Intelligent sensing method and system for operation state of ship lift

Abstract

The invention discloses an intelligent sensing method and system for the operation state of a ship lift, and the method comprises the steps: taking each key part of the ship lift as a node, arranging a sensor, obtaining the sensor data of each key part, and mapping the sensor data into a corresponding state symbol; obtaining a state symbol of each node at each time in a certain time period, and forming a change track subgraph of the state symbol corresponding to each node; calculating the trajectory deviation degree between the change trajectory sub-graph of each node and the map template of the corresponding node when the ship lift operates healthily, and calculating an abnormal index in combination with the state symbol change distance of the current node between two continuous time points in the certain time period; and comparing the abnormal index with a preset early warning threshold value, and adopting a corresponding response strategy according to a comparison result.

Description

Technical Field

[0001] This invention belongs to the field of intelligent monitoring technology for ship lifts, and more specifically, relates to an intelligent sensing method and system for the operating status of ship lifts. Background Technology

[0002] A ship lift is a large engineering device used in water transport systems to help ships quickly and safely rise and fall between water levels, thus replacing traditional lock systems. Its main function is to lift or lower ships like an elevator when there are significant water level differences in the waterway, allowing them to pass smoothly through complex hydraulic environments such as dams, reservoirs, or mountainous terrain. A ship lift typically consists of a ship chamber (also called a ship support trough), a mechanical drive system, a control system, hydraulic structures, and auxiliary facilities. Its core component is a huge ship-carrying structure that achieves smooth vertical or inclined operation through various methods such as wire rope traction, rack and pinion, hydraulic drive, or counterweights. Compared to traditional locks, ship lifts have advantages such as faster dam passage, water conservation, and adaptability to large water level differences, making them particularly suitable for waterways with frequent navigation needs and significant head variations.

[0003] Ship lifts, as ultra-large electromechanical-hydraulic integrated equipment, typically have a load-bearing capacity of several thousand tons. If a malfunction occurs during operation, it may not only cause ships to capsize and equipment to be damaged, but may also trigger serious secondary disasters such as falls and dam failures, resulting in irreparable casualties and economic losses.

[0004] Therefore, there is an urgent need for a technical solution that can intelligently sense the operating status of the ship lift, thereby improving the safety of the ship lift. Summary of the Invention

[0005] To address the above technical problems, this invention proposes an intelligent sensing method for the operating status of a ship lift, comprising:

[0006] Sensors are installed at each key part of the ship lift as nodes, and sensor data at each key part is acquired and mapped to corresponding status symbols.

[0007] Obtain the state symbol of each node at each time within a certain time period, and form a subgraph of the change trajectory of the state symbol of each node;

[0008] Calculate the deviation of the trajectory subgraph of each node from the trajectory template of the corresponding node when the ship lift is running healthily, and calculate the anomaly index by combining the distance of the state symbol change of the current node between two consecutive time points within the certain time period.

[0009] The abnormal index is compared with a preset warning threshold, and a corresponding response strategy is adopted according to the comparison result.

[0010] Furthermore, acquiring sensor data for each key component includes: performing time synchronization operations on the sensor data through a distributed clock synchronization protocol.

[0011] Furthermore, mapping the sensor data to corresponding state symbols includes: collecting sensor data for each node at fixed time steps;

[0012] Set a threshold for the change in variables between time steps in the sensor data, divide the change in variables of consecutive time steps according to the threshold, and generate state identifiers.

[0013] Furthermore, the calculation of the anomaly index includes:

[0014]

[0015] Where Ψ(t) is the anomaly index at time t, N is the number of nodes, and w i The weight of the distance of the state symbol change of the i-th node. Let σ be the state symbol of the i-th node at time t. i (t) and the state symbol σ of the i-th node at time t-1. i The state symbol change distance is (t-1), where λ is the weight of the edit distance. This represents the overall trajectory deviation.

[0016] Furthermore, calculate the overall trajectory deviation. include:

[0017]

[0018] in, Let be the trajectory deviation of the i-th node.

[0019] Furthermore, the deviation of the trajectory subgraph of each node from the trajectory template of the corresponding node during the healthy operation of the ship lift is calculated. include:

[0020]

[0021] Among them, P i For the subgraph of the change trajectory of the i-th node, T i This is a graph template for the ship lift corresponding to the i-th node during healthy operation.

[0022] Furthermore, comparing the abnormal index with preset warning thresholds and taking corresponding response strategies based on the comparison results includes: setting a first warning threshold, a second warning threshold, and a third warning threshold; if the abnormal index is less than the first warning threshold, the ship lift is operating normally and no warning is needed; if the abnormal index is greater than or equal to the first warning threshold and less than the second warning threshold, a level one warning is issued, a warning message is sent, and the ship lift's lifting speed is reduced; if the abnormal index is greater than or equal to the second warning threshold and less than or equal to the third warning threshold, a level two warning is issued, a warning message is sent, the ship lift's lifting speed is immediately stopped, and manual control is initiated.

[0023] This invention also proposes an intelligent sensing system for the operating status of a ship lift, comprising:

[0024] A status symbol generation module is used to set up sensors at each key part of the ship lift as nodes, acquire sensor data at each key part, and map the sensor data into corresponding status symbols.

[0025] The module for generating change trajectory subgraphs is used to obtain the state symbol of each node at each time within a certain time period, and form a change trajectory subgraph of the state symbol corresponding to each node.

[0026] The anomaly index calculation module is used to calculate the deviation of the trajectory subgraph of each node from the trajectory template of the corresponding node when the ship lift is running healthily, and to calculate the anomaly index by combining the distance of the state symbol change of the current node between two consecutive time points within the certain time period.

[0027] The early warning module is used to compare the abnormal index with a preset early warning threshold and take corresponding response strategies according to the comparison results.

[0028] Furthermore, acquiring sensor data for each key component includes: performing time synchronization operations on the sensor data through a distributed clock synchronization protocol.

[0029] Furthermore, mapping the sensor data to corresponding state symbols includes: collecting sensor data for each node at fixed time steps;

[0030] Set a threshold for the change in variables between time steps in the sensor data, divide the change in variables of consecutive time steps according to the threshold, and generate state identifiers.

[0031] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0032] This invention compresses multidimensional sensing streams (temperature rise, acceleration, voltage, etc.) into symbol streams (such as ↑, ↓, ...). -) This invention avoids uploading large amounts of raw data, significantly improves the efficiency of edge computing and remote communication, and preserves the essential characteristics of events. In addition, by calculating anomaly indices, this invention can accurately determine whether a ship lift has malfunctioned, and then issue warnings according to a multi-level early warning mechanism and take corresponding measures, thereby greatly improving the safety of the ship lift. Attached Figure Description

[0033] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention;

[0034] Figure 2 This is a system structure diagram of Embodiment 2 of the present invention. Detailed Implementation

[0035] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0036] The method provided by this invention can be implemented in a terminal environment that may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0037] A processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the terminal, and performs various functions and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and by calling data stored in the storage medium.

[0038] Storage media can include random access memory (RAM) or read-only memory (ROM). Storage media can be used to store instructions, programs, code, code sets, or instructions.

[0039] The display screen is used to show the user interface of each application.

[0040] In addition, those skilled in the art will understand that the structure of the terminal described above does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include radio frequency circuits, input units, sensors, audio circuits, power supplies, and other components, which will not be described in detail here.

[0041] Example 1

[0042] like Figure 1As shown, this embodiment proposes an intelligent sensing method for the operating status of a ship lift, including:

[0043] Step 101: Set up sensors at each key part of the ship lift as nodes, acquire sensor data at each key part, and map the sensor data into corresponding status symbols.

[0044] Preferably, key components may include hoisting system, motor drive module, ship-bearing structure, track system, braking system, pulley block, wire rope anchoring area, etc. The sensing content (sensor data) obtained by the set sensors may include: spatial displacement or spatial displacement velocity (laser ranging + IMU), vibration (MEMS acceleration), current / voltage disturbance (Hall sensor), temperature rise or humidity (environmental monitoring), etc.

[0045] Specifically, acquiring sensor data for each key component includes: performing time synchronization operations on the sensor data using a distributed clock synchronization protocol (such as IEEE 1588).

[0046] Specifically, mapping the sensor data to corresponding state symbols includes: collecting sensor data for each node at fixed time steps;

[0047] Set a threshold for the change in variables between time steps in the sensor data, divide the change in variables of consecutive time steps according to the threshold, and generate state identifiers.

[0048] Preferably, this embodiment explains the state identifier through the following examples:

[0049] For example, a temperature sensor is installed on the motor drive module to monitor its temperature. Temperature data is collected every 1 second, for a total of 5 temperature data points (T′1, T′2, T′3, T′4, T′5). If T′2 - T′1 is positive and greater than or equal to the change threshold 1, the status identifier is ↑, indicating a temperature increase. If T′3 - T′2 is negative and less than or equal to the change threshold 2, the status identifier is ↓, indicating a temperature decrease. If T′4 - T′3 is positive and less than the change threshold 1, or T′4 - T′3 is negative and greater than the change threshold 2, the status identifier is —, indicating no temperature change. If T′5 - T′4 is positive and greater than or equal to the change threshold 3 (change threshold 3 is greater than change threshold 1), or T′5 - T′4 is negative and less than or equal to the change threshold 4 (change threshold 4 is less than change threshold 2), the status identifier is... This indicates an abnormal temperature in the motor drive module, ultimately forming a status identifier sequence [↑, ↓, —, ].

[0050] Step 102: Obtain the state symbol of each node at each time within a certain time period, and form a sub-graph of the change trajectory of the state symbol corresponding to each node;

[0051] Step 103: Calculate the trajectory deviation of each node's change trajectory subgraph from the corresponding node's atlas template during the healthy operation of the ship lift, and combine the distance of the current node's state symbol change between two consecutive time points within the specified time period to calculate the anomaly index.

[0052] Specifically, the calculation of the anomaly index includes:

[0053]

[0054] Where Ψ(t) is the anomaly index at time t, N is the number of nodes, and w i The weight of the distance of the state symbol change of the i-th node. Let σ be the state symbol of the i-th node at time t. i (t) and the state symbol σ of the i-th node at time t-1. i The state symbol change distance is (t-1), where λ is the weight of the edit distance. This represents the overall trajectory deviation.

[0055] Specifically, calculate the overall trajectory deviation. include:

[0056]

[0057] in, Let be the trajectory deviation of the i-th node.

[0058] Specifically, the deviation between the trajectory subgraph of each node and the trajectory template of the corresponding node during the healthy operation of the ship lift is calculated. include:

[0059]

[0060] Among them, P i For the subgraph of the change trajectory of the i-th node, T i This is a graph template for the ship lift corresponding to the i-th node during healthy operation.

[0061] Preferably, this embodiment provides the following example of obtaining the graph template of the corresponding node during the healthy operation of the ship lift:

[0062] When no abnormal events occur in the ship lift, sensor data from each node is collected during multiple consecutive lifts and lowerings of the ship lift, and mapped to corresponding status symbols. For example, a temperature sensor is set on the motor drive module to monitor the temperature of the motor drive module, and the temperature data of the motor drive module is collected once every 1 second, for a total of 5 temperature data points, namely (T″1, T″2, T″3, T″4, T″5). The status identifier sequence [↑,↓,↑,↓] when no abnormal events occur in the ship lift is obtained. This status identifier sequence is the map template of the corresponding node when the ship lift is running healthily.

[0063] Step 104: Compare the abnormal index with the preset warning threshold, and take corresponding response strategies according to the comparison results.

[0064] Specifically, comparing the abnormal index with a preset warning threshold and taking corresponding response strategies based on the comparison results includes: setting a first warning threshold, a second warning threshold, and a third warning threshold; if the abnormal index is less than the first warning threshold, the ship lift is operating normally and no warning is needed; if the abnormal index is greater than or equal to the first warning threshold and less than the second warning threshold, a level one warning is issued, a warning message is sent, and the ship lift's lifting speed is reduced; if the abnormal index is greater than or equal to the second warning threshold and less than or equal to the third warning threshold, a level two warning is issued, a warning message is sent, the ship lift's lifting speed is immediately stopped, and manual control is initiated.

[0065] Example 2

[0066] like Figure 2 As shown, this embodiment proposes an intelligent sensing system for the operating status of a ship lift, including:

[0067] A status symbol generation module is used to set up sensors at each key part of the ship lift as nodes, acquire sensor data at each key part, and map the sensor data into corresponding status symbols.

[0068] Specifically, acquiring sensor data for each key component includes: performing time synchronization operations on the sensor data through a distributed clock synchronization protocol.

[0069] Specifically, mapping the sensor data to corresponding state symbols includes: collecting sensor data for each node at fixed time steps;

[0070] Set a threshold for the change in variables between time steps in the sensor data, divide the change in variables of consecutive time steps according to the threshold, and generate state identifiers.

[0071] The module for generating change trajectory subgraphs is used to obtain the state symbol of each node at each time within a certain time period, and form a change trajectory subgraph of the state symbol corresponding to each node.

[0072] The anomaly index calculation module is used to calculate the deviation of the trajectory subgraph of each node from the trajectory template of the corresponding node when the ship lift is running healthily, and to calculate the anomaly index by combining the distance of the state symbol change of the current node between two consecutive time points within the certain time period.

[0073] Specifically, the calculation of the anomaly index includes:

[0074]

[0075] Where Ψ(t) is the anomaly index at time t, N is the number of nodes, and w i The weight of the distance of the state symbol change of the i-th node. Let σ be the state symbol of the i-th node at time t. i (t) and the state symbol σ of the i-th node at time t-1. i The state symbol change distance is (t-1), where λ is the weight of the edit distance. This represents the overall trajectory deviation.

[0076] Specifically, calculate the overall trajectory deviation. include:

[0077]

[0078] in, Let be the trajectory deviation of the i-th node.

[0079] Specifically, the deviation between the trajectory subgraph of each node and the trajectory template of the corresponding node during the healthy operation of the ship lift is calculated. include:

[0080]

[0081] Among them, P i For the subgraph of the change trajectory of the i-th node, T i This is a graph template for the ship lift corresponding to the i-th node during healthy operation.

[0082] The early warning module is used to compare the abnormal index with a preset early warning threshold and take corresponding response strategies according to the comparison results.

[0083] Specifically, comparing the abnormal index with a preset warning threshold and taking corresponding response strategies based on the comparison results includes: setting a first warning threshold, a second warning threshold, and a third warning threshold; if the abnormal index is less than the first warning threshold, the ship lift is operating normally and no warning is needed; if the abnormal index is greater than or equal to the first warning threshold and less than the second warning threshold, a level one warning is issued, a warning message is sent, and the ship lift's lifting speed is reduced; if the abnormal index is greater than or equal to the second warning threshold and less than or equal to the third warning threshold, a level two warning is issued, a warning message is sent, the ship lift's lifting speed is immediately stopped, and manual control is initiated.

[0084] Example 3

[0085] This invention also proposes a storage medium storing multiple instructions for implementing the aforementioned intelligent sensing method for the operating status of a ship lift.

[0086] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0087] Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: Step 101, setting up sensors as nodes at each key part of the ship lift, acquiring sensor data at each key part, and mapping the sensor data to corresponding state symbols;

[0088] Specifically, acquiring sensor data for each key component includes: performing time synchronization operations on the sensor data through a distributed clock synchronization protocol.

[0089] Specifically, mapping the sensor data to corresponding state symbols includes: collecting sensor data for each node at fixed time steps;

[0090] Set a threshold for the change in variables between time steps in the sensor data, divide the change in variables of consecutive time steps according to the threshold, and generate state identifiers.

[0091] Step 102: Obtain the state symbol of each node at each time within a certain time period, and form a sub-graph of the change trajectory of the state symbol corresponding to each node;

[0092] Step 103: Calculate the trajectory deviation of each node's change trajectory subgraph from the corresponding node's atlas template during the healthy operation of the ship lift, and combine the distance of the current node's state symbol change between two consecutive time points within the specified time period to calculate the anomaly index.

[0093] Specifically, the calculation of the anomaly index includes:

[0094]

[0095] Where Ψ(t) is the anomaly index at time t, N is the number of nodes, and w i The weight of the distance of the state symbol change of the i-th node. Let σ be the state symbol of the i-th node at time t. i (t) and the state symbol σ of the i-th node at time t-1. i The state symbol change distance is (t-1), where λ is the weight of the edit distance. This represents the overall trajectory deviation.

[0096] Specifically, calculate the overall trajectory deviation. include:

[0097]

[0098] in, Let be the trajectory deviation of the i-th node.

[0099] Specifically, the deviation between the trajectory subgraph of each node and the trajectory template of the corresponding node during the healthy operation of the ship lift is calculated. include:

[0100]

[0101] Among them, P i For the subgraph of the change trajectory of the i-th node, T i This is a graph template for the ship lift corresponding to the i-th node during healthy operation.

[0102] Step 104: Compare the abnormal index with the preset warning threshold, and take corresponding response strategies according to the comparison results.

[0103] Specifically, comparing the abnormal index with a preset warning threshold and taking corresponding response strategies based on the comparison results includes: setting a first warning threshold, a second warning threshold, and a third warning threshold; if the abnormal index is less than the first warning threshold, the ship lift is operating normally and no warning is needed; if the abnormal index is greater than or equal to the first warning threshold and less than the second warning threshold, a level one warning is issued, a warning message is sent, and the ship lift's lifting speed is reduced; if the abnormal index is greater than or equal to the second warning threshold and less than or equal to the third warning threshold, a level two warning is issued, a warning message is sent, the ship lift's lifting speed is immediately stopped, and manual control is initiated.

[0104] Example 4

[0105] This invention also proposes an electronic device, including a processor and a storage medium connected to the processor. The storage medium stores multiple instructions, which can be loaded and executed by the processor to enable the processor to execute the aforementioned intelligent sensing method for the operating status of a ship lift.

[0106] Specifically, the electronic device in this embodiment can be a computer terminal, which may include one or more processors and a storage medium.

[0107] The storage medium can be used to store software programs and modules, such as the intelligent sensing method for the operating status of a ship lift in this embodiment of the invention. The corresponding program instructions / modules are executed by the processor through running the software programs and modules stored in the storage medium, thereby performing various functional applications and data processing, thus realizing the aforementioned intelligent sensing method for the operating status of a ship lift. The storage medium may include high-speed random access storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely configured relative to the processor, which can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0108] The processor can call the information and application stored in the storage medium through the transmission system to perform the following steps: Step 101, set up sensors as nodes at each key part of the ship lift, acquire sensor data of each key part, and map the sensor data into corresponding status symbols;

[0109] Specifically, acquiring sensor data for each key component includes: performing time synchronization operations on the sensor data through a distributed clock synchronization protocol.

[0110] Specifically, mapping the sensor data to corresponding state symbols includes: collecting sensor data for each node at fixed time steps;

[0111] Set a threshold for the change in variables between time steps in the sensor data, divide the change in variables of consecutive time steps according to the threshold, and generate state identifiers.

[0112] Step 102: Obtain the state symbol of each node at each time within a certain time period, and form a sub-graph of the change trajectory of the state symbol corresponding to each node;

[0113] Step 103: Calculate the trajectory deviation of each node's change trajectory subgraph from the corresponding node's atlas template during the healthy operation of the ship lift, and combine the distance of the current node's state symbol change between two consecutive time points within the specified time period to calculate the anomaly index.

[0114] Specifically, the calculation of the anomaly index includes:

[0115]

[0116] Where Ψ(t) is the anomaly index at time t, N is the number of nodes, and w i The weight of the distance of the state symbol change of the i-th node. Let σ be the state symbol of the i-th node at time t. i (t) and the state symbol σ of the i-th node at time t-1. i The state symbol change distance is (t-1), where λ is the weight of the edit distance. This represents the overall trajectory deviation.

[0117] Specifically, calculate the overall trajectory deviation. include:

[0118]

[0119] in, Let be the trajectory deviation of the i-th node.

[0120] Specifically, the deviation between the trajectory subgraph of each node and the trajectory template of the corresponding node during the healthy operation of the ship lift is calculated. include:

[0121]

[0122] Among them, P i For the subgraph of the change trajectory of the i-th node, T i This is a graph template for the ship lift corresponding to the i-th node during healthy operation.

[0123] Step 104: Compare the abnormal index with the preset warning threshold, and take corresponding response strategies according to the comparison results.

[0124] Specifically, comparing the abnormal index with a preset warning threshold and taking corresponding response strategies based on the comparison results includes: setting a first warning threshold, a second warning threshold, and a third warning threshold; if the abnormal index is less than the first warning threshold, the ship lift is operating normally and no warning is needed; if the abnormal index is greater than or equal to the first warning threshold and less than the second warning threshold, a level one warning is issued, a warning message is sent, and the ship lift's lifting speed is reduced; if the abnormal index is greater than or equal to the second warning threshold and less than or equal to the third warning threshold, a level two warning is issued, a warning message is sent, the ship lift's lifting speed is immediately stopped, and manual control is initiated.

[0125] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0126] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0127] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The system embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between units or modules, and may be electrical or other forms.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0130] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, optical disks, and other media capable of storing program code.

[0131] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. An intelligent sensing method for the operating status of a ship lift, characterized in that, include: Sensors are installed at each key part of the ship lift as nodes, and sensor data at each key part is acquired and mapped to corresponding status symbols. Obtain the state symbol of each node at each time within a certain time period, and form a subgraph of the change trajectory of the state symbol of each node; Calculate the deviation of the trajectory subgraph of each node from the trajectory template of the corresponding node when the ship lift is running healthily, and calculate the anomaly index by combining the distance of the state symbol change of the current node between two consecutive time points within the certain time period; The abnormal index is compared with a preset warning threshold, and a corresponding response strategy is adopted according to the comparison result.

2. The intelligent sensing method for the operating status of a ship lift as described in claim 1, characterized in that, Acquiring sensor data for each key component includes: performing time synchronization operations on the sensor data through a distributed clock synchronization protocol.

3. The intelligent sensing method for the operating status of a ship lift as described in claim 1, characterized in that, Mapping the sensor data to corresponding state symbols includes: collecting sensor data for each node at fixed time steps; Set a threshold for the change in variables between time steps in the sensor data, divide the change in variables of consecutive time steps according to the threshold, and generate state identifiers.

4. The intelligent sensing method for the operating status of a ship lift as described in claim 1, characterized in that, The calculation of the anomaly index includes: Where Ψ(t) is the anomaly index at time t, N is the number of nodes, and w i The weight of the distance of the state symbol change of the i-th node. Let σ be the state symbol of the i-th node at time t. i (t) and the state symbol σ of the i-th node at time t-1. i The state symbol change distance is (t-1), where λ is the weight of the edit distance. This represents the overall trajectory deviation.

5. The intelligent sensing method for the operating status of a ship lift as described in claim 4, characterized in that, Calculate the overall trajectory deviation include: in, Let be the trajectory deviation of the i-th node.

6. The intelligent sensing method for the operating status of a ship lift as described in claim 5, characterized in that, Calculate the deviation of the trajectory subplot of each node from the trajectory template of the corresponding node during the healthy operation of the ship lift. include: Among them, P i For the subgraph of the change trajectory of the i-th node, T i This is a graph template for the ship lift corresponding to the i-th node during healthy operation.

7. The intelligent sensing method for the operating status of a ship lift as described in claim 1, characterized in that, The abnormal index is compared with a preset warning threshold, and corresponding response strategies are adopted according to the comparison results, including: setting a first warning threshold, a second warning threshold, and a third warning threshold; if the abnormal index is less than the first warning threshold, the ship lift is operating normally and no warning is required; if the abnormal index is greater than or equal to the first warning threshold and less than the second warning threshold, a level one warning is issued, a warning message is sent, and the lifting speed of the ship lift is reduced; if the abnormal index is greater than or equal to the second warning threshold and less than or equal to the third warning threshold, a level two warning is issued, a warning message is sent, the lifting of the ship lift is immediately stopped, and manual control is initiated.

8. An intelligent sensing system for the operating status of a ship lift, characterized in that, include: A status symbol generation module is used to set up sensors at each key part of the ship lift as nodes, acquire sensor data at each key part, and map the sensor data into corresponding status symbols. The module for generating change trajectory subgraphs is used to obtain the state symbol of each node at each time within a certain time period, and form a change trajectory subgraph of the state symbol corresponding to each node. The anomaly index calculation module is used to calculate the deviation of the trajectory subgraph of each node from the trajectory template of the corresponding node when the ship lift is running healthily, and to calculate the anomaly index by combining the distance of the state symbol change of the current node between two consecutive time points within the certain time period. The early warning module is used to compare the abnormal index with a preset early warning threshold and take corresponding response strategies according to the comparison results.

9. The intelligent sensing system for the operating status of a ship lift as described in claim 8, characterized in that, Acquiring sensor data for each key component includes: performing time synchronization operations on the sensor data through a distributed clock synchronization protocol.

10. The intelligent sensing system for the operating status of a ship lift as described in claim 8, characterized in that, Mapping the sensor data to corresponding state symbols includes: collecting sensor data for each node at fixed time steps; Set a threshold for the change in variables between time steps in the sensor data, divide the change in variables of consecutive time steps according to the threshold, and generate state identifiers.

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