Crane monitoring system and crane monitoring method

Abstract

The invention relates to the field of hoisting equipment, in particular to a crane monitoring system and a crane monitoring method, and the operation efficiency, the safety and the reliability of the system are remarkably improved through real-time monitoring analysis, trend analysis and fault analysis. Real-time monitoring and analysis can respond to abnormal conditions in time, operation parameters are optimized, and the equipment utilization rate is improved. Trend analysis supports predictive maintenance and resource optimization, and downtime is reduced. Fault reasons are accurately diagnosed through fault analysis, detailed reports and prevention measures are provided, and safety is guaranteed. Data quality and analysis accuracy are improved through data preprocessing, and long-term stable operation of the system is ensured through historical data analysis and trend prediction. And the comprehensive report and data visualization assist in management decision making, and the operation cost is reduced. On the whole, the data analysis module brings comprehensive optimization and remarkable benefit improvement to the crane monitoring system.

Description

Technical Field

[0001] The invention relates to the field of lifting equipment, and in particular to a crane monitoring system and a crane monitoring method. Background Art

[0002] Existing crane monitoring systems mostly use manual monitoring methods, which have problems such as limited monitoring range, poor real-time performance, and large human errors. At the same time, the traditional monitoring system has limited capabilities in data analysis and fault warning, and cannot reflect the operating status of the crane in a timely and accurate manner, resulting in greater safety hazards during the operation of the crane. Therefore, it is particularly necessary to design an automated and intelligent crane monitoring system. Summary of the invention

[0003] The object of the present invention is to provide a crane monitoring system and a crane monitoring method to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a crane monitoring system, comprising a data acquisition unit, a data transmission unit, a data processing unit, a display unit and an alarm unit, wherein the data acquisition unit is used to collect various data during the operation of the crane; the data transmission unit is used to transmit the collected data to a monitoring center; the data processing unit is located in the monitoring center and is used to analyze and process the received data; the display unit is used to display the results of the analysis and processing by the data processing unit; the alarm unit is used to send an alarm signal when an abnormal situation is detected;

[0005] The data acquisition unit includes the following parts: sensor module: composed of multiple sensors installed at different parts of the crane, each of which is responsible for collecting specific types of data; data collector: a device used to connect various sensors, responsible for preliminary processing and integration of data collected by each sensor, and the data collector has multi-channel input capabilities, receives multiple types of data at the same time, and performs necessary signal processing such as filtering and amplification; the storage is used to temporarily store the raw data collected by the sensor, so that the data transmission unit transmits it to the monitoring center; the data transmission module is responsible for transmitting the data processed by the data collector to the monitoring center; the power management module provides a stable power supply for the data acquisition unit to ensure the normal operation of each sensor and data collector;

[0006] The data transmission unit includes the following parts: wired transmission module: transmits the data of the data acquisition unit to the monitoring center through physical cables; wireless transmission module: transmits the data of the data acquisition unit to the monitoring center through wireless communication technology; data protocol module: is responsible for formatting the data to ensure that the data can be correctly identified and interpreted during the transmission process; data encryption module: ensures the security of the data of the data acquisition unit during the transmission process, and the data encryption module uses AES to encrypt the transmitted data; data compression module: is used to compress the data of the data acquisition unit, reduce the amount of data transmission, increase the transmission speed, and reduce the network bandwidth occupation; fault detection and recovery module: is responsible for monitoring the fault situation during the transmission process, and automatically recovers when a fault occurs;

[0007] The data processing unit includes the following parts: data receiving module: responsible for receiving data transmitted from the data transmission unit; data storage module: used to store received data and processed data, and the data storage module adopts SQL database system to efficiently store and retrieve large amounts of data; data preprocessing module: performs preliminary processing on the original data received by the data receiving module, including data cleaning, data conversion, and data completion operations. Data cleaning includes removing noise data and abnormal data, data conversion includes converting data into a standard format, and data completion includes filling in missing data; data analysis module: responsible for in-depth analysis of preprocessed data; machine learning module: establishes a model by training the processed data with a neural network algorithm to perform fault prediction and remaining life prediction; fault diagnosis module: performs detailed fault diagnosis based on the results of the data analysis module and the machine learning module, provides detailed fault cause analysis and processing suggestions, generates a diagnostic report, and helps operators take timely measures; visualization module: displays the results of the fault diagnosis module analysis and processing in a graphical manner; alarm module: determines whether an alarm signal needs to be issued based on the results of data analysis and fault diagnosis. When an abnormal situation or potential fault is detected, the alarm module issues an alarm through sound, light, or text message to remind the operator to handle it in time;

[0008] The alarm unit includes the following parts: anomaly detection module: used to set alarm thresholds and delay times to prevent false alarms caused by accidental instantaneous fluctuations; alarm generation module: collects abnormal data and information, and prepares detailed alarm descriptions, including abnormality type, occurrence time, specific parameter values, and possible causes; alarm output module: used to issue alarms via display interface, warning lights, LED screens, buzzers, and text messages; alarm management and recording: performs statistical analysis on historical alarm data, identifies frequently occurring abnormal situations, and provides a basis for equipment maintenance and operation optimization.

[0009] Preferably, the sensor module includes the following sensors: acceleration sensor: installed on the boom, hook and other parts of the crane, used to monitor the acceleration and vibration of the crane to evaluate the running stability and safety of the crane; temperature sensor: installed on the key parts of the motor and hydraulic system of the crane, used to monitor the temperature changes of the installation parts to prevent overheating; stress sensor: installed on the steel structure and sling of the crane, used to monitor the stress changes of the installation parts, and evaluate its bearing capacity and structural health; displacement sensor: used to monitor the movement state of the crane, including the angle of the boom and the position of the hook, to ensure that the operation of the crane is within a safe range; pressure sensor: installed in the hydraulic system, used to monitor the pressure of the hydraulic oil to ensure the normal operation of the hydraulic system; wind speed sensor: installed on the top of the crane, used to monitor the ambient wind speed, so as to evaluate the impact of the external environment on the operation of the crane.

[0010] Preferably, the data analysis module includes real-time monitoring analysis, trend analysis and fault analysis, specifically:

[0011] a. Real-time monitoring and analysis: monitor and analyze the real-time operation data of the crane, identify abnormal situations and generate real-time monitoring reports;

[0012] Data collection: real-time collection of crane operation data, including temperature, pressure, stress, and displacement;

[0013] Real-time analysis: Analyze the collected real-time data to identify abnormal conditions, such as excessive temperature, excessive pressure, and excessive stress;

[0014] Anomaly detection: Apply thresholds to detect anomalies and determine the severity of the anomaly;

[0015] Real-time reporting: Generate real-time monitoring reports, record real-time data, detected anomalies and their handling suggestions;

[0016] b. Trend analysis: Analyze historical data, identify the changing patterns of operating parameters, and predict what may happen in the future;

[0017] Data accumulation: collect and store the historical operation data of the crane to form a complete historical data set;

[0018] Trend identification: Analyze historical data to identify the changing trends of operating parameters;

[0019] Pattern recognition: Apply pattern recognition algorithms to identify regular changes and periodic fluctuations in data;

[0020] Trend forecasting: forecasting future operations based on identified trends;

[0021] Trend report: Generate trend analysis reports, record historical data, identified trends and their forecast results, and provide reference suggestions;

[0022] c. Fault analysis: Conduct in-depth analysis of fault data, determine the cause of the fault and the scope of impact, and provide a fault diagnosis report;

[0023] Fault data collection: Collect the fault data of the crane, including the time of fault occurrence, fault type, and related parameter values;

[0024] Fault pattern recognition: Apply fault pattern recognition algorithms to analyze fault data and determine the patterns and characteristics of faults;

[0025] Fault cause analysis: Combine historical data and fault modes to analyze possible causes of faults;

[0026] Fault impact assessment: assess the extent of the fault’s impact on the overall operation of the crane;

[0027] Fault diagnosis report: Generate a fault diagnosis report to record the detailed information, cause analysis, impact assessment and handling suggestions of the fault.

[0028] Preferably, the specific working steps of the machine learning module are as follows:

[0029] Step 1: Data collection and preprocessing: Collect various sensor data generated during the operation of the crane, including acceleration, temperature, stress, displacement, pressure, and wind speed; remove noise and outliers in the data to ensure data quality and accuracy; standardize the data of different sensors to meet the requirements of model input; divide the data into training set, validation set, and test set for model training and evaluation;

[0030] Step 2: Model training: Initialize the parameters of the convolutional neural network, train the neural network using the training set data, use the back-propagation algorithm to adjust the model parameters, and minimize the loss function; evaluate the performance of the model on the validation set, and adjust the hyperparameters to obtain the best model; use the test set to evaluate the final model to ensure its generalization ability and prediction accuracy;

[0031] Step 3: Real-time data prediction: The crane sensor data collected in real time is input into the trained convolutional neural network model. The model processes the input data and outputs the fault prediction results. The model predicts the remaining life of each key component of the crane based on the input data to help operators perform preventive maintenance.

[0032] Step 4: Fault prediction and remaining life prediction: The convolutional neural network model identifies potential fault signs in the current operating data and generates a fault prediction report. Based on historical data and current operating status, the model predicts the remaining service life of key components and generates a life prediction report.

[0033] Preferably, a crane monitoring method comprises the following steps:

[0034] Step S1, collecting the operation status data of the crane in real time through the data acquisition unit;

[0035] Step S2: transmitting the collected data to a monitoring center through a data transmission unit;

[0036] Step S3: the data processing unit analyzes and processes the received data and generates an analysis report;

[0037] Step S4, the display unit displays the result of the analysis and processing;

[0038] Step S5: The alarm unit sends out an alarm signal when an abnormal situation is detected.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention can detect abnormal conditions in the operation of the crane at the first time through real-time monitoring and analysis, and generate alarm signals in time to ensure that the operator can take measures quickly to avoid the problem from expanding; by identifying the changing rules of operating parameters, potential problems can be predicted in advance, predictive maintenance can be supported, the time and frequency of downtime due to failures can be reduced, and the utilization rate of equipment can be improved; the present invention can accurately determine the cause and impact range of the failure through fault analysis, provide detailed fault diagnosis reports, help operators to troubleshoot the failure in time, prevent the failure from expanding, and ensure the safety of equipment and personnel; fault pattern recognition and cause analysis can identify the precursors of common faults, take preventive measures in advance, reduce the probability of failure, and improve the safety of the system; when the system detects an abnormal situation, the alarm unit can immediately send out an alarm signal to remind the operator to prevent the occurrence of safety accidents; according to the severity of the abnormal situation, different levels of alarm information are provided to ensure Ensure that operators can quickly handle emergency situations according to priority; the present invention can continuously monitor the long-term operating status of equipment through the accumulation and analysis of historical data, promptly discover potential problems, and maintain long-term reliable operation of equipment. Trend prediction based on historical data can foresee the operating status of equipment in advance, take measures in time, avoid sudden failures, and ensure the stability of the system; the present invention provides detailed real-time monitoring reports to help managers understand the current operating status and abnormal conditions of equipment and support rapid decision-making. At the same time, it generates trend analysis reports, provides historical data and future predictions, provides decision-making basis for management, optimizes equipment management and maintenance strategies, and provides detailed fault diagnosis reports, records fault information and handling suggestions, supports fault management and maintenance decisions, and improves the efficiency and effectiveness of fault handling; the present invention uses a friendly visual interface to display the analysis results in intuitive forms such as charts and images, helping operators and management to quickly understand and use the analysis results and make correct decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the system structure of the present invention;

[0041] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] See also Figure 1-2The present invention provides a technical solution: a crane monitoring system, including a data acquisition unit, a data transmission unit, a data processing unit, a display unit and an alarm unit, wherein the data acquisition unit is used to collect various data during the operation of the crane; the data transmission unit is used to transmit the collected data to a monitoring center; the data processing unit is located in the monitoring center and is used to analyze and process the received data; the display unit is used to display the results of the analysis and processing by the data processing unit; the alarm unit is used to send an alarm signal when an abnormal situation is detected;

[0044] The data acquisition unit includes the following parts: sensor module: composed of a variety of sensors installed in different parts of the crane, each of which is responsible for collecting specific types of data; data collector: a device used to connect various sensors, responsible for preliminary processing and integration of the data collected by each sensor, and the data collector has multi-channel input capabilities, receiving multiple types of data at the same time, and performing necessary signal processing such as filtering and amplification; the storage is used to temporarily store the raw data collected by the sensor so that the data transmission unit can transmit it to the monitoring center; the data transmission module is responsible for transmitting the data processed by the data collector to the monitoring center; the power management module provides a stable power supply for the data acquisition unit to ensure the normal operation of each sensor and data collector;

[0045] The data transmission unit includes the following parts: wired transmission module: transmits the data of the data acquisition unit to the monitoring center through physical cables; wireless transmission module: transmits the data of the data acquisition unit to the monitoring center through wireless communication technology; data protocol module: is responsible for formatting the data to ensure that the data can be correctly identified and interpreted during the transmission process; data encryption module: ensures the security of the data acquisition unit data during the transmission process, and the data encryption module uses AES to encrypt the transmitted data; data compression module: is used to compress the data of the data acquisition unit, reduce the data transmission volume, increase the transmission speed, and reduce the network bandwidth occupation; fault detection and recovery module: is responsible for monitoring the fault conditions during the transmission process, and automatically recovers when a fault occurs;

[0046] The data processing unit includes the following parts: data receiving module: responsible for receiving data transmitted from the data transmission unit; data storage module: used to store received data and processed data, and the data storage module adopts SQL database system to efficiently store and retrieve large amounts of data; data preprocessing module: preliminary processing of the original data received by the data receiving module, including data cleaning, data conversion, and data completion operations. Data cleaning includes removing noise data and abnormal data, data conversion includes converting data into a standard format, and data completion includes filling in missing data; data analysis module: responsible for in-depth analysis of preprocessed data; machine learning module: establish a model through training the processed data with neural network algorithm to perform fault prediction and remaining life prediction; fault diagnosis module: perform detailed fault diagnosis according to the results of the data analysis module and machine learning module, provide detailed fault cause analysis and processing suggestions, generate a diagnostic report, and help operators take timely measures; visualization module: display the results of the analysis and processing of the fault diagnosis module in a graphical way; alarm module: determine whether an alarm signal needs to be issued according to the results of data analysis and fault diagnosis. When an abnormal situation or potential fault is detected, the alarm module issues an alarm through sound, light, and text message to remind the operator to deal with it in time;

[0047] The alarm unit consists of the following parts: Anomaly detection module: used to set alarm thresholds and delay times to prevent false alarms caused by accidental instantaneous fluctuations; Alarm generation module: collects abnormal data and information, and prepares detailed alarm descriptions, including abnormality type, occurrence time, specific parameter values, and possible causes; Alarm output module: used to issue alarms through display interface, warning lights, LED screens, buzzers, and text messages; Alarm management and recording: performs statistical analysis on historical alarm data, identifies frequently occurring abnormal situations, and provides a basis for equipment maintenance and operation optimization.

[0048] Furthermore, the sensor module includes the following sensors: acceleration sensor: installed on the boom, hook and other parts of the crane, used to monitor the acceleration and vibration of the crane, so as to evaluate the running stability and safety of the crane; temperature sensor: installed on the key parts of the motor and hydraulic system of the crane, used to monitor the temperature changes of the installation parts to prevent overheating; stress sensor: installed on the steel structure and sling of the crane, used to monitor the stress changes of the installation parts, and evaluate its bearing capacity and structural health; displacement sensor: used to monitor the movement state of the crane, including the angle of the boom and the position of the hook, so as to ensure that the operation of the crane is within a safe range; pressure sensor: installed in the hydraulic system, used to monitor the pressure of the hydraulic oil, and ensure the normal operation of the hydraulic system; wind speed sensor: installed on the top of the crane, used to monitor the ambient wind speed, so as to evaluate the impact of the external environment on the operation of the crane.

[0049] Furthermore, the data analysis module includes real-time monitoring analysis, trend analysis and fault analysis, specifically:

[0050] a. Real-time monitoring and analysis: monitor and analyze the real-time operation data of the crane, identify abnormal situations and generate real-time monitoring reports;

[0051] Data collection: real-time collection of crane operation data, including temperature, pressure, stress, and displacement;

[0052] Real-time analysis: Analyze the collected real-time data to identify abnormal conditions, such as excessive temperature, excessive pressure, and excessive stress;

[0053] Anomaly detection: Apply thresholds to detect anomalies and determine the severity of the anomaly;

[0054] Real-time reporting: Generate real-time monitoring reports, record real-time data, detected anomalies and their handling suggestions;

[0055] b. Trend analysis: Analyze historical data, identify the changing patterns of operating parameters, and predict what may happen in the future;

[0056] Data accumulation: collect and store the historical operation data of the crane to form a complete historical data set;

[0057] Trend identification: Analyze historical data to identify the changing trends of operating parameters;

[0058] Pattern recognition: Apply pattern recognition algorithms to identify regular changes and periodic fluctuations in data;

[0059] Trend forecasting: forecasting future operations based on identified trends;

[0060] Trend report: Generate trend analysis reports, record historical data, identified trends and their forecast results, and provide reference suggestions;

[0061] c. Fault analysis: Conduct in-depth analysis of fault data, determine the cause of the fault and the scope of impact, and provide a fault diagnosis report;

[0062] Fault data collection: Collect the fault data of the crane, including the time of fault occurrence, fault type, and related parameter values;

[0063] Fault pattern recognition: Apply fault pattern recognition algorithms to analyze fault data and determine the patterns and characteristics of faults;

[0064] Fault cause analysis: Combine historical data and fault modes to analyze possible causes of faults;

[0065] Fault impact assessment: assess the extent of the fault’s impact on the overall operation of the crane;

[0066] Fault diagnosis report: Generate a fault diagnosis report to record the detailed information, cause analysis, impact assessment and handling suggestions of the fault.

[0067] Furthermore, the specific working steps of the machine learning module are as follows:

[0068] Step 1: Data collection and preprocessing: Collect various sensor data generated during the operation of the crane, including acceleration, temperature, stress, displacement, pressure, and wind speed; remove noise and outliers in the data to ensure data quality and accuracy; standardize the data of different sensors to meet the requirements of model input; divide the data into training set, validation set, and test set for model training and evaluation;

[0069] Step 2: Model training: Initialize the parameters of the convolutional neural network, train the neural network using the training set data, use the back-propagation algorithm to adjust the model parameters, and minimize the loss function; evaluate the performance of the model on the validation set, and adjust the hyperparameters to obtain the best model; use the test set to evaluate the final model to ensure its generalization ability and prediction accuracy;

[0070] Step 3: Real-time data prediction: The crane sensor data collected in real time is input into the trained convolutional neural network model. The model processes the input data and outputs the fault prediction results. The model predicts the remaining life of each key component of the crane based on the input data to help operators perform preventive maintenance.

[0071] Step 4: Fault prediction and remaining life prediction: The convolutional neural network model identifies potential fault signs in the current operating data and generates a fault prediction report. Based on historical data and current operating status, the model predicts the remaining service life of key components and generates a life prediction report.

[0072] Furthermore, a crane monitoring method comprises the following steps:

[0073] Step S1, collecting the operation status data of the crane in real time through the data acquisition unit;

[0074] Step S2: transmitting the collected data to a monitoring center through a data transmission unit;

[0075] Step S3: the data processing unit analyzes and processes the received data and generates an analysis report;

[0076] Step S4, the display unit displays the result of the analysis and processing;

[0077] Step S5: The alarm unit sends out an alarm signal when an abnormal situation is detected.

[0078] Through real-time monitoring and analysis, the present invention can detect abnormal conditions in crane operation at the first time, generate alarm signals in time, and ensure that operators can take prompt measures to avoid the expansion of problems; by identifying the changing patterns of operating parameters, potential problems can be predicted in advance, predictive maintenance can be supported, the time and frequency of downtime due to failures can be reduced, and the utilization rate of equipment can be improved; through fault analysis, the present invention can accurately determine the cause and impact range of the fault, provide a detailed fault diagnosis report, help operators to troubleshoot the fault in time, prevent the fault from expanding, and ensure the safety of equipment and personnel; fault pattern recognition and cause analysis can identify the precursors of common faults, take preventive measures in advance, reduce the probability of failure, and improve the safety of the system; when the system detects an abnormal situation, the alarm unit can immediately send out an alarm signal to remind the operator to prevent the occurrence of safety accidents; according to the severity of the abnormal situation, different levels of alarm information are provided to ensure that the operator can Prioritize and quickly handle emergencies; the present invention can continuously monitor the long-term operating status of equipment through the accumulation and analysis of historical data, discover potential problems in a timely manner, and maintain long-term reliable operation of equipment. Trend prediction based on historical data can foresee the operating status of equipment in advance, take measures in a timely manner, avoid sudden failures, and ensure the stability of the system; the present invention provides detailed real-time monitoring reports to help managers understand the current operating status and abnormal conditions of equipment and support rapid decision-making. At the same time, it generates trend analysis reports, provides historical data and future predictions, provides decision-making basis for management, optimizes equipment management and maintenance strategies, and provides detailed fault diagnosis reports, records fault information and handling suggestions, supports fault management and maintenance decisions, and improves the efficiency and effectiveness of fault handling; the present invention uses a friendly visual interface to display the analysis results in intuitive forms such as charts and images, helping operators and management to quickly understand and use the analysis results and make correct decisions.

[0079] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A crane monitoring system, comprising a data acquisition unit, a data transmission unit, a data processing unit, a display unit and an alarm unit, characterized in that: The data acquisition unit is used to collect various data during the operation of the crane; the data transmission unit is used to transmit the collected data to the monitoring center; the data processing unit is located in the monitoring center and is used to analyze and process the received data; the display unit is used to display the results of the analysis and processing by the data processing unit; the alarm unit is used to send an alarm signal when an abnormal situation is detected; The data acquisition unit includes the following parts: sensor module: composed of multiple sensors installed at different parts of the crane, each of which is responsible for collecting specific types of data; data collector: a device used to connect various sensors, responsible for preliminary processing and integration of the data collected by each sensor, and the data collector has multi-channel input capabilities, receives multiple types of data at the same time, and performs necessary signal processing such as filtering and amplification; The storage device is used to temporarily store the raw data collected by the sensor so that the data transmission unit can transmit it to the monitoring center; The data transmission module is responsible for transmitting the data processed by the data collector to the monitoring center; the power management module provides a stable power supply for the data acquisition unit to ensure the normal operation of each sensor and data collector; The data transmission unit includes the following parts: a wired transmission module: transmitting the data of the data acquisition unit to the monitoring center through a physical cable; a wireless transmission module: transmitting the data of the data acquisition unit to the monitoring center through wireless communication technology; a data protocol module: responsible for formatting the data to ensure that the data can be correctly identified and interpreted during the transmission process; a data encryption module: ensuring the security of the data of the data acquisition unit during the transmission process, the data encryption module uses AES to encrypt the transmitted data; a data compression module: used to compress the data of the data acquisition unit, reduce the amount of data transmission, increase the transmission speed, and reduce the network bandwidth occupation; Fault detection and recovery module: responsible for monitoring faults during transmission and automatically recovering when a fault occurs; The data processing unit comprises the following parts: a data receiving module: responsible for receiving data transmitted from the data transmission unit; a data storage module: used to store received data and processed data, and the data storage module adopts an SQL database system to efficiently store and retrieve a large amount of data; Data preprocessing module: performs preliminary processing on the raw data received by the data receiving module, including data cleaning, data conversion, and data completion operations. Data cleaning includes removing noise data and abnormal data, data conversion includes converting data into a standard format, and data completion includes filling in missing data; Data analysis module: is responsible for in-depth analysis of preprocessed data; Machine learning module: establishes a model through training of processed data using a neural network algorithm to perform fault prediction and remaining life prediction; Fault diagnosis module: Based on the results of the data analysis module and the machine learning module, it performs detailed fault diagnosis, provides detailed fault cause analysis and treatment suggestions, generates a diagnosis report, and helps operators take timely measures; Visualization module: Displays the results of the fault diagnosis module analysis and treatment in a graphical manner; Alarm module: Based on the results of data analysis and fault diagnosis, it determines whether an alarm signal needs to be issued. When an abnormal situation or potential fault is detected, the alarm module issues an alarm through sound, light, or text message to remind operators to handle it in time; The alarm unit includes the following parts: anomaly detection module: used to set alarm thresholds and delay times to prevent false alarms caused by accidental instantaneous fluctuations; alarm generation module: collects abnormal data and information, and prepares detailed alarm descriptions, including abnormality type, occurrence time, specific parameter values, and possible causes; alarm output module: used to issue alarms via display interface, warning lights, LED screens, buzzers, and text messages; alarm management and recording: performs statistical analysis on historical alarm data, identifies frequently occurring abnormal situations, and provides a basis for equipment maintenance and operation optimization.

2. A crane monitoring system according to claim 1, characterized in that: The sensor module includes the following sensors: acceleration sensor: installed in the crane's boom, hook and other parts, used to monitor the acceleration and vibration of the crane to evaluate the crane's running stability and safety; temperature sensor: installed in the crane's motor and hydraulic system key parts, used to monitor the temperature changes of the installation parts to prevent overheating; stress ... Sensors: installed on the steel structure and slings of the crane, used to monitor stress changes in the installation location and evaluate its load-bearing capacity and structural health; Displacement sensors: used to monitor the movement status of the crane, including the angle of the boom and the position of the hook, to ensure that the operation of the crane is within a safe range; Pressure sensors: installed in the hydraulic system, used to monitor the pressure of the hydraulic oil to ensure the normal operation of the hydraulic system; Wind speed sensors: installed on the top of the crane, used to monitor the ambient wind speed to evaluate the impact of the external environment on the operation of the crane.

3. A crane monitoring system according to claim 1, characterized in that: The data analysis module includes real-time monitoring analysis, trend analysis and fault analysis, specifically: a. Real-time monitoring and analysis: monitor and analyze the real-time operation data of the crane, identify abnormal situations and generate real-time monitoring reports; Data collection: real-time collection of crane operation data, including temperature, pressure, stress, and displacement; Real-time analysis: Analyze the collected real-time data to identify abnormal conditions, such as excessive temperature, excessive pressure, and excessive stress; Anomaly detection: Apply thresholds to detect anomalies and determine the severity of the anomaly; Real-time reporting: Generate real-time monitoring reports, record real-time data, detected anomalies and their handling suggestions; b. Trend analysis: Analyze historical data, identify the changing patterns of operating parameters, and predict what may happen in the future; Data accumulation: collect and store the historical operation data of the crane to form a complete historical data set; Trend identification: Analyze historical data to identify the changing trends of operating parameters; Pattern recognition: Apply pattern recognition algorithms to identify regular changes and periodic fluctuations in data; Trend forecasting: forecasting future operations based on identified trends; Trend report: Generate trend analysis reports, record historical data, identified trends and their forecast results, and provide reference suggestions; c. Fault analysis: Conduct in-depth analysis of fault data, determine the cause of the fault and the scope of impact, and provide a fault diagnosis report; Fault data collection: Collect the fault data of the crane, including the time of fault occurrence, fault type, and related parameter values; Fault pattern recognition: Apply fault pattern recognition algorithms to analyze fault data and determine the patterns and characteristics of faults; Fault cause analysis: Combine historical data and fault modes to analyze possible causes of faults; Fault impact assessment: assess the extent of the fault’s impact on the overall operation of the crane; Fault diagnosis report: Generate a fault diagnosis report to record the detailed information, cause analysis, impact assessment and handling suggestions of the fault.

4. A crane monitoring system according to claim 1, characterized in that: The specific working steps of the machine learning module are as follows: Step 1: Data collection and preprocessing: Collect various sensor data generated during the operation of the crane, including acceleration, temperature, stress, displacement, pressure, and wind speed; remove noise and outliers in the data to ensure data quality and accuracy; standardize the data of different sensors to meet the requirements of model input; divide the data into training set, validation set, and test set for model training and evaluation; Step 2: Model training: Initialize the parameters of the convolutional neural network, train the neural network using the training set data, use the back-propagation algorithm to adjust the model parameters, and minimize the loss function; evaluate the performance of the model on the validation set, and adjust the hyperparameters to obtain the best model; use the test set to evaluate the final model to ensure its generalization ability and prediction accuracy; Step 3: Real-time data prediction: The crane sensor data collected in real time is input into the trained convolutional neural network model. The model processes the input data and outputs the fault prediction results. The model predicts the remaining life of each key component of the crane based on the input data to help operators perform preventive maintenance. Step 4: Fault prediction and remaining life prediction: The convolutional neural network model identifies potential fault signs in the current operating data and generates a fault prediction report. Based on historical data and current operating status, the model predicts the remaining service life of key components and generates a life prediction report.

5. A crane monitoring method according to any one of claims 1 to 4, characterized in that: The following steps are involved: Step S1, collecting the operation status data of the crane in real time through the data acquisition unit; Step S2: transmitting the collected data to a monitoring center through a data transmission unit; Step S3: the data processing unit analyzes and processes the received data and generates an analysis report; Step S4, the display unit displays the result of the analysis and processing; Step S5: The alarm unit sends out an alarm signal when an abnormal situation is detected.

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