Crane status online monitoring system and method

Abstract

The present invention belongs to the technical field of cranes and provides an online monitoring system and method for crane status. The method comprises: obtaining the current working state of the crane based on the analysis of the key operating parameters of the crane, the working state including the non-lifting state and the lifting state; determining a fault analysis model corresponding to the working state and switching it to an activated state; determining a number of crane status data corresponding to the activated fault analysis model, inputting each crane status data into the activated fault analysis model, obtaining the fault type and fault probability predicted by the fault analysis model; generating and outputting an alarm signal corresponding to the fault type whose fault probability is higher than the probability threshold. Compared with the traditional single model monitoring method, the fault analysis model under different working states set by the present invention has a smaller model size. The smaller model requires fewer resources and has a faster processing speed during data processing and calculation, thereby improving the fault monitoring and early warning rate.

Description

Technical Field

[0001] The present invention relates to the technical field of cranes, and in particular to a system and method for online monitoring of crane status. Background Art

[0002] Online monitoring of crane status is a system that uses sensors, data acquisition and transmission, data analysis and processing technologies to grasp the working status of the crane in real time and prevent failures and accidents.

[0003] Some research in the prior art has involved the use of models to identify, predict and alarm abnormalities in cranes. For example, CN112418460A discloses a fault diagnosis method for engineering vehicles, including: correlating the operating data of the vehicle to be diagnosed with the operating data of a sample fault vehicle and outputting a correlation degree to be diagnosed; when the correlation degree to be diagnosed is not less than a target correlation degree, determining the characteristics of the fault to be diagnosed based on the operating data of the vehicle to be diagnosed; inputting the characteristics of the fault to be diagnosed into a fault diagnosis model and outputting a diagnosis result; wherein the fault diagnosis model is trained using the sample fault characteristics of the sample fault vehicle as samples and using a predetermined fault type corresponding to the sample fault characteristics as a sample label.

[0004] The above-mentioned existing technology uses a single model to identify and predict various types of crane faults. Under this fault monitoring method, the model used needs to be trained using multiple data, resulting in the final model being generally large in size. An excessively large model size will lead to a reduced response rate of the model, which is not conducive to more timely warning of early or impending faults.

[0005] Therefore, how to further improve the fault monitoring and early warning rate of cranes is a technical problem that needs to be solved or improved. Summary of the Invention

[0006] To this end, the present invention provides a crane status online monitoring method, system, electronic equipment, computer storage medium and computer program product to solve the above technical problems.

[0007] The present invention discloses a method for online monitoring of crane status, which comprises the following steps:

[0008] Analyzing the current working state of the crane based on key operating parameters of the crane, wherein the working state includes a non-lifting state and a lifting state; determining a fault analysis model corresponding to the working state and switching it to an active state;

[0009] Determining a plurality of crane status data corresponding to the activated fault analysis model, inputting each of the crane status data into the activated fault analysis model, and obtaining a fault type and a fault probability predicted by the fault analysis model;

[0010] An alarm signal corresponding to the fault type whose fault probability is higher than a probability threshold is generated and output.

[0011] In some embodiments, the fault analysis model corresponding to the lifting state includes a plurality of fault analysis sub-models, each of which corresponds to a lifting sub-state; wherein the lifting sub-state includes at least one of a lifting state, a lowering state, a translation state, and a rotation state;

[0012] According to the historical lifting data of the crane for this operation object, the order of each lifting sub-state and the corresponding average time consumption are obtained according to the statistics of the historical lifting data. When the ratio of the actual time consumption of the lifting sub-state being executed to the average time consumption reaches a preset ratio, the fault analysis sub-model corresponding to the lifting sub-state with the next sequence number is controlled to be activated.

[0013] In some embodiments, the preset ratio is determined by:

[0014] Extracting all time-consuming data corresponding to the currently executed lifting sub-state from the historical lifting data, calculating the fluctuation amplitude of all the time-consuming data, and obtaining the preset ratio according to the fluctuation amplitude matching;

[0015] The fluctuation range is the variance or standard deviation of all the time-consuming data.

[0016] In some embodiments, the fault analysis model is constructed based on a convolutional neural network, which includes at least a convolutional layer, a pooling layer, and a fully connected layer;

[0017] Before inputting each crane status data into the activated fault analysis model, each crane status data is normalized to map it to a specific interval.

[0018] In some embodiments, generating and outputting an alarm signal corresponding to the fault type for which the fault probability is higher than a probability threshold includes:

[0019] For the fault types whose fault probabilities are higher than the probability threshold, taking them as target fault types, generating the alarm signals corresponding to the target fault types, and determining the output order of the alarm signals according to the fault hazard levels corresponding to the target fault types;

[0020] The alarm signals are outputted according to the output sequence.

[0021] The present invention also provides a crane status online monitoring system, which includes a model selection module, a fault analysis module, and an early warning module;

[0022] The model selection module is used to analyze the current working state of the crane based on the key operating parameters of the crane, wherein the working state includes a non-lifting state and a lifting state; determine the fault analysis model corresponding to the working state and switch it to an active state;

[0023] The fault analysis module is configured to determine a plurality of crane status data corresponding to the activated fault analysis model, input each of the crane status data into the activated fault analysis model, and obtain a fault type and fault probability predicted by the fault analysis model;

[0024] The early warning module is used to generate and output an alarm signal corresponding to the fault type whose fault probability is higher than a probability threshold.

[0025] The present invention also discloses an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and runnable on the at least one processor, wherein the computer program code implements the method described in any of the preceding items when executed.

[0026] The present invention also discloses a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program code is executed, the method as described in any of the above items is implemented.

[0027] The present invention also discloses a computer program product. The computer program product is pre-packaged with computer program code. When the computer program code is executed, the method described in any of the above items is implemented.

[0028] The beneficial effect of the present invention is that: by subdividing the working status and applying targeted fault analysis models, the present invention greatly reduces the data processing volume and complexity of the fault analysis models under different working conditions compared with the traditional single model monitoring method, making the model smaller in size. The smaller model requires fewer resources and has a faster processing speed during data processing and calculation, thereby improving the fault monitoring and early warning rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 This is a flow chart of a method for online monitoring of crane status disclosed in an embodiment of the present invention;

[0031] Figure 2 is a schematic diagram of the structure of the fault analysis model disclosed in an embodiment of the present invention;

[0032] Figure 3 It is a structural diagram of a crane status online monitoring system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following specific embodiments illustrate the implementation of this application. Those familiar with the art can easily understand the other advantages and functions of this application from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of this application, but not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0034] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0035] Existing fault monitoring methods use a single model to identify and predict multiple types of crane faults. This method requires training the model using a variety of data, resulting in a generally large model size. This excessively large model size reduces the model's response rate, hindering timely warning of early or impending faults. Therefore, further improving the rate of crane fault monitoring and warning is a technical issue that needs to be addressed or improved.

[0036] like Figure 1 As shown, an embodiment of the present invention discloses a method for online monitoring of crane status, the method comprising the following steps:

[0037] S10, analyzing the current working state of the crane based on key operating parameters of the crane, wherein the working state includes a non-lifting state and a lifting state; determining a fault analysis model corresponding to the working state, and switching the model to an active state.

[0038] During this step, the crane's operation involves several key operating parameters, such as lift capacity, lifting height, operating speed, and operating range. Real-time monitoring and analysis of these parameters can determine whether the crane is currently in a non-lifting or lifting state. For example, if the lift capacity is zero, the lifting height remains unchanged, and other moving mechanisms are inactive, the crane is considered to be in a non-lifting state. Conversely, if the lift capacity is greater than zero and the lifting mechanism or other related mechanisms are in motion, the crane is considered to be in a lifting state.

[0039] Since the crane has different fault types in different working states, the present invention constructs different fault analysis models for non-lifting state and lifting state respectively. After determining the current working state of the crane, the corresponding fault analysis model is switched to the active state so that targeted fault analysis can be performed later. This approach avoids using a single model to handle all fault types, thereby reducing the complexity and volume of the model. Specifically, if it is determined to be a non-lifting state, a fault analysis model specifically for the non-lifting state is activated. This model only needs to handle the types of faults that may occur in this state, such as electrical system faults, abnormal initial states of mechanical components, etc.; and when it is determined to be a lifting state, a fault analysis model specifically for the non-lifting state is activated. This model can handle the types of faults that may occur in this state, such as lifting mechanism faults, electrical system faults, structural component faults, etc. The types of faults of the crane in different working states are generally divided into electrical system faults and mechanical system faults, as shown in the following table:

[0040]

[0041]

[0042] S20, determining a number of crane status data corresponding to the activated fault analysis model, inputting each of the crane status data into the activated fault analysis model, and obtaining the fault type and fault probability predicted by the fault analysis model.

[0043] In this step, different fault analysis models require different input data. For activated fault analysis models, corresponding crane status data is collected. This data may include electrical parameters (such as voltage, current, and temperature) and mechanical parameters (such as vibration, stress, and wear) monitored in real time by various sensors. For example, if the fault analysis model for the lifting state is activated, in addition to conventional parameters such as lifting weight and lifting height, data closely related to the lifting operation, such as the hoist motor current, wire rope tension, and brake temperature, must also be collected.

[0044] The collected status data is fed into the activated fault analysis model. Based on existing training data and algorithms, the model analyzes and processes the input data to predict the likely fault type and probability of each. For example, the model might output results such as "wire rope broken, 80% probability" or "motor overheating, 30% probability." These results provide a basis for subsequent warnings and actions.

[0045] S30 , generating and outputting an alarm signal corresponding to the fault type whose fault probability is higher than a probability threshold.

[0046] In this step, the probability of each fault predicted by the fault analysis model is compared with a set probability threshold. For fault types with a probability higher than the threshold, the system generates and outputs a corresponding alarm signal. This alarm signal can be presented in a variety of ways, such as audio and visual alarms, SMS notifications, and app push notifications, allowing relevant personnel to be promptly informed of possible faults and take appropriate measures, such as shutting down the machine for inspection and repairs, thereby providing timely warnings of early or impending faults.

[0047] Furthermore, these probability thresholds are determined based on practical experience, the importance of the crane, and the severity of the consequences of the failure. For example, for failures that could potentially lead to serious safety incidents, the threshold might be set relatively low (e.g., 70%) to ensure timely warnings are issued even if the probability of the failure is not particularly high. For failures with less significant impact, the threshold might be set higher (e.g., 90%).

[0048] The above-mentioned crane status online monitoring method of the present invention, through the subdivision of working status and the application of targeted fault analysis models, greatly reduces the data processing volume and complexity of fault analysis models under different working conditions compared to traditional single-model monitoring methods, making the model smaller in size. Smaller models require fewer resources and faster processing speeds during data processing and calculation, thereby improving fault monitoring and early warning rates.

[0049] In some embodiments, the fault analysis model corresponding to the lifting state includes a plurality of fault analysis sub-models, each of which corresponds to a lifting sub-state; wherein the lifting sub-state includes at least one of a lifting state, a lowering state, a translation state, and a rotation state;

[0050] According to the historical lifting data of the crane for this operation object, the order of each lifting sub-state and the corresponding average time consumption are obtained according to the statistics of the historical lifting data. When the ratio of the actual time consumption of the lifting sub-state being executed to the average time consumption reaches a preset ratio, the fault analysis sub-model corresponding to the lifting sub-state with the next sequence number is controlled to be activated.

[0051] In an embodiment of the present invention, the crane's lifting state can be further subdivided into various lifting sub-states, such as the hoisting state, the lowering state, the translation state, and the rotation state. Each lifting sub-state has different operating characteristics, stress conditions, and potential fault types. For example, the lifting state primarily focuses on faults related to the lifting mechanism, such as problems with the wire rope, drum, and brake during the lifting process; while the lowering state focuses more on faults related to lowering speed control and brake reliability. Therefore, the present invention further constructs a dedicated fault analysis sub-model for each lifting sub-state, enabling more targeted monitoring and prediction of faults at different stages.

[0052] At the same time, the present invention also collects the crane's historical lifting data for the current operation object. This data includes the order of each lifting sub-state in each lifting operation and the time spent in each lifting sub-state. For example, the crane is unloading containers from a cargo ship. Based on the statistics of the crane's unloading data for the first 30 and 50 times, it is found that it generally first enters the lifting state, which takes an average of 2 minutes, followed by the translation state, which takes an average of 3 minutes, and so on. When the ratio of the actual time spent in a certain lifting sub-state to the average time reaches a preset ratio (for example, 80%), it indicates that the lifting sub-state is about to end. At this time, the fault analysis sub-model corresponding to the next lifting sub-state is activated. This can avoid the problem of a fault monitoring "blank period" caused by activating the corresponding fault analysis sub-model after entering the next lifting sub-state.

[0053] In some embodiments, the preset ratio is determined by:

[0054] Extracting all time-consuming data corresponding to the currently executed lifting sub-state from the historical lifting data, calculating the fluctuation amplitude of all the time-consuming data, and obtaining the preset ratio according to the fluctuation amplitude matching;

[0055] The fluctuation range is the variance or standard deviation of all the time-consuming data.

[0056] In an embodiment of the present invention, the present invention sets the size of the preset ratio to be determined by the fluctuation of the historical time consumption corresponding to the currently executed lifting sub-state, that is, the larger the fluctuation amplitude of the historical time consumption, the smaller the preset ratio is set, that is, the earlier the fault analysis sub-model corresponding to the next numbered lifting sub-state is activated; otherwise, the smaller the preset ratio is set, the later the fault analysis sub-model corresponding to the next numbered lifting sub-state is activated.

[0057] Specifically, after determining the currently executing lifting sub-state (e.g., raising, lowering, etc.), the entire time consumption data corresponding to that lifting sub-state is filtered from the historical data. For example, if the current state is raising, the time consumption data for all lifting states in the historical operations are extracted. This data reflects the time spent on each lift in the current operation. Based on this, the present invention further calculates the fluctuation range of this time consumption data, for example, by calculating the variance or standard deviation to obtain the fluctuation range. The fluctuation range is used to measure the degree of dispersion of this time consumption data.

[0058] If the fluctuation (variance or standard deviation) is small, the duration of the lifting substate is relatively stable, and the duration of each operation is close to the average. In this case, a relatively high preset ratio can be set, such as 90%. This means that only when the actual duration reaches 90% of the average duration can the lifting substate be considered nearly completed, and the fault analysis submodel corresponding to the next lifting substate be activated.

[0059] Large fluctuations indicate that the duration of the lifting sub-state is unstable, with significant variations between operations. To prevent premature or late activation of the next fault analysis sub-model due to time fluctuations, a relatively low preset ratio, such as 80%, is set. This means that when the actual duration reaches 80% of the average, the next model is activated. This allows for greater time fluctuations and ensures timely switching to fault monitoring when a potential lifting sub-state transition occurs.

[0060] By determining the preset ratio according to the fluctuation amplitude of historical data, the present invention can more flexibly and accurately dynamically activate the corresponding fault analysis sub-model according to the time-consuming characteristics of different lifting sub-states, thereby improving the real-time and effectiveness of crane fault monitoring.

[0061] In some embodiments, the fault analysis model is constructed based on a convolutional neural network, which includes at least a convolutional layer, a pooling layer, and a fully connected layer;

[0062] Before inputting each crane status data into the activated fault analysis model, each crane status data is normalized to map it to a specific interval.

[0063] In this embodiment of the present invention, the input data consists of various types of status data related to crane failures, such as vibration signals, temperature data, and electrical parameters. This data requires preprocessing based on its type and conversion into a format suitable for CNN processing. If the data is a one-dimensional time series, such as a vibration signal changing over time, it can be organized into a one-dimensional array. If it is two-dimensional image data, such as a temperature distribution image acquired by a sensor array, it should be maintained in a two-dimensional matrix format. Multi-channel data, such as vibration signals collected simultaneously from multiple locations, can be input as multi-channel data, similar to the RGB channels of a color image.

[0064] Before inputting the above input data into the fault analysis model, it needs to be normalized. Map the data to a specific interval, such as [0,1] or [-1,1]. For example, for temperature data with a value range of [0,100], the formula Normalize, where is the original data, and are the minimum and maximum values ​​of this group of data respectively.

[0065] like Figure 2 As shown, the fault analysis model of the present invention is constructed based on a convolutional neural network, which includes at least a convolutional layer, a pooling layer, and a fully connected layer.

[0066] The convolutional layer is the core component of a CNN. It convolves the input data with a convolution kernel to extract local features. A convolution kernel is a learnable weight matrix, such as a 3×3 or 5×5 matrix. For one-dimensional data, the convolution kernel is a one-dimensional vector. For example, when processing a vibration signal, a convolution kernel with a 3-timestep length can capture the signal's changing pattern over a short period of time. Multiple convolution kernels with different weights operate in parallel, each extracting a specific feature, such as vibration patterns of different frequencies or specific temperature trends.

[0067] The pooling layer follows the convolutional layer and is used to reduce the dimensionality of the feature maps output by the convolutional layer, reducing the amount of data while retaining the key features. Pooling can be performed using either max pooling or average pooling. Pooling not only reduces data dimensionality, computational complexity, and model parameters, but also improves model robustness to a certain extent, making it more adaptable to small changes in the data.

[0068] After feature extraction and dimensionality reduction in the convolutional and pooling layers, the resulting feature map is expanded into a one-dimensional vector and then input into a fully connected layer. The fully connected layer can determine the crane's current fault state based on previously extracted vibration, temperature, and electrical characteristics. Furthermore, the fully connected layer in the present invention is preferably a multi-layer fully connected layer, meaning that multiple fully connected layers form a network, with the output of each fully connected layer serving as the input to the next fully connected layer. Through a layer-by-layer progressive approach, features are continuously abstracted and combined. For example, the first fully connected layer may learn some relatively basic feature combinations, while subsequent fully connected layers build on this foundation to learn more advanced, more abstract features to improve the accuracy of fault classification.

[0069] In addition, an output layer should also be included. The number of neurons in the output layer depends on the specific fault analysis task. For example, if n different types of crane faults are to be identified, the output layer will have n neurons. The activation function of the output layer is selected according to the task type. For multi-classification problems, the Softmax function is preferably used. The Softmax function converts the output of each neuron into a probability value. The sum of these probability values ​​is 1, representing the probability that the input data belongs to each fault type. For example, after the output of the three neurons in the output layer is processed by the Softmax function, the result is [0.1, 0.3, 0.6], indicating that the probability that the input data belongs to the three fault types is 10%, 30%, and 60%, respectively.

[0070] In some embodiments, generating and outputting an alarm signal corresponding to the fault type for which the fault probability is higher than a probability threshold includes:

[0071] For the fault types whose fault probabilities are higher than the probability threshold, taking them as target fault types, generating the alarm signals corresponding to the target fault types, and determining the output order of the alarm signals according to the fault hazard levels corresponding to the target fault types;

[0072] The alarm signals are outputted according to the output sequence.

[0073] In this embodiment of the present invention, fault types with a probability above a probability threshold are selected as target fault types requiring special attention and treatment. For example, if the probability threshold is set at 50%, and the fault analysis model predicts a 60% probability for "wire rope breakage" and a 70% probability for "motor overheating," these two fault types will be identified as target fault types.

[0074] Different fault types pose varying degrees of risk to crane operations and personnel safety. Therefore, the system assigns a specific fault severity level to each target fault type. Fault severity levels can be categorized based on the severity of the potential consequences of the fault, for example, into high, medium, and low. A "wire rope break" could cause a heavy object to fall, resulting in serious injury, loss of life, and equipment damage, so its severity level might be set to "high." Meanwhile, a "motor overheat" might not pose serious consequences if addressed promptly, so its severity level might be set to "medium." The order in which alarm signals are output is determined based on the severity level of each target fault type. Faults with higher severity levels receive higher priority, ensuring that operators first focus on the most serious faults and take timely action to prevent more serious accidents. For example, if both "wire rope break" (high severity level) and "motor overheat" (medium severity level) occur simultaneously, the system will output the "wire rope break" alarm signal first, followed by the "motor overheat" alarm signal.

[0075] An embodiment of the present invention further provides an online monitoring system for crane status, the system comprising a model selection module, a fault analysis module, and an early warning module;

[0076] The model selection module is used to analyze the current working state of the crane based on the key operating parameters of the crane, wherein the working state includes a non-lifting state and a lifting state; determine the fault analysis model corresponding to the working state and switch it to an active state;

[0077] The fault analysis module is configured to determine a plurality of crane status data corresponding to the activated fault analysis model, input each of the crane status data into the activated fault analysis model, and obtain a fault type and fault probability predicted by the fault analysis model;

[0078] The early warning module is used to generate and output an alarm signal corresponding to the fault type whose fault probability is higher than a probability threshold.

[0079] This system corresponds to the aforementioned method, and its beneficial technical effects will not be described in detail here.

[0080] An embodiment of the present invention further discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the computer program code implements the method described in any of the preceding items when executed.

[0081] An embodiment of the present invention further discloses a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program code is executed, the method as described in any of the above items is implemented.

[0082] An embodiment of the present invention further discloses a computer program product. The computer program product is pre-packaged with computer program code, and when the computer program code is executed, the method as described in any of the above items is implemented.

[0083] The device embodiments described above are merely illustrative, and the modules designed therein may or may not be physically separate, i.e., they may be located in one place or distributed in multiple locations. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. Those skilled in the art can understand and implement the present invention without inventive effort.

[0084] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0085] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A crane status online monitoring method, characterized in that: The method comprises the following steps: Analyzing the current working state of the crane based on key operating parameters of the crane, wherein the working state includes a non-lifting state and a lifting state; determining a fault analysis model corresponding to the working state and switching it to an active state; Determining a plurality of crane status data corresponding to the activated fault analysis model, inputting each of the crane status data into the activated fault analysis model, and obtaining a fault type and a fault probability predicted by the fault analysis model; generating and outputting an alarm signal corresponding to the fault type for which the fault probability is higher than a probability threshold; The fault analysis model corresponding to the lifting state includes several fault analysis sub-models, each of which corresponds to a lifting sub-state; wherein the lifting sub-state includes at least one of a lifting state, a lowering state, a translation state, and a rotation state; Based on historical lifting data of the crane for the current operation object, a sequence of each lifting sub-state and a corresponding average time consumption are statistically determined based on the historical lifting data; when a ratio of an actual time consumption of the lifting sub-state being executed to the average time consumption reaches a preset ratio, control activation of the fault analysis sub-model corresponding to the lifting sub-state with the next sequence number; The generating and outputting an alarm signal corresponding to the fault type whose fault probability is higher than a probability threshold comprises: For the fault types whose fault probabilities are higher than the probability threshold, taking them as target fault types, generating the alarm signals corresponding to the target fault types, and determining the output order of the alarm signals according to the fault hazard levels corresponding to the target fault types; The alarm signals are outputted according to the output sequence.

2. The method for online monitoring of crane status according to claim 1, characterized in that: The preset ratio is determined by: Extracting all time-consuming data corresponding to the currently executed lifting sub-state from the historical lifting data, calculating the fluctuation amplitude of all the time-consuming data, and obtaining the preset ratio according to the fluctuation amplitude matching; The fluctuation range is the variance or standard deviation of all the time-consuming data.

3. The method for online monitoring of crane status according to claim 1, characterized in that: The fault analysis model is constructed based on a convolutional neural network, which includes at least a convolutional layer, a pooling layer, and a fully connected layer; Before inputting each crane status data into the activated fault analysis model, each crane status data is normalized to map it to a specific interval.

4. A crane status online monitoring system, characterized by: The system includes a model selection module, a fault analysis module, and an early warning module; The model selection module is used to analyze the current working state of the crane based on the key operating parameters of the crane, wherein the working state includes a non-lifting state and a lifting state; determine the fault analysis model corresponding to the working state and switch it to an active state; The fault analysis module is configured to determine a plurality of crane status data corresponding to the activated fault analysis model, input each of the crane status data into the activated fault analysis model, and obtain a fault type and fault probability predicted by the fault analysis model; The early warning module is configured to generate and output an alarm signal corresponding to the fault type for which the fault probability is higher than a probability threshold; The fault analysis model corresponding to the lifting state includes several fault analysis sub-models, each of which corresponds to a lifting sub-state; wherein the lifting sub-state includes at least one of a lifting state, a lowering state, a translation state, and a rotation state; Based on historical lifting data of the crane for the current operation object, a sequence of each lifting sub-state and a corresponding average time consumption are statistically determined based on the historical lifting data; when a ratio of an actual time consumption of the lifting sub-state being executed to the average time consumption reaches a preset ratio, control activation of the fault analysis sub-model corresponding to the lifting sub-state with the next sequence number; The generating and outputting an alarm signal corresponding to the fault type whose fault probability is higher than a probability threshold comprises: For the fault types whose fault probabilities are higher than the probability threshold, taking them as target fault types, generating the alarm signals corresponding to the target fault types, and determining the output order of the alarm signals according to the fault hazard levels corresponding to the target fault types; The alarm signals are outputted according to the output sequence.

5. An electronic device, characterized in that: The electronic device comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the computer program implements the method according to any one of claims 1 to 3 when executed.

6. A computer storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 3 is implemented.

7. A computer program product, characterized in that: The computer program product is pre-packaged with a computer program, which implements the method according to any one of claims 1 to 3 when executed.

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