Hoisting machinery remote monitoring and diagnosis system and method based on Internet of Things

Abstract

The invention provides a hoisting machinery remote monitoring and diagnosis system and method based on the Internet of Things, and the system comprises an Internet of Things module which is provided with a plurality of Internet of Things sensor nodes and is used for collecting the state data of hoisting machinery in real time, and uploading the state data to a data collection terminal; the data acquisition terminal is used for summarizing and preprocessing the state data and transmitting the processed state data to the cloud platform; the cloud platform is used for receiving the processed state data and storing and managing the processed state data; wherein the management comprises the step of carrying out self-adaptive abnormity monitoring and diagnosis on the hoisting machinery based on the processed state data and the maintenance record from the user terminal; the user terminal is used for executing corresponding maintenance measures based on the result report provided by the cloud platform; wherein the result report comprises a diagnosis result, a fault type and a maintenance suggestion.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment fault monitoring, and particularly to a remote monitoring and diagnosis system and method for lifting machinery based on the Internet of Things. Background Art

[0002] With the continuous development of Internet of Things technology, the application of remote monitoring and diagnosis systems based on sensors has gradually increased in fields such as industry, construction, and transportation. However, the existing detection and diagnosis systems usually have the following problems:

[0003] 1. Slow response: Traditional detection systems rely on manual inspections or periodic data collection and cannot achieve real-time abnormal response.

[0004] 2. Insufficient data processing capacity: Systems deployed locally are difficult to process massive amounts of sensor data, resulting in low diagnostic efficiency.

[0005] 3. Low diagnostic accuracy: The alarm mechanism based on fixed thresholds is prone to false alarms or missed alarms and lacks the ability to identify complex fault modes. Summary of the Invention

[0006] The purpose of the present invention is to provide a remote monitoring and diagnosis system and method for lifting machinery based on the Internet of Things, aiming to solve the above problems in the prior art.

[0007] An embodiment of the present invention provides a remote monitoring and diagnosis system for lifting machinery based on the Internet of Things, including:

[0008] An Internet of Things module, provided with a plurality of Internet of Things sensor nodes, connected to a data acquisition terminal, for real-time collecting the status data of the lifting machinery and uploading the status data to the data acquisition terminal;

[0009] A data acquisition terminal, connected to the Internet of Things module and a cloud platform, for summarizing and preprocessing the status data and transmitting the processed status data to the cloud platform;

[0010] A cloud platform, connected to the data acquisition terminal and a user terminal, for receiving the processed status data and storing and managing the processed status data; wherein, the management includes performing adaptive abnormal monitoring and diagnosis on the lifting machinery based on the processed status data and maintenance records from the user terminal;

[0011] A user terminal, connected to the cloud platform, for performing corresponding maintenance measures based on the result report provided by the cloud platform; wherein, the result report includes a diagnosis result, a fault type, and a maintenance suggestion.

[0012] An embodiment of the present invention provides a method for remote monitoring and diagnosis of a lifting machine based on the Internet of Things, including:

[0013] Collect the status data of the lifting machine in real time through the Internet of Things module, and upload the status data to the data acquisition terminal;

[0014] Summarize and preprocess the status data through the data acquisition terminal, and transmit the processed status data to the cloud platform;

[0015] Receive the processed status data through the cloud platform, and store and manage the processed status data; wherein, the management includes adaptive anomaly monitoring and diagnosis of the lifting machine based on the processed status data and maintenance records from the user terminal;

[0016] Execute corresponding maintenance measures through the user terminal based on the result report provided by the cloud platform; wherein, the result report includes a diagnosis result, a fault type, and a maintenance suggestion.

[0017] An embodiment of the present invention also provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the computer program is executed by the processor, the steps of the above-mentioned method for remote monitoring and diagnosis of a lifting machine based on the Internet of Things are implemented.

[0018] An embodiment of the present invention also provides a computer-readable storage medium, on which an implementation program for information transmission is stored, and when the program is executed by a processor, the steps of the above-mentioned method for remote monitoring and diagnosis of a lifting machine based on the Internet of Things are implemented.

[0019] Adopting the embodiment of the present invention may include the following beneficial effects: The embodiment of the present invention proposes a system and method for intelligent remote detection and diagnosis of a lifting machine based on the Internet of Things. This system realizes real-time monitoring, fault prediction, and intelligent diagnosis of the operating status of the lifting machine through multi-sensor data fusion, cloud computing, and machine learning algorithms, and is applicable to the health management of industrial equipment, building structures, and transportation facilities. Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a schematic diagram of a remote monitoring and diagnosis system for a lifting machine based on the Internet of Things according to an embodiment of the present invention;

[0022] Figure 2 is the overall architecture diagram of the system according to an embodiment of the present invention;

[0023] Figure 3 is the flowchart of the intelligent analysis module according to an embodiment of the present invention;

[0024] Figure 4 is the timing diagram of fault warning according to an embodiment of the present invention;

[0025] Figure 5 is the flowchart of the method for remote monitoring and diagnosis of a crane based on the Internet of Things according to an embodiment of the present invention. Detailed implementation manners

[0026] In order to enable those skilled in the art of the present technology to better understand the technical solutions in one or more embodiments of this specification, the following will clearly and completely describe the technical solutions in one or more embodiments of this specification in conjunction with the accompanying drawings in one or more embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this document.

[0027] System embodiment

[0028] According to an embodiment of the present invention, a remote monitoring and diagnosis system for a crane based on the Internet of Things is provided. Figure 1 is the schematic diagram of the remote monitoring and diagnosis system for a crane based on the Internet of Things according to an embodiment of the present invention, as Figure 1 shown, the remote monitoring and diagnosis system for a crane based on the Internet of Things according to an embodiment of the present invention specifically includes:

[0029] The Internet of Things module 10 is provided with a plurality of Internet of Things sensor nodes, connected to the data acquisition terminal, and is used for real-time collecting the status data of the crane and uploading the status data to the data acquisition terminal;

[0030] The data acquisition terminal 12 is connected to the Internet of Things module and the cloud platform, and is used for summarizing and preprocessing the status data and transmitting the preprocessed status data to the cloud platform;

[0031] The cloud platform 14 is connected to the data acquisition terminal and the user terminal, and is used for receiving the preprocessed status data and storing and managing the preprocessed status data; wherein, the management includes performing adaptive anomaly monitoring and diagnosis on the crane based on the preprocessed status data and the maintenance records from the user terminal, specifically including:

[0032] The intelligent analysis module is provided with a real-time analysis unit, a fault knowledge base, a warning unit, and an adaptive configuration unit that communicate with each other. It is connected to the visualization module and is used to perform abnormal monitoring and diagnosis on the crane based on the processed status data, generate corresponding diagnosis results, obtain the fault type and maintenance suggestions according to the diagnosis results, send the diagnosis results, fault type, and maintenance suggestions to the user terminal, and issue a warning prompt to the user terminal according to the diagnosis results; and perform real-time adaptive configuration on the monitoring parameters and diagnosis model according to the equipment type of the crane and the maintenance records from the user terminal. Specifically, it is used for:

[0033] The real-time analysis unit uses the LSTM neural network model to train and generate a benchmark model for the normal operation state of the crane based on historical status data; residual analysis is used to compare the processed status data with the benchmark model. If there is abnormal data deviating from the baseline, the processed status data is input into a pre-constructed diagnosis model for abnormal diagnosis to generate corresponding diagnosis results;

[0034] If there is no abnormal data deviating from the baseline, the processed status data is input into a pre-constructed prediction model for real-time monitoring to predict the potential fault trend and remaining service life of the crane;

[0035] The fault knowledge base matches the diagnosis results with historical cases to obtain the fault type and maintenance suggestions of the crane; among them, the maintenance suggestions include fault location information, fault cause analysis, and maintenance steps;

[0036] The warning unit is used to issue a warning prompt to the user terminal according to the diagnosis results;

[0037] The adaptive configuration unit performs real-time adaptive configuration on the thresholds of the monitoring parameters and the diagnosis rules of the diagnosis model according to the equipment type of the crane and the maintenance records from the user terminal;

[0038] Among them, each equipment type of the crane is configured with a unique equipment type identification code;

[0039] The visualization module is connected to the intelligent analysis module and is used to perform real-time visualization display of the status data, diagnosis results, fault type, and maintenance suggestions of the crane.

[0040] The user terminal 16 is connected to the cloud platform and is used to perform corresponding maintenance measures based on the result report provided by the cloud platform; among them, the result report includes diagnosis results, fault type, and maintenance suggestions.

[0041] The following combines the specific situation of the Internet of Things-based remote monitoring and diagnosis system for cranes in the embodiments of the present invention, such asFigure 2 As shown in the figure, the above technical solutions of the embodiments of the present invention will be described in detail.

[0042] To solve the problems of poor real-time performance, low diagnostic accuracy and insufficient generality of the existing system, the embodiments of the present invention provide an adaptive and high-precision intelligent remote detection and diagnosis solution, that is, a lifting machinery intelligent remote detection and diagnosis system based on the Internet of Things, which is characterized in that it includes:

[0043] A plurality of Internet of Things sensor nodes for real-time collection of the state data of the lifting machinery, the state data including temperature, pressure, vibration, current, etc.;

[0044] A data acquisition terminal, communicatively connected to the Internet of Things sensor nodes, for aggregating and preprocessing the sensor data; wherein, the Internet of Things sensor nodes are connected to the data acquisition terminal through wireless communication technology.

[0045] A cloud platform for receiving, storing and managing the data from the data acquisition terminal;

[0046] An intelligent analysis module, deployed on the cloud platform, and the specific analysis process is as Figure 3 shown, including:

[0047] A machine learning model, trained based on historical data to generate a benchmark model under the normal operation state of the device; the machine learning model adopts a time series data analysis algorithm for predicting the potential fault trend of the lifting machinery, as Figure 4 shown.

[0048] A real-time analysis unit for comparing the current sensor data with the benchmark model, detecting anomalies and generating diagnostic results;

[0049] An early warning unit for sending an alarm to the user terminal when an anomaly is detected or a fault is predicted;

[0050] An adaptive configuration unit for adjusting the monitoring parameters and diagnostic models according to the type of the lifting machinery; wherein, the adaptive configuration unit automatically loads the corresponding monitoring parameter thresholds and diagnostic rules through the device type identification code.

[0051] Furthermore, the cloud platform also provides a visualization interface for real-time display of the device status, anomaly alarm and diagnostic report.

[0052] The intelligent analysis module further includes a fault knowledge base for matching the fault type according to the diagnostic result and providing repair suggestions, wherein the repair suggestions include fault location information, fault cause analysis and repair steps; the generation of the repair suggestions includes: matching the historical cases in the fault knowledge base and outputting a solution in combination with the current data change trend.

[0053] That is, the system proposed in the embodiment of the present invention collects the status data of the hoisting machinery in real time through multiple sensor nodes; transmits the status data to the cloud platform through the data acquisition terminal; uses the machine learning model of the intelligent analysis module to analyze the data, detect anomalies and generate diagnostic results; when an anomaly is detected, trigger an early warning and push maintenance suggestions to the user terminal; and can dynamically adjust the monitoring parameters and diagnostic models according to the equipment type.

[0054] Specifically, an intelligent remote detection and diagnosis system based on the Internet of Things proposed in the embodiment of the present invention includes the following core modules:

[0055] 1. Multi-source sensor network: Deploy sensors such as temperature, pressure, and vibration to achieve full-dimensional data collection.

[0056] 2. Cloud platform architecture: Adopt a distributed storage and computing framework to support high-concurrency data processing.

[0057] 3. Intelligent analysis module:

[0058] A. Establish a baseline model for the equipment health status through the LSTM neural network;

[0059] B. Real-time detect abnormal data deviating from the baseline based on residual analysis;

[0060] C. Combine the fault knowledge base (including the historical case base and expert rules) to output diagnostic suggestions.

[0061] 4. Adaptive configuration engine: Automatically load the pre-trained model parameters and threshold rules according to the equipment model.

[0062] The intelligent remote detection and diagnosis system for hoisting machinery proposed in the embodiment of the present invention consists of multiple Internet of Things sensor nodes, a data acquisition terminal, a cloud platform, and an intelligent analysis module. Each sensor node can collect the status data of the equipment or structure in real time (such as temperature, pressure, vibration, current, etc.), and transmit the data to the cloud platform through wireless communication technology. The cloud platform is responsible for storing, managing, and preliminarily analyzing a large amount of sensor data.

[0063] Compared with the prior art, the advantages of the embodiment of the present invention are as follows:

[0064] 1. Improved real-time performance: Wireless transmission + edge computing (pre-filtering by the data acquisition terminal) shortens the response time to the millisecond level;

[0065] 2. Improved diagnostic accuracy: Multi-modal data fusion and deep learning models increase the fault recognition accuracy to over 95%;

[0066] 3. Enhanced versatility: One-key match the configuration template through the equipment type identification code (such as QR code) to adapt to different scenarios.

[0067] Example 1 of the implementation according to the embodiments of the present invention is as follows: Fault prediction and health management of lifting machinery

[0068] 1. Application scenario: Real-time monitoring and fault warning of key components (such as wire ropes, motors, gearboxes) of a port container gantry crane.

[0069] 2. System configuration

[0070] A. Sensor deployment:

[0071] Vibration sensors (model: ICP 618B) are installed on the bearing seats of the gearbox, with a sampling frequency of 10 kHz;

[0072] Infrared temperature sensors (model: FLIR A315) monitor the temperature of the motor windings, with an accuracy of ±1°C;

[0073] Current transformers (model: LEM CKSR series) collect the three-phase current of the motor, with a resolution of 0.1 A.

[0074] B. Edge computing node:

[0075] The Raspberry Pi CM4 module is adopted to run a lightweight FFT algorithm to preprocess vibration data and reduce the occupancy of cloud transmission bandwidth.

[0076] C. Cloud analysis model:

[0077] ① Fault diagnosis model: 1D-CNN network. The input layer receives the vibration spectrogram (size 256×256), and the output layer classifies 6 common faults (such as bearing spalling, gear tooth breakage);

[0078] ② Remaining useful life prediction model: Combining temperature and current data, using LSTM to predict the remaining useful life (RUL), with an error <5%.

[0079] D. Workflow

[0080] ① Data acquisition:

[0081] The vibration sensor detects abnormal high-frequency components (>8 kHz) in the gearbox, and the edge node triggers real-time data upload (the original period is 1 minute / time).

[0082] ② Fault diagnosis:

[0083] The cloud model identifies the 3x frequency component in the vibration spectrum and determines it as local wear of the gear (confidence level 92%);

[0084] Synchronously check that the temperature of the motor has risen suddenly by 15°C (exceeding the threshold 301 setting value), and comprehensively diagnose it as gear overheating caused by insufficient lubrication.

[0085] ③ Maintenance response:

[0086] The system automatically pushes an alarm to the maintenance personnel's handheld terminal, suggesting "immediately replenish the grease and check the gear meshing clearance" (matched according to historical maintenance records);

[0087] After maintenance, the system records the repair effect and adaptively adjusts the vibration threshold of the device to 110% of the original value.

[0088] Method embodiments

[0089] According to an embodiment of the present invention, there is provided a method for remote monitoring and diagnosis of a lifting machine based on the Internet of Things. Figure 5 is a flowchart of the method for remote monitoring and diagnosis of a lifting machine based on the Internet of Things according to an embodiment of the present invention, as Figure 5 shown, the method for remote monitoring and diagnosis of a lifting machine based on the Internet of Things according to an embodiment of the present invention specifically includes:

[0090] Step S501, collect the status data of the lifting machine in real time through the Internet of Things module, and upload the status data to the data acquisition terminal;

[0091] Step S502, summarize and preprocess the status data through the data acquisition terminal, and transmit the processed status data to the cloud platform;

[0092] Step S503, receive the processed status data through the cloud platform, and store and manage the processed status data; wherein, the management includes adaptively monitoring and diagnosing the lifting machine based on the processed status data and the maintenance records from the user terminal; specifically including:

[0093] Monitor and diagnose the lifting machine for abnormalities based on the processed status data through the intelligent analysis module in the cloud platform, generate corresponding diagnostic results, obtain the fault type and maintenance suggestions according to the diagnostic results, send the diagnostic results, fault type and maintenance suggestions to the user terminal, and issue a warning prompt to the user terminal according to the diagnostic results; and adaptively configure the monitoring parameters and diagnostic model in real time according to the equipment type of the lifting machine and the maintenance records from the user terminal; specifically including:

[0094] Use the real-time analysis unit in the intelligent analysis module to train and generate a benchmark model for the normal operation state of the lifting machine based on the historical status data by using the LSTM neural network model; compare the processed status data with the benchmark model by using residual analysis, and if there is abnormal data deviating from the baseline, input the processed status data into the pre-constructed diagnostic model for abnormal diagnosis to generate corresponding diagnostic results;

[0095] If there is no abnormal data deviating from the baseline, the processed status data is input into a pre-constructed prediction model for real-time monitoring to predict the potential fault trend and remaining service life of the lifting machinery;

[0096] The diagnostic result is matched with historical cases through the fault knowledge base in the intelligent analysis module to obtain the fault type and maintenance suggestions of the lifting machinery; wherein, the maintenance suggestions include fault location information, fault cause analysis, and maintenance steps;

[0097] The warning unit in the intelligent analysis module issues a warning prompt to the user terminal according to the diagnostic result;

[0098] The adaptive configuration unit in the intelligent analysis module adaptively configures the thresholds of monitoring parameters and the diagnostic rules of the diagnostic model in real time according to the equipment type of the lifting machinery and the maintenance records from the user terminal;

[0099] Among them, each equipment type of the lifting machinery is configured with a unique equipment type identification code;

[0100] The status data, diagnostic results, fault types, and maintenance suggestions of the lifting machinery are visually displayed in real time through the visualization module in the cloud platform.

[0101] Step S504, the user terminal performs corresponding maintenance measures based on the result report provided by the cloud platform; wherein, the result report includes diagnostic results, fault types, and maintenance suggestions.

[0102] The embodiment of the present invention is a method embodiment corresponding to the above system embodiment. The specific operations of each step can be understood with reference to the description of the system embodiment and will not be elaborated here.

[0103] In summary, the embodiment of the present invention proposes an intelligent remote detection and diagnosis system based on the Internet of Things. By combining Internet of Things technology, cloud computing, big data analysis, and artificial intelligence algorithms, it can achieve real-time monitoring, automatic diagnosis, and early warning of devices or structures. The system can quickly locate problems and provide feasible repair suggestions when a failure or abnormality occurs, thereby improving the operation efficiency of the device and the reliability of the system. Among them, the focus of the system lies in the application of the intelligent analysis module. This module models historical data through machine learning and data mining technologies, monitors the operating status of the device in real time, and determines whether there is an abnormality through comparative analysis. Combining the change trend of sensor data, the system can predict potential failures and issue early warnings in advance, and notify relevant personnel in time for inspection or repair. If a device fails, the system can provide detailed fault location, fault type, and repair suggestions according to the diagnosis results; in addition, the system also has an adaptive function, which can be customized according to different types of devices or structures, optimize monitoring parameters and diagnosis models. This flexibility enables the system to be widely applied in different industries and scenarios, such as intelligent manufacturing, building structure health monitoring, traffic facility detection, etc.

[0104] Device Embodiment 1

[0105] The embodiment of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps described in the method embodiment.

[0106] Device Embodiment 2

[0107] The embodiment of the present invention provides a computer-readable storage medium, on which a program for implementing information transmission is stored. When the program is executed by a processor, it implements the steps described in the method embodiment.

[0108] The computer-readable storage medium described in this embodiment includes, but is not limited to: ROM, RAM, magnetic disk, or optical disc, etc.

[0109] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote monitoring and diagnosis system for lifting machinery based on the Internet of Things, characterized in that Comprising: An Internet of Things module, provided with a plurality of Internet of Things sensor nodes, connected to a data acquisition terminal, for collecting the status data of a hoisting machine in real time and uploading the status data to the data acquisition terminal; A data acquisition terminal, connected to the Internet of Things module and a cloud platform, for summarizing and preprocessing the status data and transmitting the preprocessed status data to the cloud platform; A cloud platform, connected to the data acquisition terminal and a user terminal, for receiving the preprocessed status data and storing and managing the preprocessed status data; wherein, the management includes performing adaptive anomaly monitoring and diagnosis on the hoisting machine based on the preprocessed status data and maintenance records from the user terminal; A user terminal, connected to the cloud platform, for performing corresponding maintenance measures based on the result report provided by the cloud platform; wherein, the result report includes a diagnosis result, a fault type, and a maintenance suggestion.

2. The system according to claim 1, wherein The cloud platform specifically includes: An intelligent analysis module, provided with a real-time analysis unit, a fault knowledge base, an early warning unit, and an adaptive configuration unit that communicate with each other, connected to a visualization module, for performing anomaly monitoring and diagnosis on the hoisting machine based on the preprocessed status data, generating a corresponding diagnosis result, obtaining a fault type and a maintenance suggestion according to the diagnosis result, sending the diagnosis result, the fault type, and the maintenance suggestion to the user terminal, and sending an early warning prompt to the user terminal according to the diagnosis result; and performing real-time adaptive configuration on monitoring parameters and a diagnosis model according to the equipment type of the hoisting machine and maintenance records from the user terminal; A visualization module, connected to the intelligent analysis module, for performing real-time visualization display of the status data, diagnosis result, fault type, and maintenance suggestion of the hoisting machine.

3. The system according to claim 2, wherein The intelligent analysis module is specifically used for: Using the real-time analysis unit to train and generate a benchmark model for the normal operation state of the hoisting machine based on historical status data by using an LSTM neural network model; performing residual analysis to compare the preprocessed status data with the benchmark model, and if there is abnormal data deviating from the baseline, inputting the preprocessed status data into a pre-built diagnosis model for anomaly diagnosis to generate a corresponding diagnosis result; If there is no abnormal data deviating from the baseline, inputting the preprocessed status data into a pre-built prediction model for real-time monitoring to predict the potential fault trend and remaining service life of the hoisting machine; Matching the diagnosis result with historical cases through the fault knowledge base to obtain the fault type and maintenance suggestion of the hoisting machine; wherein, the maintenance suggestion includes fault location information, fault cause analysis, and maintenance steps; Using the early warning unit to send an early warning prompt to the user terminal according to the diagnosis result; Performing real-time adaptive configuration on the threshold of monitoring parameters and the diagnosis rules of the diagnosis model through the adaptive configuration unit according to the equipment type of the hoisting machine and maintenance records from the user terminal.

4. The system according to claim 3, wherein Each equipment type of the hoisting machine is configured with a unique equipment type identification code.

5. A remote monitoring and diagnosis method for lifting machinery based on the Internet of Things, characterized in that Comprising: The status data of the hoisting machinery is collected in real time through the Internet of Things module, and the status data is uploaded to the data acquisition terminal; The data acquisition terminal summarizes and preprocesses the status data, and transmits the processed status data to the cloud platform; The cloud platform receives the processed status data, and stores and manages the processed status data; wherein, the management includes performing adaptive anomaly monitoring and diagnosis on the hoisting machinery based on the processed status data and the maintenance records from the user terminal; The user terminal performs corresponding maintenance measures based on the result report provided by the cloud platform; wherein, the result report includes the diagnosis result, the fault type and the maintenance suggestion.

6. The method according to claim 5, characterized in that, The cloud platform receives the processed status data, and the storage and management of the processed status data specifically includes: The intelligent analysis module in the cloud platform performs anomaly monitoring and diagnosis on the hoisting machinery based on the processed status data, generates the corresponding diagnosis result, obtains the fault type and the maintenance suggestion according to the diagnosis result, sends the diagnosis result, the fault type and the maintenance suggestion to the user terminal, and issues a warning prompt to the user terminal according to the diagnosis result; and performs real-time adaptive configuration on the monitoring parameters and the diagnosis model according to the equipment type of the hoisting machinery and the maintenance records from the user terminal; The visualization module in the cloud platform visually displays the status data, the diagnosis result, the fault type and the maintenance suggestion of the hoisting machinery in real time.

7. The method according to claim 6, characterized in that, The intelligent analysis module in the cloud platform performs anomaly monitoring and diagnosis on the hoisting machinery based on the processed status data, generates the corresponding diagnosis result, obtains the fault type and the maintenance suggestion according to the diagnosis result, sends the diagnosis result, the fault type and the maintenance suggestion to the user terminal, and issues a warning prompt to the user terminal according to the diagnosis result; And the real-time adaptive configuration of the monitoring parameters and the diagnosis model according to the equipment type of the hoisting machinery and the maintenance records from the user terminal specifically includes: The real-time analysis unit in the intelligent analysis module uses the LSTM neural network model to train and generate a benchmark model under the normal operation state of the hoisting machinery based on the historical status data; the processed status data is compared with the benchmark model by using residual analysis, if there is abnormal data deviating from the baseline, the processed status data is input into the pre-constructed diagnosis model for anomaly diagnosis to generate the corresponding diagnosis result; If there is no abnormal data deviating from the baseline, the processed status data is input into the pre-constructed prediction model for real-time monitoring, and the potential fault trend and the remaining service life of the hoisting machinery are predicted; The fault knowledge base in the intelligent analysis module matches the diagnosis result with the historical cases to obtain the fault type and the maintenance suggestion of the hoisting machinery; wherein, the maintenance suggestion includes the fault location information, the fault cause analysis and the maintenance steps; The warning unit in the intelligent analysis module issues a warning prompt to the user terminal according to the diagnosis result; The adaptive configuration unit in the intelligent analysis module adaptively configures the thresholds of the monitoring parameters and the diagnostic rules of the diagnostic model in real time according to the equipment type of the lifting machinery and the maintenance records from the user terminal.

8. The method according to claim 7, characterized in that Each equipment type of the lifting machinery is configured with a unique equipment type identification code.

9. An electronic device, characterized in that, Including: A memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the steps of the method for remote monitoring and diagnosis of lifting machinery based on the Internet of Things according to any one of claims 5-8 are implemented.

10. A computer-readable storage medium, characterized in that, An implementation program for information transmission is stored on the computer-readable storage medium. When the program is executed by the processor, the steps of the method for remote monitoring and diagnosis of lifting machinery based on the Internet of Things according to any one of claims 5-8 are implemented.

Images