Lifting appliance state monitoring method and system, terminal and medium

Abstract

The invention belongs to the technical field of lifting appliance monitoring, and particularly discloses a lifting appliance state monitoring method and system, a terminal and a medium, and the method comprises the following steps: obtaining multiple types of sensor signals corresponding to a preset key state of a lifting appliance, combining time alignment processing and multi-source signal consistency judgment, and utilizing stream-oriented calculation to realize real-time state confirmation; meanwhile, a sensor abnormal mode is identified based on historical data, and a redundancy judgment strategy is dynamically adjusted; and further predicting the fault trend of the sensor through the trained state prediction model and generating a pre-maintenance suggestion. The system and the terminal are provided with corresponding function modules, high-reliability lifting appliance state monitoring and fault early warning are cooperatively achieved, and the monitoring accuracy and the safety guarantee level are remarkably improved. By introducing a multi-sensor redundancy detection mechanism, misjudgment or missed judgment caused by a single sensor fault is effectively avoided, and the reliability and accuracy of lifting appliance key state detection are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of lifting gear monitoring technology, specifically relating to a lifting gear status monitoring method, system, terminal, and medium. Background Technology

[0002] As a critical load-bearing component in lifting machinery, the safety performance of the spreader directly affects the overall safety and efficiency of lifting operations. Currently, the monitoring of spreader status mainly relies on a single sensor, such as a proximity switch, to detect key operational states such as the telescopic dimensions of the spreader, the opening and closing status of the pivot pin, and the position of the hook. While this single-sensor detection solution is simple in structure and low in cost, it presents significant reliability risks in practical applications.

[0003] Proximity switches and similar sensors are susceptible to various factors such as circuit faults, foreign object interference, component aging, and adjustment errors in complex industrial environments, which can lead to false sensing, missed sensing, or unstable output. This can cause malfunctions in lifting equipment or failure in status recognition, seriously threatening operational safety and reducing equipment operating efficiency. Summary of the Invention

[0004] This invention addresses the problems in the prior art by providing a method, system, terminal, and medium for monitoring the status of lifting equipment, thus solving the problem of lifting equipment malfunctions or status recognition failures caused by sensor mis-sensing, incomplete sensing, or unstable output.

[0005] The technical solution adopted in this invention is as follows: Firstly, this application provides a method for monitoring the condition of a spreading device, the method comprising the following steps: Step S1: Obtain the first type of sensor signal and the second type of sensor signal corresponding to the preset key state of the spreader; Step S2: Perform time alignment processing on the first type of sensor signals and the second type of sensor signals to construct a multi-source state signal set; Step S3: Perform consistency judgment on the multi-source state signal set based on parallel or series logic to obtain the redundancy judgment result of each key state. Step S4: Input the redundancy determination result into the streaming computing engine, and combine it with the state change rate, signal stability threshold and acquisition time window to perform real-time state confirmation. Step S5: Based on historical state data, identify abnormal sensor patterns, update redundant judgment strategies, and adjust the judgment logic parameters for the corresponding states. Step S6: Using the trained state prediction model, predict the potential failure trends of the sensor in the future time period and generate pre-maintenance suggestions.

[0006] Furthermore, in step S1, the key states include single box extension state, pivot pin opening and closing state, hook allowable position state, hook hook detachment state, center lock on state, and guide plate state. The first type of sensor signal and the second type of sensor signal come from sensors installed in different types or different positions.

[0007] Furthermore, in step S2, the time alignment process includes selecting a unified time reference period based on the sampling periods of various sensors, and interpolating and resampling the signals of various sensors. In the time alignment process, for sensor signals with delayed, lost, or jittered sampling data, a sliding time window mechanism and a weighted average strategy are used to estimate missing values. After time alignment, the constructed multi-source state signal set is optimized based on signal fluctuation trends, correlation weights, or acquisition reliability indicators.

[0008] Furthermore, in step S3, the consistency judgment includes comparing multiple sensor signals corresponding to the same critical state according to a preset parallel logic rule. When any signal meets the expected state condition, the critical state is determined to be a valid state. Consistency judgment also includes verifying multiple sensor signals according to preset serial logic rules. When all signals meet the expected state conditions, the key state is determined to be a valid state.

[0009] Furthermore, based on the historical stability data and current fluctuation range of each sensor, the adopted logical judgment strategy is dynamically adjusted. When a sensor is detected to be unstable for a long period of time or inconsistent with other signals, it automatically switches to a redundant judgment mechanism based on multi-signal weighted voting. In the redundancy determination mechanism of multi-signal weighted voting, each sensor signal is assigned a different weight value according to its historical false alarm rate, current signal-to-noise ratio, and degree of deviation from other signals. A comprehensive score is calculated based on the weight of each signal and its corresponding state judgment result. When the comprehensive score exceeds the set threshold, the corresponding key state is determined to be a valid state.

[0010] Furthermore, in step S4, during the real-time status confirmation process, the redundancy determination results are continuously processed based on the streaming computing engine. The rate of change of status is tracked within each acquisition time window, and the status is determined to be in an effective maintenance state in combination with the stability threshold of the current signal.

[0011] Furthermore, in step S5, during the process of identifying abnormal sensor modes, by analyzing the frequency of sensor signal jumps, duration of deviation, and inconsistency ratio with other sensor results in historical state data, abnormal signals that deviate from the normal mode for a long time are identified and marked as low-confidence sources, reducing their participation weight in redundancy judgment or temporarily removing them from the judgment. In the process of updating the redundancy judgment strategy, the logical judgment parameters of the corresponding key states are dynamically adjusted based on the anomaly identification results and the recent sensor performance. This includes switching the redundant logical structure, updating the signal combination method for participating in voting, and adjusting the state judgment threshold.

[0012] Secondly, this application provides a spreader condition monitoring system, which includes: The signal acquisition module is used to acquire the first type of sensor signals and the second type of sensor signals corresponding to the preset key states of the spreader; The signal processing module is used to perform time alignment processing on the first type of sensor signals and the second type of sensor signals to construct a multi-source state signal set; The determination module is used to perform consistency determination on the multi-source state signal set based on parallel logic or series logic, and obtain the redundancy determination result for each key state. The status confirmation module is used to input the redundancy judgment results into the streaming computing engine, and combine them with the status change rate, signal stability threshold and acquisition time window to realize real-time status confirmation. The anomaly identification and strategy adjustment module is used to identify sensor anomaly patterns based on historical state data, update redundant judgment strategies, and adjust the corresponding state judgment logic parameters. The prediction module is used to predict the potential failure trends of sensors in the future time period and generate pre-maintenance suggestions based on the trained state prediction model.

[0013] Thirdly, this application provides a terminal, including: Memory, used to store the spreader status monitoring program; A processor is configured to implement the steps of the spreader condition monitoring method as described in the first aspect when executing the spreader condition monitoring system.

[0014] Fourthly, this application provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the lifting device status monitoring method as described in the first aspect.

[0015] As can be seen from the above technical solutions, the advantages of the present invention are: By introducing a multi-sensor redundancy detection mechanism, false or missed detections caused by the failure of a single sensor are effectively avoided, significantly improving the reliability and accuracy of critical status detection of the spreader. By combining parallel and series logic for consistency judgment, collaborative verification of multi-source state signals is achieved, enhancing the system's ability to identify abnormal signals and improving the fault tolerance of detection. By employing a streaming computing engine that combines the rate of change of state and the signal stability threshold, the status of the spreader can be confirmed in real time, and the status fluctuations can be responded to in a timely manner, avoiding malfunctions caused by transient signal interference. Based on historical data analysis, the abnormal pattern recognition and dynamic redundancy judgment strategy adjustment improve the system's adaptability to sensor performance degradation and environmental changes, and extend the system's stable operation cycle. By combining the trained state prediction model, potential sensor failure trends can be predicted in advance, supporting the formulation of pre-maintenance strategies, effectively reducing the risk of sudden equipment failures, and ensuring the safety of lifting operations. Attached Figure Description

[0016] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the method for monitoring the status of a lifting device provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 As shown, the present invention provides a method for monitoring the status of a lifting device, comprising the following steps: Step S1: Obtain the first type of sensor signal and the second type of sensor signal corresponding to the preset key state of the spreader; In this embodiment, key status signals preset by various types of sensors are first collected from the spreader. These key statuses include, but are not limited to, single-box extension / retraction status, pivot pin opening / closing status, hook permissible position status, hook engagement / disengagement status, center lock engagement status, and guide plate status. The first type of sensor signals can originate from proximity switches, encoders, or other devices, while the second type can be provided by magnetic scales, photoelectric sensors, or other types of sensors. Each sensor is installed at a pre-defined location on key parts of the spreader to ensure comprehensive perception and coverage of all key statuses. The collected signals are transmitted in real-time to the signal processing unit for subsequent processing.

[0020] Step S2: Perform time alignment processing on the first type of sensor signals and the second type of sensor signals to construct a multi-source state signal set; For the collected signals from various sensors, the system first performs time alignment processing. Based on the differences in sampling periods among different sensors, a unified time reference is selected, and the sensor data is interpolated and resampled to ensure signal synchronization in the time dimension. For cases with data delays, loss, or fluctuations, a sliding time window mechanism and a weighted averaging strategy are used to estimate and compensate for missing data. Subsequently, combining the fluctuation trend of sensor signals, correlation weights, and acquisition reliability indicators, the multi-source state signals are sorted and filtered to construct a stable and highly reliable multi-source state signal set.

[0021] Step S3: Perform consistency judgment on the multi-source state signal set based on parallel or series logic to obtain the redundancy judgment result of each key state. This step employs both parallel and series logic rules to determine the consistency of the multi-source state signal set. The parallel logic rule defines a critical state as valid only when at least one sensor signal meets a preset state condition; the series logic rule requires all sensor signals to meet the state condition for a state to be considered valid. Through these preset logic rules, the system generates redundant determination results for each critical state. This method effectively utilizes the complementarity of multi-sensor information to improve the accuracy and robustness of state determination.

[0022] Step S4: Input the redundancy determination result into the streaming computing engine, and combine it with the state change rate, signal stability threshold and acquisition time window to perform real-time state confirmation. The redundancy determination result obtained in step S3 is input into the streaming computing engine, and the system continuously monitors the rate of state change within each acquisition time window. Combined with a preset signal stability threshold, the system evaluates the maintenance of the current state and confirms the validity of the spreader's state in real time. For short-term state fluctuations that do not exceed the stability tolerance range, the system maintains the original determination state to avoid misjudgments caused by transient noise or interference. This real-time state confirmation process ensures the dynamic response capability and anti-interference performance of the state determination.

[0023] Step S5: Based on historical state data, identify abnormal sensor patterns, update redundant judgment strategies, and adjust the judgment logic parameters for the corresponding states. Based on accumulated historical status data, the system performs abnormal pattern analysis on sensor signals. By monitoring the signal transition frequency, duration of deviation, and consistency with other sensor signals, it identifies abnormal signals that deviate from normal conditions for extended periods and marks them as low-confidence signal sources. The system then dynamically adjusts its redundancy judgment strategy accordingly, including switching between different redundant logic structures, adjusting the sensor combinations involved in the judgment, and updating the status judgment threshold, to ensure accurate determination of the spreader status even under conditions of sensor performance degradation or abnormal environments.

[0024] Step S6: Using the trained state prediction model, predict the potential failure trends of the sensor in the future time period and generate pre-maintenance suggestions.

[0025] By utilizing historical sensor status data, a status prediction model is trained based on machine learning or other artificial intelligence techniques. This model can predict potential sensor failure trends over a future period, identifying potential risks in advance. The system combines the prediction results to automatically generate pre-maintenance recommendations, guiding maintenance personnel to carry out targeted sensor maintenance or replacement work, thereby reducing unexpected failures and ensuring the stable operation and safety of the spreader monitoring system.

[0026] In some embodiments, in step S1, the key states include single box extension state, pivot pin opening and closing state, hook allowable position state, hook hook detachment state, center lock on state, and guide plate state. The first type of sensor signal and the second type of sensor signal come from sensors installed in different types or different positions.

[0027] In some embodiments, in step S2, the time alignment process includes selecting a unified time reference period based on the sampling period of various types of sensors, and interpolating and resampling the signals of various types of sensors. In the time alignment process, for sensor signals with delayed, lost, or jittered sampling data, a sliding time window mechanism and a weighted average strategy are used to estimate missing values. After time alignment, the constructed multi-source state signal set is optimized based on signal fluctuation trends, correlation weights, or acquisition reliability indicators.

[0028] In some embodiments, in step S3, the consistency judgment includes comparing multiple sensor signals corresponding to the same key state according to a preset parallel logic rule, and determining that the key state is a valid state when any signal meets the expected state condition. Consistency judgment also includes verifying multiple sensor signals according to preset serial logic rules. When all signals meet the expected state conditions, the key state is determined to be a valid state.

[0029] In some embodiments, the logic judgment strategy adopted is dynamically adjusted based on the historical stability data and current fluctuation amplitude of each sensor. When a sensor is detected to be unstable for a long period of time or inconsistent with other signals, it is automatically switched to a redundant judgment mechanism based on multi-signal weighted voting. In the redundancy determination mechanism of multi-signal weighted voting, each sensor signal is assigned a different weight value according to its historical false alarm rate, current signal-to-noise ratio, and degree of deviation from other signals. A comprehensive score is calculated based on the weight of each signal and its corresponding state judgment result. When the comprehensive score exceeds the set threshold, the corresponding key state is determined to be a valid state.

[0030] In some embodiments, during the real-time status confirmation process in step S4, the redundancy determination results are continuously processed based on the streaming computing engine, the rate of status change is tracked within each acquisition time window, and the status is determined to be in an effective maintenance state in combination with the stability threshold of the current signal.

[0031] In some embodiments, in step S5, during the process of identifying abnormal sensor modes, by analyzing the frequency of sensor signal jumps, duration of deviation, and inconsistency ratio with other sensor results in historical state data, abnormal signals that deviate from the normal mode for a long time are identified and marked as low-confidence sources, reducing their participation weight in redundancy judgment or temporarily removing them from the judgment. In the process of updating the redundancy judgment strategy, the logical judgment parameters of the corresponding key states are dynamically adjusted based on the anomaly identification results and the recent sensor performance. This includes switching the redundant logical structure, updating the signal combination method for participating in voting, and adjusting the state judgment threshold.

[0032] In some embodiments, this application provides a spreader condition monitoring system, the system comprising: The signal acquisition module is used to acquire the first type of sensor signals and the second type of sensor signals corresponding to the preset key states of the spreader; In this embodiment, the signal acquisition module is deployed at key parts of the lifting device to collect multiple types of sensor signals corresponding to preset key states. The first type of sensor signal may come from conventional sensors such as proximity switches and encoders, while the second type of sensor signal is provided by magnetic scales, photoelectric sensors, or other auxiliary sensors. The acquisition process employs real-time data transmission technology to ensure the timeliness and integrity of the signals, providing reliable raw data for subsequent data processing and analysis.

[0033] The signal processing module is used to perform time alignment processing on the first type of sensor signals and the second type of sensor signals to construct a multi-source state signal set; The signal processing module performs time alignment processing on Type I and Type II sensor signals with different sampling frequencies and timestamps. This process includes unifying the time base of data from various sensors and performing interpolation, resampling, or smoothing on the sampling points. To address data delays or missing data, compensation methods such as sliding time windows and weighted averaging are employed to ensure data continuity and consistency. The resulting multi-source state signal set not only includes synchronous time series signals but is also filtered and sorted based on signal fluctuation trends and reliability indicators, improving data quality.

[0034] The determination module is used to perform consistency determination on the multi-source state signal set based on parallel logic or series logic, and obtain the redundancy determination result for each key state. The decision-making module employs a multi-sensor signal consistency judgment algorithm, performing redundant judgments based on preset parallel and series logic rules. Parallel logic stipulates that a state is considered valid as long as any sensor signal meets the expected state condition; series logic requires all sensor signals to simultaneously meet the condition for confirmation. This module dynamically selects the judgment logic, flexibly responding to changes in sensor states and ensuring the accuracy and robustness of critical state determinations.

[0035] The status confirmation module is used to input the redundancy judgment results into the streaming computing engine, and combine them with the status change rate, signal stability threshold and acquisition time window to realize real-time status confirmation. The status confirmation module, based on streaming computing technology, continuously receives redundant judgment results from the judgment module. By setting a reasonable status change rate monitoring mechanism and signal stability threshold, combined with a specific time acquisition window, it determines in real time whether the spreader is in an effective holding state. For short-term signal fluctuations or anomalies, the module can maintain the current status judgment to prevent malfunctions caused by momentary interference and ensure the overall stability of the system.

[0036] The anomaly identification and strategy adjustment module is used to identify sensor anomaly patterns based on historical state data, update redundant judgment strategies, and adjust the corresponding state judgment logic parameters. The anomaly identification and strategy adjustment module relies on historical state data analysis, utilizing signal transition frequency, persistent deviation, and inter-sensor consistency comparison to identify potential abnormal sensor signals. The system marks abnormal signals as low confidence and dynamically adjusts redundant judgment strategies based on the identification results. This includes changing the judgment logic structure, adjusting the sensor combinations involved in the judgment, and optimizing the state judgment threshold, ensuring a high judgment accuracy even when sensor performance degrades or malfunctions.

[0037] The prediction module is used to predict the potential failure trends of sensors in the future time period and generate pre-maintenance suggestions based on the trained state prediction model. The prediction module uses historical sensor status data and advanced algorithms such as machine learning to train a status prediction model. This model can predict potential sensor failure trends over a future period and issue timely pre-maintenance recommendations. The prediction results help maintenance personnel to rationally plan maintenance schedules, identify potential risks in advance, thereby reducing equipment failure rates and improving the reliability and safety of the spreader monitoring system.

[0038] In some embodiments, this application provides a terminal, including: Memory, used to store the spreader status monitoring program; A processor is used to execute the steps of the spreader condition monitoring method when the spreader condition monitoring system is used.

[0039] In some embodiments, this application provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the lifting device status monitoring method.

[0040] It is understood that the systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can be a personal computer, a laptop computer, a personal digital assistant, a tablet computer, a wearable device, or any combination of these devices.

[0041] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0042] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0043] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0044] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0045] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of this specification, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "in response to a determination," or "when," or "in the event of a determination."

[0046] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the protection scope of one or more embodiments of this specification.

Claims

1. A method for monitoring the condition of a lifting device, characterized in that, Includes the following steps: Step S1: Obtain the first type of sensor signal and the second type of sensor signal corresponding to the preset key state of the spreader; Step S2: Perform time alignment processing on the first type of sensor signals and the second type of sensor signals to construct a multi-source state signal set; Step S3: Perform consistency judgment on the multi-source state signal set based on parallel or series logic to obtain the redundancy judgment result of each key state. Step S4: Input the redundancy determination result into the streaming computing engine, and combine it with the state change rate, signal stability threshold and acquisition time window to perform real-time state confirmation. Step S5: Based on historical state data, identify abnormal sensor patterns, update redundant judgment strategies, and adjust the judgment logic parameters for the corresponding states. Step S6: Using the trained state prediction model, predict the potential failure trends of the sensor in the future time period and generate pre-maintenance suggestions.

2. The method for monitoring the status of lifting equipment according to claim 1, characterized in that, In step S1, the key states include single box extension state, pivot pin opening and closing state, hook allowable position state, hook hook detachment state, center lock on state, and guide plate state. The first type of sensor signal and the second type of sensor signal come from sensors installed in different types or different positions.

3. The method for monitoring the status of lifting equipment according to claim 1, characterized in that, In step S2, the time alignment process includes selecting a unified time reference period based on the sampling periods of various sensors, and interpolating and resampling the signals of various sensors. In the time alignment process, for sensor signals with delayed, lost, or jittered sampling data, a sliding time window mechanism and a weighted average strategy are used to estimate missing values. After time alignment, the constructed multi-source state signal set is optimized based on signal fluctuation trends, correlation weights, or acquisition reliability indicators.

4. The method for monitoring the status of lifting equipment according to claim 1, characterized in that, In step S3, the consistency judgment includes comparing multiple sensor signals corresponding to the same critical state according to a preset parallel logic rule. When any signal meets the expected state condition, the critical state is determined to be a valid state. Consistency judgment also includes verifying multiple sensor signals according to preset serial logic rules. When all signals meet the expected state conditions, the key state is determined to be a valid state.

5. The method for monitoring the status of lifting equipment according to claim 4, characterized in that, Based on the historical stability data and current fluctuation range of each sensor, the logic judgment strategy adopted is dynamically adjusted. When a sensor is detected to be unstable for a long time or inconsistent with other signals, it automatically switches to a redundant judgment mechanism based on multi-signal weighted voting. In the redundancy determination mechanism of multi-signal weighted voting, each sensor signal is assigned a different weight value according to its historical false alarm rate, current signal-to-noise ratio, and degree of deviation from other signals. A comprehensive score is calculated based on the weight of each signal and its corresponding state judgment result. When the comprehensive score exceeds the set threshold, the corresponding key state is determined to be a valid state.

6. The method for monitoring the status of lifting equipment according to claim 1, characterized in that, In step S4, during the real-time status confirmation process, the redundancy determination results are continuously processed based on the streaming computing engine. The rate of change of status is tracked within each acquisition time window, and the status is determined to be in an effective maintenance state in combination with the stability threshold of the current signal.

7. The method for monitoring the status of lifting equipment according to claim 6, characterized in that, In step S5, during the process of identifying abnormal sensor modes, by analyzing the frequency of sensor signal jumps, duration of deviation, and inconsistency ratio with other sensor results in historical state data, abnormal signals that deviate from the normal mode for a long time are identified and marked as low-confidence sources, reducing their participation weight in redundancy judgment or temporarily removing them from the judgment. In the process of updating the redundancy judgment strategy, the logical judgment parameters of the corresponding key states are dynamically adjusted based on the anomaly identification results and the recent sensor performance. This includes switching the redundant logical structure, updating the signal combination method for participating in voting, and adjusting the state judgment threshold.

8. A lifting gear status monitoring system, characterized in that, The system includes: The signal acquisition module is used to acquire the first type of sensor signals and the second type of sensor signals corresponding to the preset key states of the spreader; The signal processing module is used to perform time alignment processing on the first type of sensor signals and the second type of sensor signals to construct a multi-source state signal set; The determination module is used to perform consistency determination on the multi-source state signal set based on parallel logic or series logic, and obtain the redundancy determination result for each key state. The status confirmation module is used to input the redundancy judgment results into the streaming computing engine, and combine them with the status change rate, signal stability threshold and acquisition time window to realize real-time status confirmation. The anomaly identification and strategy adjustment module is used to identify sensor anomaly patterns based on historical state data, update redundant judgment strategies, and adjust the corresponding state judgment logic parameters. The prediction module is used to predict the potential failure trends of sensors in the future time period and generate pre-maintenance suggestions based on the trained state prediction model.

9. A terminal, characterized in that, include: Memory, used to store the spreader status monitoring program; A processor is configured to implement the steps of the spreader condition monitoring method as described in claim 1 when executing the spreader condition monitoring system.

10. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions from the storage medium, the computer executes the lifting device status monitoring method as described in claim 1.

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