Large crane state real-time monitoring and task configuration system and method

Abstract

The invention discloses a large crane state real-time monitoring and task configuration system and method. The system comprises a state sensing layer, a data transmission layer, a data storage and processing layer, a task configuration layer and a man-machine interaction layer. According to the invention, a traditional large integration module is split into multiple layers of sub-modules, the fault of a single sub-module only affects the local function, the failure of the whole system is not caused, and the fault positioning is more accurate; key state features are extracted, task priorities are calculated, task allocation is determined by crane states, and resource mismatching caused by artificial experience is avoided; industrial LoRa and Ethernet transmission are adopted, butt joint with a slipway cloud platform and a process planning system is supported, the data format is compatible with other slipway resources, and the interconnection cooperation requirement is met; mechanical, hydraulic and electrical systems are covered, upgrading from single parameter monitoring to whole system monitoring is achieved, potential faults are recognized in advance, and safety risks are reduced.

Description

Technical Field

[0001] This invention relates to the field of digital shipyard resource modeling and interconnection technology, specifically to a system and method for real-time monitoring and task configuration of large crane status. Background Technology

[0002] In the construction of digital shipyards, large cranes, as core lifting equipment, directly affect the efficiency and safety of processes such as section lifting and component transfer. Existing crane monitoring systems often employ large-module integrated designs. For example, integrating all status monitoring functions into a single module means that a failure in any monitoring unit within the module will cause the entire monitoring function to fail, and fault location will be difficult. Furthermore, existing task allocation relies heavily on manual experience and is not linked to the real-time operating status of the cranes (such as load rate and structural health). This can easily lead to problems such as assigning heavy-load tasks to cranes with high load rates or assigning emergency tasks to cranes with abnormal conditions, resulting in resource waste or safety risks. In addition, existing systems lack interconnection interfaces with other resources on the digital shipyard (such as transport vehicles and lifting stations), making it difficult to integrate into the overall resource scheduling system of the shipyard and failing to meet the "interconnected and collaborative" construction requirements of digital shipyards.

[0003] Therefore, there is an urgent need for a real-time status monitoring and task configuration system and method for large cranes to solve the aforementioned technical problems. Summary of the Invention

[0004] The purpose of this invention is to provide a system and method for real-time monitoring and task configuration of large crane status, so as to solve the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a real-time status monitoring and task configuration system for large cranes, comprising a status perception layer, a data transmission layer, a data storage and processing layer, a task configuration layer, and a human-machine interaction layer;

[0006] The status perception layer includes a mechanical structure status perception module, a hydraulic system status perception module, and an electrical system status perception module; the mechanical structure status perception module includes a hook deformation sensing submodule and a boom vibration sensing submodule; the hydraulic system status perception module includes a hydraulic pressure sensing submodule and a hydraulic oil temperature sensing submodule; the electrical system status perception module includes a motor current sensing submodule and a control cabinet voltage sensing submodule.

[0007] The data transmission layer includes an edge data preprocessing submodule, a wireless transmission submodule, and a wired backup transmission submodule;

[0008] The data storage and processing layer includes a distributed data storage module, a state feature extraction module, and an anomaly diagnosis and analysis module; the state feature extraction module is used to calculate the effective value of the crane boom vibration acceleration using formula (1);

[0009] The task configuration layer includes a task requirement parsing module, a resource matching module, and a task scheduling module; the task scheduling module is used to calculate task priority using formula (2);

[0010] The human-computer interaction layer includes a status display module, an operation command input module, and an alarm prompt module.

[0011] Preferably, in the mechanical structure state sensing module, the hook deformation sensing submodule uses a strain gauge sensor, which is attached to the stress concentration area of ​​the hook beam to collect the hook strain signal; the boom vibration sensing submodule uses a triaxial accelerometer, which is installed in the middle section of the boom to collect the boom's X-axis, Y-axis, and Z-axis vibration signals.

[0012] Preferably, the edge data preprocessing submodule of the data transmission layer uses wavelet transform algorithm for data denoising, linear normalization algorithm to convert data into 0-1 interval, and differential coding algorithm for data compression; the wireless transmission submodule uses LoRa communication technology to support simultaneous communication of multiple nodes; and the wired backup transmission submodule uses industrial Ethernet and TCP / IP protocol to realize data transmission.

[0013] Preferably, the distributed data storage module of the data storage and processing layer adopts a two-level storage architecture of "edge server and cloud"; the edge server stores nearly 72 hours of real-time monitoring data, and the cloud stores historical monitoring data; the storage format adopts JSON, which is compatible with the data interface of the digital shipyard cloud platform.

[0014] Preferably, the formula (1) of the state feature extraction module is:

[0015]

[0016] in, This represents the effective value of the crane boom vibration acceleration. This represents the number of sampling points for a single vibration signal. For the first The original vibration acceleration values ​​at each sampling point.

[0017] Preferably, the abnormal diagnosis and analysis module adopts the diagnostic logic of "threshold comparison and trend judgment"; threshold comparison is to compare the state feature parameters with the preset normal threshold and mark "potential abnormality"; trend judgment is to perform linear fitting on the state feature parameters for 5 consecutive minutes, and if the absolute value of the fitting slope exceeds the preset range, it is judged as "trend abnormality"; the diagnosis results are divided into 4 levels: "normal", "potential abnormality", "trend abnormality" and "severe abnormality".

[0018] Preferably, the task requirement parsing module of the task configuration layer is used to parse the task instructions issued by the digital shipyard cloud platform and extract the task type, task load, task urgency, and task-related workstation information; the resource matching module is used to filter cranes that are "load-matched, in good condition, and in similar location"; the formula (2) of the task scheduling module is:

[0019]

[0020] in, As a task priority, This represents the urgency level of the task. For the real-time load rate of the crane, The importance coefficient of the associated processes in the task. , , The weighting coefficients and .

[0021] Preferably, when the anomaly diagnosis and analysis module outputs "trend anomaly" or "serious anomaly", the alarm prompt module of the human-computer interaction layer activates an audible and visual alarm, pushes an alarm message to the mobile terminal, and records the alarm log to the distributed data storage module.

[0022] The configuration method for a real-time status monitoring and task configuration system for large cranes includes the following steps:

[0023] Step 1: The mechanical structure, hydraulic system, and electrical system sensing modules of the state perception layer collect real-time operating data of the crane and output standard electrical signals;

[0024] Step 2: The edge data preprocessing submodule of the data transmission layer performs noise reduction, normalization, and compression on the standard electrical signal to obtain preprocessed data; the wireless transmission submodule transmits the preprocessed data to the shipyard edge gateway, and the wired backup transmission submodule automatically switches transmission when wireless transmission is interrupted.

[0025] Step 3: The distributed data storage module of the data storage and processing layer stores the preprocessed data; the state feature extraction module calls formula (1) to calculate the state feature parameters; the anomaly diagnosis and analysis module outputs the diagnosis results through "threshold comparison and trend judgment";

[0026] Step 4: The task requirement parsing module of the task configuration layer parses the shipyard task instructions; the resource matching module filters qualified cranes; the task scheduling module calls formula (2) to calculate the task priority and generate a task allocation scheme.

[0027] Step 5: The status display module of the human-computer interaction layer displays status data and task plans; the operation command input module supports manual intervention; and the alarm prompt module activates an alarm when an anomaly occurs.

[0028] Preferably, in step 4, the task scheduling module calculates the task priority according to formula (2). Tasks are assigned from largest to smallest value, the task assignment plan is sent to the crane control system, and feedback is provided to the human-machine interface layer.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] This invention breaks down traditional large integrated modules into multiple layers and sub-modules. A failure in a single sub-module only affects local functions and does not lead to the failure of the entire system, resulting in more accurate fault location. It extracts key state features and calculates task priorities, enabling crane status to determine task allocation and avoiding resource misallocation caused by human experience. It adopts industrial LoRa and Ethernet transmission, supporting integration with shipyard cloud platforms and process planning systems. The data format is compatible with other shipyard resources, meeting the needs of interconnection and collaboration. It covers the three major systems of mechanical, hydraulic, and electrical systems, achieving an upgrade from single parameter monitoring to full system monitoring, identifying potential faults in advance, and reducing safety risks. Attached Figure Description

[0031] Figure 1 This is a system schematic diagram of the present invention;

[0032] Figure 2 This is a schematic diagram of the hook deformation sensing submodule of the present invention;

[0033] Figure 3 This is a flowchart of the method of the present invention. Detailed Implementation

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

[0035] Please see Figure 1-3 This invention provides a real-time status monitoring and task configuration system for large cranes, including a status perception layer, a data transmission layer, a data storage and processing layer, a task configuration layer, and a human-machine interaction layer.

[0036] The state awareness layer is used to collect real-time operating data of the crane's mechanical structure, hydraulic system, and electrical system, including mechanical structure state awareness modules, hydraulic system state awareness modules, and electrical system state awareness modules.

[0037] The mechanical structure status sensing module includes a hook deformation sensing submodule and a boom vibration sensing submodule;

[0038] Hook Deformation Sensing Submodule: Employs strain gauge sensors, non-contactly installed in the stress concentration area of ​​the hook beam. By collecting strain signals of the hook during the hoisting process, it reflects the deformation state of the hook. The sensor outputs an analog signal, which is converted into a standard voltage signal by the built-in signal conditioning circuit (including amplification and filtering units) to avoid interference from shipyard vibrations on the original signal.

[0039] Crane boom vibration sensing submodule: It uses an accelerometer installed in the middle of the crane boom to collect three-axis vibration signals of the crane boom during lifting and luffing processes: X-axis: horizontal direction, Y-axis: vertical direction, and Z-axis: rotation direction. It is used to analyze the vibration intensity of the crane boom structure and determine whether there is a risk of resonance.

[0040] The hydraulic system status sensing module includes a hydraulic pressure sensing submodule and a hydraulic oil temperature sensing submodule;

[0041] Hydraulic pressure sensing submodule: Employs a pressure sensor connected in series at the interface between the main hydraulic circuit and the branch hydraulic circuit to collect the hydraulic system's supply and return pressure signals, and monitor in real time whether there are blockages or leaks in the oil circuit; the sensor has a built-in temperature compensation unit to avoid the influence of changes in the shipyard's ambient temperature on the pressure measurement.

[0042] Hydraulic oil temperature sensing submodule: It adopts a temperature sensor, which is inserted into the hydraulic oil tank to collect the real-time temperature of the hydraulic oil and prevent the hydraulic oil viscosity from decreasing and the system seal from failing due to excessive oil temperature.

[0043] The electrical system status sensing module includes a motor current sensing submodule and a control cabinet voltage sensing submodule;

[0044] Motor current sensing submodule: A current transformer is installed outside the power supply cable of the main hoisting motor and luffing motor of the crane to collect the motor operating current signal and determine whether the motor has overload or stall problems; the output signal of the transformer is processed by an isolation circuit to avoid electrical interference affecting the accuracy of the data.

[0045] Control cabinet voltage sensing submodule: It adopts a voltage sensor connected in parallel to both ends of the control cabinet DC bus to collect the control cabinet power supply voltage signal, monitor whether the voltage fluctuation exceeds the normal operating range, and prevent electrical components from being damaged due to abnormal voltage.

[0046] The data transmission layer is used to transmit the data collected by the state awareness layer to the data storage and processing layer, and at the same time realize data preprocessing, including the edge data preprocessing submodule, the wireless transmission submodule and the wired backup transmission submodule.

[0047] The edge data preprocessing submodule is integrated into the local acquisition terminal of each sensor in the state awareness layer, including:

[0048] Data denoising: Wavelet transform algorithm is used to denoise the original acquired data (such as vibration signals and current signals) to remove abnormal pulse signals generated by electromagnetic interference and mechanical impact from the shipyard.

[0049] Data normalization: Convert the output signals of different sensors (such as pressure 0-30MPa, temperature 0-100℃) into normalized data in the 0-1 range to facilitate subsequent unified analysis;

[0050] Data compression: Differential coding algorithm is used to compress continuously collected time-series data (such as oil temperature data every second) to reduce data transmission volume and adapt to the complex network environment of the shipyard.

[0051] The wireless transmission submodule adopts industrial LoRa wireless communication technology and is deployed on the top of the crane control cabinet and the edge gateway of the slipway to realize the wireless transmission of pre-processed data; it supports simultaneous communication of multiple nodes (up to 32 status sensing submodules can be connected), the communication distance covers the maximum operating radius of the slipway, and it has anti-interference capabilities (frequency hopping technology is used to avoid interference from other wireless devices in the slipway).

[0052] The wired backup transmission submodule uses industrial Ethernet (TCP / IP protocol) as a backup for wireless transmission, connecting the crane and the shipyard control center via armored cables. When wireless transmission is interrupted due to obstruction or interference, it automatically switches to wired transmission to ensure data transmission continuity, meeting the "high-reliability interconnection" requirements of digital shipyards.

[0053] The data storage and processing layer is used to store monitoring data and perform status analysis and anomaly diagnosis, including a distributed data storage module, a status feature extraction module, and an anomaly diagnosis and analysis module.

[0054] The distributed data storage module adopts a two-tier storage architecture of "edge server and cloud":

[0055] The edge server is deployed at the ship's control center, storing nearly 72 hours of real-time monitoring data for easy retrieval and analysis.

[0056] The system connects to a digital shipyard cloud platform to store historical monitoring data and supports data backtracking and trend analysis.

[0057] The storage format uses JSON, which is compatible with the data analysis interfaces of other resources on the digital slipway (such as transport vehicles and workstations).

[0058] The state feature extraction module performs feature calculations on the preprocessed monitoring data to extract key parameters reflecting the crane's state. Among them, the vibration state feature of the crane boom is calculated using the effective value formula (Formula 1):

[0059]

[0060] in, : The effective value of the vibration acceleration of the crane boom, used to characterize the vibration intensity;

[0061] The number of sampling points for a single vibration signal (e.g., N=1024, determined by the sensor sampling frequency, not involving specific accuracy parameters);

[0062] : No. The raw vibration acceleration values ​​of each sampling point are collected by the crane boom vibration sensing submodule.

[0063] The anomaly diagnosis and analysis module employs a diagnostic logic based on "threshold comparison and trend judgment":

[0064] Threshold comparison: The parameters output by the state feature extraction module (such as...) The system compares the hydraulic pressure and motor current with preset normal thresholds (set according to the crane model and shipyard operation specifications, without involving specific values). If the values ​​exceed the thresholds, they are marked as "potential abnormalities".

[0065] Trend judgment: Linear fitting is performed on the state characteristic parameters for 5 consecutive minutes. If the absolute value of the fitting slope exceeds the preset range, it is judged as "abnormal trend" (e.g., a continuous drop in hydraulic pressure may indicate oil leakage).

[0066] The diagnostic results are divided into four levels: "normal", "potentially abnormal", "trend abnormal" and "severely abnormal", providing a status basis for task configuration.

[0067] The task configuration layer is used to parse the requirements of the slipway hoisting task and combine the real-time status of the crane to realize task matching and scheduling, including a task requirement parsing module, a resource matching module and a task scheduling module;

[0068] The task requirement parsing module receives the hoisting task instructions issued by the digital shipyard cloud platform and parses the key task information:

[0069] Task type (e.g., segmented hoisting, component transfer);

[0070] Task load (e.g., lifting weight range);

[0071] Task urgency level (e.g., "urgent", "routine", "delayable");

[0072] Task-related workstations (such as the hoisting workstation in area A of the slipway and the transfer channel in area B) are used to ensure that the task is coordinated with other resources on the slipway.

[0073] The resource matching module matches task requirements with the crane's real-time status (from the anomaly diagnosis and analysis module):

[0074] Load matching: Select cranes with rated lifting capacity ≥ task load;

[0075] Status matching: Exclude cranes with "severe abnormality" or "abnormal trend" status, and prioritize cranes with "normal" status;

[0076] Location matching: Filter the cranes that are closest to the workstation associated with the task to reduce idle time.

[0077] The task scheduling module sorts the matched cranes by task priority, using the task priority calculation formula (Formula 2):

[0078]

[0079] in: Task priority (value range 0-1, higher value means higher priority);

[0080] : Task urgency level coefficient (urgency: =1, Normal: =0.6, can be delayed: =0.2 (dimensionless), obtained from the task requirement parsing module;

[0081] Crane real-time load rate (value range 0-1, =Current load / Rated load), calculated from the motor current collected by the electrical system status sensing module;

[0082] Importance coefficient of task-related processes (e.g., main hull welding process): =1, Auxiliary component assembly: =0.5 (dimensionless), issued by the digital shipyard process planning system;

[0083] , , Weighting coefficient It is set according to the priority of the slipway operation.

[0084] according to Tasks are assigned from highest to lowest value to achieve a scheduling logic that prioritizes urgent tasks, cranes in good condition, and critical process tasks.

[0085] The human-machine interface layer is used to display the crane status and task configuration results, and supports manual intervention. It includes a status display module, an operation command input module, and an alarm prompt module.

[0086] The status display module uses an industrial touchscreen, deployed in the shipyard control center and crane operator's cab, and displays information through a visual interface (such as dashboards and graphs).

[0087] Real-time status: Monitoring data from each sensing submodule (such as hook deformation strain value, hydraulic oil temperature);

[0088] Feature parameters: Output of the state feature extraction module Load rate, etc.;

[0089] Diagnostic results: The four levels of diagnostic results from the abnormality diagnosis analysis module are marked with different colors: normal: green, potential abnormality: yellow, trend abnormality: orange, and severe abnormality: red.

[0090] The operation command input module supports manual input of operation commands:

[0091] Status threshold adjustment: When the crane model changes or the operating scenario changes, the threshold for abnormal diagnosis is manually modified (e.g., adjusted). (Normal range)

[0092] Task intervention: When the automatic scheduling results do not meet the actual needs (such as an emergency task that requires temporary replacement of the crane), the task allocation results are manually adjusted;

[0093] Command issuance: Convert the task configuration results into crane operation commands (such as lifting height and luffing angle) and issue them to the crane control system.

[0094] The alarm notification module triggers an alarm when the anomaly diagnosis and analysis module outputs "trend anomaly" or "serious anomaly":

[0095] Audible and visual alarm: The audible and visual alarm in the control center is activated, and the indicator lights in the control room flash.

[0096] Message push: Push alarm information to the mobile terminals (such as industrial tablets) of shipyard dispatchers and crane operators, including the type of abnormality (such as "abnormal hydraulic oil temperature trend") and the location of the abnormality (such as "hydraulic oil tank").

[0097] Log recording: Automatically records alarm time, abnormal parameters, and processing results, and stores them in a distributed data storage module for easy traceability later.

[0098] The configuration method for a real-time status monitoring and task configuration system for large cranes includes the following steps:

[0099] Step 1: State-aware layer collects data

[0100] 1.1 Operation of the mechanical structure state sensing module: The hook deformation sensing submodule collects the hook strain signal, and the boom vibration sensing submodule collects the boom triaxial vibration signal;

[0101] 1.2 Operation of the hydraulic system status sensing module: The hydraulic pressure sensing submodule collects the oil circuit pressure signal, and the hydraulic oil temperature sensing submodule collects the hydraulic oil temperature signal;

[0102] 1.3 Operation of the electrical system status sensing module: The motor current sensing submodule collects the current signals of the main hoisting and luffing motors, and the control cabinet voltage sensing submodule collects the voltage signals of the control cabinet;

[0103] 1.4 Each submodule converts the acquired raw signals into standard electrical signals (such as 0-5V voltage and 4-20mA current) and outputs them to the data transmission layer.

[0104] Step 2: Data transmission layer processes and transmits data

[0105] 2.1 Edge data preprocessing submodule operation: Denoise (wavelet transform), normalize (0-1 interval), and compress (differential coding) the standard electrical signal output from step 1 to obtain preprocessed data;

[0106] 2.2 Operation of the wireless transmission submodule: Transmits preprocessed data to the ship's edge gateway via LoRa technology;

[0107] 2.3 Wired backup transmission submodule operation: Real-time monitoring of the wireless transmission link status. If the link is interrupted (e.g., signal strength < -80dBm), it automatically switches to industrial Ethernet transmission to ensure continuous transmission of pre-processed data to the data storage and processing layer.

[0108] Step 3: Data storage and processing layer analysis

[0109] 3.1 Distributed data storage module operation: The preprocessed data transmitted in step 2 is divided into "data from the last 72 hours" and "historical data", and stored on the edge server and the cloud respectively;

[0110] 3.2 State Feature Extraction Module Operation: Calculates the effective value of the crane boom vibration acceleration by calling formula (1). Simultaneously, other state characteristic parameters such as the average hydraulic pressure and the maximum motor current are calculated.

[0111] 3.3 Anomaly Diagnosis and Analysis Module Operation:

[0112] 3.3.1 Compare the feature parameters from step 3.2 with the preset normal threshold and mark them as "potential anomalies";

[0113] 3.3.2 Perform linear fitting on the characteristic parameters over 5 consecutive minutes to determine "abnormal trend";

[0114] 3.3.3 Based on the above results, output the Level 4 diagnostic results and transmit them to the task configuration layer and the human-computer interaction layer.

[0115] Step 4: Assign tasks at the task configuration layer

[0116] 4.1 Task Requirement Analysis Module Operation: Receives hoisting task instructions from the digital shipyard cloud platform and analyzes the task type, load, urgency level, and associated workstations;

[0117] 4.2 Resource matching module operation: Based on the task requirements in step 4.1 and the diagnostic results in step 3.3, select cranes that are “load-matched, in good condition, and in similar location”;

[0118] 4.3 Task scheduling module operation:

[0119] 4.3.1 Calculate the task priority of each matched crane by calling formula (2). ;

[0120] 4.3.2 According to Sort the values ​​from largest to smallest to generate a task allocation scheme;

[0121] 4.3.3 The task allocation plan is transmitted to the human-machine interaction layer, and operation instructions are sent to the crane control system at the same time.

[0122] Step 5: Human-Computer Interaction Layer Display and Intervention

[0123] 5.1 Status display module operation: Displays real-time data from step 1, characteristic parameters from step 3.2, diagnostic results from step 3.3, and task allocation scheme from step 4.3 on the industrial touch screen;

[0124] 5.2 Operation of the operation instruction input module: It supports manual adjustment of status thresholds or task allocation schemes, and the adjusted data is fed back to the data storage and processing layer or the task configuration layer;

[0125] 5.3 Alarm Notification Module Operation: If step 3.3 outputs "Abnormal Trend" or "Severe Abnormality", activate the audible and visual alarm, push a message to the mobile terminal, and record the alarm log to the distributed data storage module.

[0126] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A real-time status monitoring and task configuration system for large cranes, characterized in that: It includes a state awareness layer, a data transmission layer, a data storage and processing layer, a task configuration layer, and a human-computer interaction layer; The status perception layer includes a mechanical structure status perception module, a hydraulic system status perception module, and an electrical system status perception module; the mechanical structure status perception module includes a hook deformation sensing submodule and a boom vibration sensing submodule; the hydraulic system status perception module includes a hydraulic pressure sensing submodule and a hydraulic oil temperature sensing submodule; the electrical system status perception module includes a motor current sensing submodule and a control cabinet voltage sensing submodule. The data transmission layer includes an edge data preprocessing submodule, a wireless transmission submodule, and a wired backup transmission submodule; The data storage and processing layer includes a distributed data storage module, a state feature extraction module, and an anomaly diagnosis and analysis module; the state feature extraction module is used to calculate the effective value of the crane boom vibration acceleration using formula (1); The task configuration layer includes a task requirement parsing module, a resource matching module, and a task scheduling module; The task scheduling module is used to calculate task priority using formula (2); The human-computer interaction layer includes a status display module, an operation command input module, and an alarm prompt module.

2. The real-time status monitoring and task configuration system for large cranes according to claim 1, characterized in that: In the mechanical structure state sensing module, the hook deformation sensing submodule uses a strain gauge sensor, which is attached to the stress concentration area of ​​the hook beam to collect the hook strain signal; the boom vibration sensing submodule uses a triaxial accelerometer, which is installed in the middle section of the boom to collect the boom's X-axis, Y-axis, and Z-axis vibration signals.

3. The real-time status monitoring and task configuration system for large cranes according to claim 1, characterized in that: The edge data preprocessing submodule of the data transmission layer uses wavelet transform algorithm for data denoising, linear normalization algorithm to convert data into 0-1 interval, and differential coding algorithm for data compression; the wireless transmission submodule uses LoRa communication technology to support simultaneous communication of multiple nodes; the wired backup transmission submodule uses industrial Ethernet and implements data transmission based on TCP / IP protocol.

4. The real-time status monitoring and task configuration system for large cranes according to claim 1, characterized in that: The distributed data storage module of the data storage and processing layer adopts a two-level storage architecture of "edge server and cloud"; the edge server stores nearly 72 hours of real-time monitoring data, and the cloud stores historical monitoring data; the storage format is JSON, which is compatible with the data interface of the digital shipyard cloud platform.

5. The real-time status monitoring and task configuration system for large cranes according to claim 1, characterized in that: The formula (1) for the state feature extraction module is: ； in, This represents the effective value of the crane boom vibration acceleration. This represents the number of sampling points for a single vibration signal. For the first The original vibration acceleration values ​​at each sampling point.

6. The real-time status monitoring and task configuration system for large cranes according to claim 1, characterized in that: The abnormality diagnosis and analysis module adopts the diagnostic logic of "threshold comparison and trend judgment". Threshold comparison compares the state feature parameters with the preset normal threshold and marks "potential abnormality". Trend judgment performs linear fitting on the state feature parameters for 5 consecutive minutes. If the absolute value of the fitting slope exceeds the preset range, it is judged as "trend abnormality". The diagnosis results are divided into 4 levels: "normal", "potential abnormality", "trend abnormality" and "severe abnormality".

7. The real-time status monitoring and task configuration system for large cranes according to claim 1, characterized in that: The task requirement parsing module of the task configuration layer is used to parse the task instructions issued by the digital shipyard cloud platform and extract the task type, task load, task urgency, and task-related workstation information; the resource matching module is used to filter cranes that are "load-matched, in good condition, and in similar location"; the formula (2) of the task scheduling module is: ； in, As a task priority, This represents the urgency level of the task. For the real-time load rate of the crane, The importance coefficient of the associated processes in the task. , , The weighting coefficients and .

8. The real-time status monitoring and task configuration system for large cranes according to claim 1, characterized in that: When the anomaly diagnosis and analysis module outputs "trend anomaly" or "serious anomaly", the alarm prompt module of the human-computer interaction layer activates an audible and visual alarm, pushes an alarm message to the mobile terminal, and records the alarm log to the distributed data storage module.

9. The configuration method of the real-time status monitoring and task configuration system for large cranes according to any one of claims 1-8, characterized in that: Includes the following steps: Step 1: The mechanical structure, hydraulic system, and electrical system sensing modules of the state perception layer collect real-time operating data of the crane and output standard electrical signals; Step 2: The edge data preprocessing submodule of the data transmission layer performs noise reduction, normalization, and compression on the standard electrical signal to obtain preprocessed data; The wireless transmission submodule transmits the pre-processed data to the ship's edge gateway, while the wired backup transmission submodule automatically switches transmission when wireless transmission is interrupted. Step 3: The distributed data storage module of the data storage and processing layer stores the preprocessed data; The state feature extraction module calls formula (1) to calculate the state feature parameters; The anomaly diagnosis and analysis module outputs diagnostic results through "threshold comparison and trend judgment"; Step 4: The task requirement parsing module of the task configuration layer parses the shipyard task instructions; the resource matching module selects qualified cranes. The task scheduling module calls formula (2) to calculate task priority and generate a task allocation scheme; Step 5: The status display module of the human-computer interaction layer displays status data and task plans; the operation command input module supports manual intervention; and the alarm prompt module activates an alarm when an anomaly occurs.

10. The configuration method of the real-time status monitoring and task configuration system for large cranes according to claim 9, characterized in that: In step 4, the task scheduling module calculates the task priority according to formula (2). Tasks are assigned from largest to smallest value, the task assignment plan is sent to the crane control system, and feedback is provided to the human-machine interface layer.

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