spectrum features, etc., and diagnostic models such as support vector machine (SVM) and hidden Markov model and retrograde diagnosis are used. Intelligent prediction models include support vector regression machine, fuzzy logic, similarity matching, gray prediction and deep learning.

[0098] When equipment-level fault diagnosis finds that a certain mechanism has a fault, the fault data can be sent to the terminal central control, and the fault mode can be further identified and classified by calling the machine learning / deep learning model established on the cloud platform.

[0099] Generally speaking, electrical system fault alarms are mainly based on switch quantities, and the system itself has realized mature alarm functions. However, in actual applications, the faults reported by the electrical system are mostly fault symptoms, and technicians are required to manually troubleshoot the faults. At the same time, false alarms may occur, misleading maintenance and wasting time and effort. This is because the faults in the electrical system are highly correlated, and one fault often leads to multiple other faults.

[0100] In one embodiment, the electrical system fault diagnosis of each crane in step S200 further includes steps S231 to S236:

[0101] In step S231, a fault tree model is established based on historical fault data and expert experience;

[0102] In step S232, based on the established fault tree model, matching top events are automatically found according to the input electrical system fault phenomena to generate a top event fault tree.

[0103] In step S233, the minimum cut set of the top event fault tree is obtained.

[0104] In step S234, the importance of each minimum cut set is calculated.

[0105] In step S235, a sequential detection process is generated according to the principle of giving priority to detecting the minimum cut sets with greater importance.

[0106] In step S236, the test is performed in the order of the detection process to find out the source of the fault.

[0107] If a fault phenomenon occurs that cannot be matched with the existing fault tree, logic gates and events should be added to the original fault tree to add a new fault subtree.

[0108] Furthermore, in step S231, historical fault data of the electrical system is mainly sorted out in combination with expert experience, the logical connection between various faults is clarified, and a fault tree is generated.

[0109] All rules of the sorted fault database and expert experience are organized into several trees. The leaf node of each tree corresponds to a fault phenomenon or auxiliary information, and the non-leaf node corresponds to a fault conclusion. The relationship between the parent node and the child node constitutes the rule. The root node, node hierarchy, node relationship, leaf node and other information of the fault tree are recorded in the database.

[0110] The fault tree establishment function is deployed in the remote center. When the fault tree is established, it is sent to the terminal central control, where electrical fault reasoning is performed.

[0111] In this embodiment, an electrical system fault tree is established based on historical fault statistics and expert experience, and a detailed qualitative and quantitative analysis is performed on the possible failure modes of the electrical system and their influencing factors; and based on the fault tree, an electrical system fault inference engine is established to provide a sequential troubleshooting strategy.

[0112] Step S300 of the management method of the present invention further includes steps S310 to S320:

[0113] In step S310, if the fault diagnosis result is a fault state, emergency maintenance is performed on the crane.

[0114] In step S310, if the fault diagnosis result is a non-fault state, maintenance processing is performed on the crane.

[0115] The emergency maintenance of the crane in step S310 further includes the following steps:

[0116] Record the failure information of the crane and form an emergency maintenance work order.

[0117] Determine the priority of the emergency repair work order based on the fault information corresponding to each emergency repair work order.

[0118] Arrange equipment maintenance personnel to go to the site to handle the fault and record the relevant information of the on-site maintenance.

[0119] If the emergency repair work order is completed, the emergency repair work order is set to be processed. If the emergency repair work order is not completed, the emergency repair work order is reassigned or transferred.

[0120] If the completed emergency repair work order requires subsequent repair, the emergency repair work order will be processed for maintenance.

[0121] The maintenance process for the crane in step S320 further includes the following steps:

[0122] Set the maintenance check item definition table for the crane. Specifically, the user sets and maintains the equipment maintenance check item definition, including adding, deleting, editing, and visibility of maintenance items.

[0123] By utilizing the maintenance service program running in the background, the maintenance inspection task item list is automatically updated according to the equipment operation statistical data interface and the maintenance inspection item definition table.

[0124] Through the interface of the predefined crane mechanism fault prediction module, the prediction results are obtained and the relevant maintenance and inspection task items are generated into a list.

[0125] Through the interface of the emergency repair module, the tasks transferred from the emergency repair module to the daily maintenance module are obtained, and the relevant maintenance inspection task items are automatically generated into a list according to the predefined format.

[0126] Through the interface of the production planning system, the production schedule is obtained from the production planning system and the equipment available time schedule is updated, and the equipment maintenance time schedule is arranged according to the production plan gaps.

[0127] Generate equipment inspection plan and inspection and maintenance plan work order. Equipment inspection plan and inspection and maintenance plan work order can be generated manually or automatically.

[0128] After confirmation and approval, a formal work order is generated, including inspection work orders, maintenance and regular inspection work orders, etc.

[0129] In combination with the above embodiments, the equipment resource subsystem 200 of the management system of the present invention can realize terminal resource sharing and support the formulation of better operation plans through an integrated resource intelligent management module; through new-generation artificial intelligence technologies such as machine learning, it can realize intelligent maintenance of equipment and support the efficient use of terminal equipment.

[0130] In one embodiment, the operation control subsystem 300 includes a terminal equipment intelligent scheduling and group control management module 320 and a terminal intelligent operation planning module 310. The subsystem improves the efficiency of terminal resource utilization by optimizing resource allocation and equipment scheduling, and realizes efficient operation through the intelligent planning module.

[0131] Specifically, the terminal equipment intelligent scheduling and group control management module 320 is used to monitor and schedule the operating equipment in the terminal, including AGV, quay cranes, yard cranes and other equipment. It monitors the equipment status in real time and reasonably allocates tasks based on the requirements of the operating tasks. It realizes collaborative work among multiple devices and supports the automated scheduling and dynamic group control of the equipment.

[0132] In the terminal equipment intelligent scheduling and group control management module 320, multiple cranes are integrated into a unified group control platform. Based on real-time data collection and analysis, the cranes' work tasks are dynamically allocated to ensure the rational use of resources and avoid equipment conflicts and idle waiting. The intelligent scheduling algorithm comprehensively considers the ship's berthing time, cargo loading and unloading requirements, equipment performance and operating load, automatically generates the optimal scheduling plan, and dynamically adjusts it according to the progress of on-site operations and emergencies. Through energy consumption monitoring and safety warning functions, the system effectively reduces energy consumption, ensures the safe operation of equipment, and improves the overall operating efficiency and controllability of the terminal.

[0133] Specifically, the terminal intelligent operation planning module 310 generates operation plans for berths, loading and unloading, and gates based on information such as the arrival of ships, weather conditions, and tides. It also supports automatic generation and dynamic adjustment of plans, and formulates optimal berth and loading and unloading operation plans based on real-time data to adapt to the port's changing operational needs.

[0134] In the terminal intelligent operation planning module 310, a detailed operation plan is automatically generated based on data such as the ship's berthing time, cargo loading and unloading list, and operation area, and the task allocation and operation sequence of the cranes are arranged. Using intelligent algorithms, the system optimizes the operation priority, dynamically matches the cranes with the task requirements, and ensures the optimal configuration of the operation resources. When there are operation delays, equipment failures, or other emergencies, the system monitors the operation progress in real time and automatically adjusts the plan to ensure the continuity and efficient advancement of the task.

[0135] The operation control subsystem 300 of the management system of the present invention can realize the intelligent deployment and group control management of terminal equipment and resources through the operation planning and equipment intelligent scheduling module, optimize the overall configuration of terminal resources, improve operation efficiency, reduce equipment idleness and congestion, thereby improving the overall operation efficiency and resource utilization of the terminal.

[0136] Furthermore, the safety supervision subsystem 400 includes a terminal intelligent operation monitoring module 410 and a terminal integrated digital twin safety supervision module 420. Through real-time monitoring and data analysis, it integrates fire protection, security and hazard detection equipment in the terminal to achieve global safety monitoring and emergency management of the work site.

[0137] Specifically, the terminal intelligent operation monitoring module 410 supports dynamic adjustment of operation links based on real-time analysis of operation data. It is used to analyze and monitor the operation status of the terminal, monitor the status of each link in the production chain through multiple dimensions, and conduct operation evaluation through KPI indicators. It is convenient to identify and adjust the resource input of each operation link, and support managers to monitor and analyze the overall status of the production chain.

[0138] In the terminal intelligent operation monitoring module 410, the black box monitoring software sends the original data, secondary signals, processed model data, warning signals, etc. to the central control room through a message mechanism. The crane status monitoring module of the central control room adopts a B / S architecture, and users monitor the status of the crane mechanism through a web page. It includes an overview of the status of the terminal crane, single-machine crane status monitoring, single-machine steel structure safety monitoring, steel structure remaining life monitoring, mechanism status monitoring, and mechanism parts status monitoring. The monitoring content includes not only the original signal status, but also the warning fault signal after signal analysis and processing, as well as the recent status of the equipment and abnormal signal statistics. The central control server runs a crane fault intelligent diagnosis and prediction module in the background, and its identification, prediction and evaluation results are also presented through the quay crane status monitoring interface.

[0139] The overall monitoring screen of the terminal crane can intuitively display the actual working conditions of the entire terminal machinery.

[0140] The terminal operation status includes system KPI indicators, machine overall parameter information, weather information and yard information. Among them, the system KPI indicators include the total number of various crane models, the number of online cranes, the number of faults, and the number of standard containers loaded and unloaded. The overall machine parameter information includes the machine number, machine status, current task type, and machine operation time. The yard information is used to display all crane models that need to be monitored on the current terminal. Among them, the location of the machine is consistent with the actual working conditions, making the monitoring screen more intuitive.

[0141] In the overview screen of terminal crane monitoring, expand crane monitoring and click any machine number to enter the monitoring screen of a single crane.

[0142] In the monitoring screen of a single crane, the operating status of a single crane includes the task information of the current machine, statistics of the number of operating boxes, real-time working condition simulation of the whole machine, information of the spreader, working status of each subsystem and number of failures of the mechanism, running time statistics, speed, operation permission status and wind speed information. Among them, the information of the spreader includes opening and closing locks, box landing signals and twist lock systems. Among them, opening and closing locks means that when the crane spreader docks with the cargo, the twist lock will switch from the open state (unlocked) to the closed state (locked) to firmly grasp the cargo; when the spreader completes the handling and needs to release the cargo, the twist lock will switch from the locked state to the unlocked state to release the cargo. The box landing signal means that when the crane spreader is accurately docked with the lock hole or contact point of the container, the system will send a box landing signal to confirm that the spreader can perform the next locking operation; if the box landing signal is not triggered, it may mean that the spreader is not completely docked with the cargo, and the system will prompt the operator to check the docking situation to ensure the safety of the cargo. TLS (Twist Lock System) means that the twist lock mechanism on the crane spreader needs to mechanically lock the cargo to prevent the cargo from slipping or shifting during lifting or moving. When the spreader grabs the container, the twist lock on the spreader will rotate to lock the container firmly; when releasing the cargo, the twist lock will rotate back to its original position and release the lock.

[0143] The number of failures of an organization can be counted on a monthly basis and can be searched based on the CATEGORY in the database. The failure types refer to the CLASS, which are generally failures, alarms, and events.

[0144] Specifically, the real-time monitoring component of the integrated digital twin safety and monitoring module 420 of the terminal is connected to a variety of safety equipment, the hidden danger management component builds a digital hidden danger record and feedback process, the risk analysis and early warning component establishes a risk model and early warning mechanism, the emergency plan and response component configures on-site emergency measures, the three-dimensional visualization management component presents the on-site status on the client, and the data management and analysis component processes and pushes data. All components work together to achieve comprehensive safety management of the terminal operation site.

[0145] The terminal integrated digital twin safety and monitoring module 420 accesses the data of fire protection, security, video surveillance, harmful gas detection, temperature sensors, pressure sensors and other equipment in the terminal through real-time monitoring components, continuously monitors the status and environmental parameters of the equipment, and maps the monitoring results to the digital twin model; through the hidden danger management component, based on real-time data and historical safety events, it identifies potential hidden dangers, builds a full-process management model for hidden dangers, digitally manages the discovery, recording, processing and feedback processes of hidden dangers, and generates closed-loop management records of hidden dangers; through the risk analysis and early warning component, it uses risk data and model libraries to analyze risk sources, builds a risk hazard model, refines the influencing factors and scope of risks, generates early warning information, and displays it in the digital twin. The risk points are marked in the production platform; through the emergency plan and response components, standard emergency plans are built-in and customized configuration is supported. The corresponding plans are automatically matched according to the on-site monitoring information in real time, and guidance information is provided in emergencies, supporting the rapid dispatch of emergency resources and risk control; through the 3D visualization management component, WebGL technology is used on the browser side to render the 3D scene, present the real-time operation status and environmental information of the terminal site, realize visual monitoring and operation, and display the safety status of the entire terminal through a "one-picture" interface; through the data management and analysis component, the server processes and stores safety monitoring data, and uses data analysis technology to process various sensor data so that it can be pushed to the client in real time, and supports historical data query and backtracking.

[0146] In the terminal integrated digital twin safety and monitoring module 420, the main mechanisms, important mechanism parts, electrical devices, etc. of the crane are monitored and fault prediction or life estimation is performed according to needs and data conditions. The system provides a fault prediction or life estimation dashboard for the above equipment, components, and devices. For example, by combining the actual crane hoisting times, load and other data statistics, as well as historical actual replacement records, wire rope life estimation and relay fault prediction can be achieved through long-term accumulation.

[0147] The safety supervision subsystem 400 of the management system of the present invention is based on real-time monitoring and digital twin technology, and conducts "one-picture" centralized monitoring of the safety operations of the entire terminal. It monitors the status of various safety equipment such as fire protection, security and harmful gas detection in real time, and combines real-time early warning and emergency response functions. It can effectively improve the safety of the terminal's operating environment and ensure a smooth and orderly production process.

[0148] In summary, the management system of the present invention adopts a modular design, supports independent operation and flexible configuration of each module, and is easy to adapt to the changing needs of terminal business. At the same time, through intelligent and dynamic data analysis and scheduling, the system can adapt to complex and changing operation scenarios in real time, reduce the operating costs of the terminal and improve service quality.

[0149] The present invention solves the problems existing in the current automated terminal management, such as unreasonable resource allocation, low equipment utilization, insufficient operational management efficiency, and inability to fully cover safety monitoring. The present invention predicts the maintenance needs of equipment based on data to prevent downtime caused by sudden failures, thereby improving the availability and life of the equipment; optimizes the operation plan of the crane, maximizes the utilization of the equipment, and further improves the overall operation efficiency and resource management level of the terminal; monitors the operating status of the crane in real time, collects new data and enables efficient terminal operations. The problem of lack of efficient and intelligent support in data integration, equipment scheduling, safety management, etc. is solved, thereby optimizing the utilization of terminal resources, operating efficiency and safety.

[0150] The control device of the present invention comprises: a memory and a processor, wherein the processor is connected to the memory and is configured to implement a management method for a dock crane.

[0151] The storage medium of the present invention is used to store non-transitory computer instructions. When the non-transitory computer instructions are executed, the management method of the dock crane is executed.

[0152] The computer program product of the present invention comprises a computer program. When the computer program is executed by a processor, the management method of the quay crane is implemented.

[0153] Embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier to be executed by a data processing apparatus or to control the operation of the data processing apparatus. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagated signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information and transmit it to a suitable receiver apparatus for execution by the data processing apparatus.

[0154] The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

[0155] Computers suitable for executing computer programs include, for example, general and / or special microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory and / or a random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, the computer will also include one or more large-capacity storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or the computer will be operably coupled to this large-capacity storage device to receive data from it or to transmit data to it, or both. However, the computer does not necessarily have such a device. In addition, the computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0156] Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including, for example, semiconductor memory devices (e.g., EPROM, EEPROM and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and memory may be supplemented by, or incorporated in, special purpose logic circuitry.

[0157] Although the present invention is disclosed as above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications without departing from the spirit and scope of the present invention. Therefore, any modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope defined by the claims of the present invention.

Claims

1. A management method for a dock crane, characterized in that: include: Get real-time data information of cranes; According to the real-time data information, steel structure fault diagnosis, important mechanism fault diagnosis and electrical system fault diagnosis are performed on each crane to obtain fault diagnosis results; Perform maintenance on the crane according to the fault diagnosis result; Combining the real-time data information of each crane and the fault diagnosis result, the terminal operation status and the single crane operation status are obtained; A digital twin model of the crane is established, the operation status of the terminal and the operation status of the single crane are mapped to the digital twin model of the crane, and the digital twin model of the crane is displayed.

2. The management method according to claim 1, characterized in that: The steel structure fault diagnosis of each crane includes: Obtaining the radial displacement of the stress release hole at the key point of the steel structure; if the radial displacement is greater than the deformation threshold, determining that the key point has a potential risk of structural damage; A key point with a potential risk of structural damage is taken as an alarm point, and the alarm point is determined to be in a fault state.

3. The management method according to claim 2, characterized in that: The steel structure fault diagnosis of each crane also includes: Obtain the vibration amplitude and natural frequency of the vehicle and the car; If the vibration amplitude in the direction of the vehicle is greater than the vibration amplitude threshold or the natural frequency in the direction of the vehicle is greater than the natural frequency threshold, it is determined that the steel structure is in a fault state.

4. The management method according to claim 1, characterized in that: The important mechanism fault diagnosis of each crane includes: Determine the vibration severity of important mechanisms; wherein the important mechanisms include a lifting mechanism, a trolley mechanism, and a pitch mechanism; If the vibration severity of the important mechanism is greater than the severity threshold, the important mechanism is determined to be in a fault state; The vibration intensity of the important mechanism meets the following requirements: The vibration of this important mechanism is composed of n simple harmonic vibrations of different frequencies, n≥1, v(t) is the vibration velocity signal of this important mechanism, V rms is the vibration intensity of this important mechanism.

5. The management method according to claim 4, characterized in that: The important mechanism fault diagnosis of each crane also includes: Data preprocessing is performed through noise reduction and order analysis algorithms; Extract time domain features, frequency domain features and time-frequency domain analysis to establish a vibration feature parameter library; The PCA principal component analysis method is used to reduce the amount of calculation in the fault diagnosis process and the transmission and storage pressure of the relevant data platform; Train and update the model through classification model; Extract the effective feature vector of the real-time vibration signal in the terminal central control as the input of the trained / updated model to obtain the model classification result; The technicians will confirm whether the diagnosis results are correct on site. If they are wrong, they will manually correct the diagnosis results, enter this part of the data and the correct results into the database and send them to the remote center to re-update the training model.

6. The management method according to claim 1, characterized in that: The electrical system fault diagnosis of each crane includes: Establish a fault tree model based on historical fault data and expert experience; Based on the established fault tree model, automatically searching for matching top events according to input electrical system fault phenomena, and generating a top event fault tree; Find the minimum cut set of the top event fault tree; Calculate the importance of each minimum cut set; Generate a sequential detection process based on the principle of giving priority to the detection of the most important minimum cut set; Perform the tests in the order of the detection process and find the source of the fault.

7. The management method according to any one of claims 1 to 6, characterized in that: The maintenance process of the crane is performed according to the fault diagnosis result, including: If the fault diagnosis result is a fault state, emergency maintenance is performed on the crane; The emergency maintenance of the crane includes: Record the failure information of the crane and form an emergency maintenance work order; Determine the priority of each emergency repair work order according to the fault information corresponding to each emergency repair work order; Arrange equipment maintenance personnel to go to the site to handle faults and record relevant information about on-site maintenance; If the emergency repair work order is completed, the emergency repair work order is set to be processed; if the emergency repair work order is not completed, the emergency repair work order is reassigned or transferred; If the completed emergency repair work order requires subsequent repair, the emergency repair work order will be maintained.

8. The management method according to claim 7, characterized in that: The performing maintenance on the crane according to the fault diagnosis result also includes: If the fault diagnosis result is a non-fault state, the crane is maintained; The maintenance of the crane includes: Set up a definition table of maintenance inspection items for cranes; Using the maintenance service program running in the background, according to the equipment operation statistical data interface, combined with the maintenance inspection item definition table, the maintenance inspection task item list is automatically updated; Obtain prediction results through the predefined interface of the crane mechanism fault prediction module and generate related maintenance and inspection task items into a list; Through the interface of the emergency repair module, the tasks transferred from the emergency repair module to the daily maintenance module are obtained, and the relevant maintenance inspection task items are automatically generated into a list according to the predefined format; Obtain the production schedule from the production planning system through the production planning system interface and update the equipment availability schedule, and arrange the equipment maintenance schedule according to the production plan gaps; Generate equipment inspection plan and inspection and maintenance plan work order; After confirmation and approval, a formal work order is generated.

9. The management method according to any one of claims 1 to 6, characterized in that: The real-time data information of the crane is obtained, including: Arrange sensor subsystems in the crane's hoisting mechanism, trolley drive mechanism, trolley travel mechanism, driver's cab, and machine room; The data of the sensor subsystem is acquired as real-time data information of the crane.

10. The management method according to claim 9, characterized in that: The sensor subsystem includes a radial vibration sensor, a liquid level sensor, a liquid temperature sensor, a rotation speed sensor, an acceleration sensor and a sound sensor; The sensor subsystem is arranged in the lifting mechanism, trolley mechanism, carriage travel mechanism, driver's cab and machine room of the crane, including: A motor vertical radial vibration sensor and a motor horizontal radial vibration sensor are arranged on the motor output shaft of the hoisting mechanism of the crane, the radial vibration sensor is arranged on the reduction box input shaft, the reduction box output shaft and the bearing seat of the hoisting mechanism, the liquid level sensor and the liquid temperature sensor are arranged on the end face of the high-speed shaft of the reduction box of the hoisting mechanism, and the speed sensor is arranged on the high-speed coupling of the hoisting mechanism; A motor radial vibration sensor is arranged on the motor output shaft of the trolley mechanism of the crane, a radial vibration sensor is arranged on the reduction box input shaft, reduction box output shaft and bearing seat of the trolley mechanism, a liquid level sensor and a liquid temperature sensor are arranged on the end face of the reduction box high-speed shaft of the trolley mechanism, a speed sensor is arranged on the high-speed coupling of the trolley mechanism, and an acceleration sensor is arranged on the bearing seat of the trolley wheel of the trolley mechanism; Arranging the acceleration sensor on the bearing seat of the trolley wheel of the trolley traveling mechanism; The sound sensors are arranged in the driver's cab and the machine room.

11. The management method according to any one of claims 1 to 6, characterized in that: The terminal operation status includes system KPI indicators, machine overall parameter information, weather information and yard information; The system KPI indicators include the total number of cranes of various types, the number of online cranes, the number of failures, and the number of standard containers loaded and unloaded; The overall machine parameter information includes the machine number, machine status, current task type and machine running time; The yard information is used to display all crane models that need to be monitored on the current dock; and / or The crane operation status includes the current machine task information, operation box quantity statistics, real-time working condition simulation of the whole machine, spreader information, working status of each subsystem and number of mechanism failures, operation time statistics, speed, operation permission status and wind speed information; wherein, The information of the spreader includes opening and closing locks, box landing signals and twist lock system.

12. A management system for a dock crane, characterized in that: include: Data resource subsystem, used to obtain real-time data information of the crane; The equipment resource subsystem is used to perform steel structure fault diagnosis, important mechanism fault diagnosis and electrical system fault diagnosis on each crane according to the real-time data information, obtain fault diagnosis results, and perform maintenance processing on the crane according to the fault diagnosis results; An operation control subsystem, used to obtain the terminal operation status and the single crane operation status by combining the real-time data information of each crane and the fault diagnosis result; The safety supervision subsystem is used to establish a digital twin model of the crane, map the operation status of the terminal and the operation status of the single crane to the digital twin model of the crane, and display the digital twin model of the crane.

13. A control device, characterized in that: include: Memory; as well as A processor is connected to the memory and is configured to implement the management method for a quay crane according to any one of claims 1 to 11.

14. A storage medium, characterized in that: Used to store non-transitory computer instructions, when the non-transitory computer instructions are executed, the management method of the quay crane according to any one of claims 1-11 is executed.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for managing a quay crane according to any one of claims 1 to 11 is implemented.

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