A dual swing arm boom monitoring system and method

Abstract

This invention relates to the field of pole monitoring technology, specifically to a dual-rocker pole monitoring system and method. The system includes: a graph modeling module, used to generate a pole monitoring association graph corresponding to the target dual-rocker pole based on the data acquisition type and installation layout information of the sensor acquisition units on the target dual-rocker pole, as well as the linkage relationship between the sensor acquisition units and other sensor acquisition units; and an association modeling module, used to determine the operating condition identifier of the target dual-rocker pole based on the pole operation data obtained by the sensor acquisition units, and to determine all target acquisition nodes in the pole monitoring association graph based on the operating condition identifier. Through graph modeling, association modeling, and operating condition coding modules, this invention enables the system to accurately acquire the operating data of the dual-rocker pole, monitor its operating condition changes in real time, and generate corresponding operating condition sub-association graphs, ensuring a comprehensive understanding of the equipment's operating status.

Description

Technical Field

[0001] This invention relates to the field of pole monitoring technology, specifically to a dual-rocker arm pole monitoring system and method. Background Technology

[0002] As an important mechanical component, the double rocker arm boom is often used in cranes, bridge construction and other large-scale mechanical equipment. With the expansion of these applications and the increasing complexity of the working environment, traditional monitoring methods often cannot provide sufficiently accurate data, resulting in the equipment not being detected in time before failure occurs.

[0003] Currently, traditional systems can only provide relatively simple status monitoring and cannot track and capture detailed changes in the operating conditions of equipment in real time. They usually rely on a small number of sensors and basic data and cannot generate accurate sub-correlation maps of operating conditions. Therefore, it is difficult to fully understand the actual status of the equipment. Moreover, the anomaly detection and localization of traditional systems generally rely on static threshold settings and cannot dynamically adapt to the complex changes in equipment operation. Due to the lack of intelligent clustering and node analysis, many potential abnormal areas may not be identified in time before they occur, resulting in no effective warning before the equipment fails, thus increasing the risk of failure.

[0004] Furthermore, traditional systems typically lack the ability to analyze the structural load-bearing capacity and mechanical transmission of equipment, making it difficult to predict potential structural risks. They fail to combine the analysis with the vector of changes in operating conditions and the deviation of the rate, resulting in the system's inability to detect and predict potential structural problems in real time. Moreover, they generally cannot classify and process changes in operating conditions at different stages, which makes the monitoring and data analysis of operating conditions inaccurate. Traditional systems often cannot effectively distinguish between different stages of operating conditions, resulting in an incomplete assessment of the health status of equipment. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution: a dual-rocker arm pole monitoring system, comprising: The map modeling module is used to generate a pole monitoring association map corresponding to the target double rocker arm pole based on the data acquisition type and installation information of the sensor acquisition unit of the target double rocker arm pole and the linkage relationship between the sensor acquisition unit and other sensor acquisition units. The association modeling module is used to acquire pole operation data from the sensing acquisition unit, determine the working condition identifier of the target double-rocker pole based on the pole operation data, determine all target acquisition nodes in the pole monitoring association map based on the working condition identifier, and determine the working condition sub-association map corresponding to the target double-rocker pole based on the node linkage relationship between the target acquisition nodes and other target acquisition nodes. The working condition coding module is used to determine the working condition transmission path that matches the working condition sub-correlation map from the pole monitoring correlation map; and to determine the working condition change vector of each target acquisition node based on the pole operation data of each target acquisition node on the working condition transmission path. The orientation determination module is used to determine the standard working condition orientation of each target acquisition node based on the structural bearing direction and mechanical transmission direction of each part of the double rocker arm mast, and to determine the orientation deviation value of each target acquisition node based on the difference between the orientation of the working condition change vector of each target acquisition node and the standard working condition orientation. The node classification module is used to classify target acquisition nodes into three categories based on the magnitude of the working condition change vector of each target acquisition node, corresponding to the lifting preparation stage, the heavy load transmission stage, and the unloading and static placement stage, respectively.

[0006] Preferably, the system further includes: The deviation determination module is used to determine the rate deviation value of each target acquisition node in each category based on the magnitude of the working condition change vector of each target acquisition node in each category, and to determine the working condition deviation degree of each target acquisition node in combination with the direction deviation value of each target acquisition node. The anomaly filtering module is used to cluster each target acquisition node according to the deviation of its operating conditions and the installation and deployment information of each target acquisition node, to obtain multiple node clusters, and to filter out abnormal node clusters according to the number of target acquisition nodes in the node cluster and the magnitude of the deviation of the operating conditions of all target acquisition nodes in the cluster. The anomaly localization module is used to determine the structural anomaly area of ​​the target double rocker arm mast based on the installation location of the sensor acquisition unit corresponding to the anomaly node cluster.

[0007] Preferably, the sensing and acquisition unit includes a stress sensor, a deformation sensor, a load sensor, a tilt sensor, and a vibration sensor; the pole monitoring correlation map corresponding to the target double rocker arm pole includes stress acquisition nodes, deformation acquisition nodes, load acquisition nodes, tilt acquisition nodes, and vibration acquisition nodes; Determining the working condition transmission path from the pole monitoring correlation map to match the working condition sub-correlation map includes: Based on the stress acquisition nodes, deformation acquisition nodes, load acquisition nodes, tilt angle acquisition nodes, and vibration acquisition nodes on the pole monitoring correlation map, the corresponding stress transmission paths, deformation transmission paths, load transmission paths, tilt angle transmission paths, and vibration transmission paths are obtained respectively. Based on the time series consistency and data correlation of all transmission paths, the working condition transmission path of the target double rocker arm pole is determined.

[0008] Preferably, based on the pole-mounted operation data of each target acquisition node along the operating condition transmission path, the operating condition change vector of each target acquisition node is determined, including: Take any target acquisition node as the core acquisition node, and trace the parameters along multiple pre-set uniformly distributed directions with the core acquisition node as the center. Obtain the boundary acquisition nodes whose pole operation data is continuously less than or equal to the pole operation data of the previous acquisition node. Calculate the deployment distance from the boundary acquisition node to the core acquisition node, obtain the parameter difference between the core acquisition node and the boundary acquisition node, and use the ratio of the parameter difference to the deployment distance as the modulus to construct the parameter change vector in each direction. The sum of the parameter change vectors in each direction of the core acquisition node is used as the operating condition change vector of the core acquisition node.

[0009] Preferably, the standard operating direction of each target acquisition node is determined based on the structural bearing direction and mechanical transmission direction of each part of the double rocker arm mast, including: Based on the structural design document of the double rocker arm pole, the structural bearing direction and mechanical transmission direction of each part are set. The structural design document is mapped to the pole monitoring correlation map to obtain the structural bearing direction and mechanical transmission direction of each target acquisition node. For each target acquisition node, the unit vector corresponding to the structural bearing direction of the node location and the unit vector corresponding to the mechanical transmission direction are vector superimposed to obtain a composite vector, and the direction of the composite vector is used as the standard working condition direction of the target acquisition node.

[0010] Preferably, the target acquisition nodes are divided into three categories based on the magnitude of the working condition change vector of each target acquisition node, corresponding to the lifting preparation stage, the heavy load transfer stage, and the unloading and static placement stage, respectively, including: Traverse the pole monitoring association map, and form a sequence of moduli of the working condition change vectors of the target acquisition nodes according to the traversal order. Divide the moduli sequence into three subsequences, and classify the target acquisition nodes corresponding to each element in each subsequence into the same category.

[0011] Preferably, the rate deviation value of each target acquisition node in each category is determined based on the magnitude of the working condition change vector of each target acquisition node in each category, and the working condition deviation degree of each target acquisition node is determined by combining the direction deviation value of each target acquisition node, including: In response to the target acquisition node belonging to the second category, the magnitude of the working condition change vector of the target acquisition node is normalized to obtain the rate deviation value of the target acquisition node. In response to the target acquisition node belonging to the first or third category, a target acquisition node belonging to the same category as the target acquisition node and having the same standard operating condition direction is used as a reference acquisition node; Calculate the difference between the magnitudes of the operating condition change vectors of the target acquisition node and all its reference acquisition nodes, and perform a weighted summation of the differences to obtain the rate deviation value of the target acquisition node; wherein, when performing the weighted summation, the weights are negatively correlated with the directional deviation values ​​of the reference acquisition nodes; The operating condition deviation of the target acquisition node is obtained by weighted summing of the directional deviation value and the rate deviation value.

[0012] Preferably, based on the operating condition deviation of each target acquisition node and the installation and deployment information of each target acquisition node, the target acquisition nodes are clustered to obtain multiple node clusters, including: The deployment coordinates and operating condition deviations of each target acquisition node in the pole monitoring correlation map are used to form the feature vector of each target acquisition node. Based on the feature vectors, the target acquisition nodes are clustered to obtain multiple node clusters.

[0013] Preferably, abnormal node clusters are filtered based on the number of target acquisition nodes in the node cluster and the degree of deviation of the operating conditions of all target acquisition nodes in the cluster, including: If the number of target acquisition nodes in the node cluster is greater than a preset first threshold, and the average deviation of the operating conditions of all target acquisition nodes in the cluster is greater than a preset second threshold, the node cluster is designated as an abnormal node cluster.

[0014] Preferably, after determining the structural anomaly area of ​​the target double-rocker arm mast based on the installation location of the sensor acquisition unit corresponding to the abnormal node cluster, the method further includes: Based on the working condition transmission path and structural anomaly area of ​​all target acquisition nodes, determine all potential risk nodes corresponding to the target double rocker arm pole; the potential risk node is the pole part corresponding to the acquisition node in the pole monitoring correlation map that is adjacent to the structural anomaly area and is subject to the preset anomaly influence range; For each potential risk node, the risk diffusion trajectory of the target double rocker arm boom is determined based on the working condition transmission path and the working condition deviation of the node. Based on the risk diffusion trajectory and boom operation data, the safety warning parameters corresponding to the potential risk node are determined.

[0015] To achieve the above objectives, the present invention also provides the following technical solution: a dual-rocker arm pole monitoring method, applicable to a dual-rocker arm pole monitoring system, comprising: Based on the data acquisition type and installation information of the sensor acquisition unit of the target double rocker arm pole, as well as the linkage relationship between the sensor acquisition unit and other sensor acquisition units, a pole monitoring association map corresponding to the target double rocker arm pole is generated. The operating data of the target double-rocker arm pole is acquired by the sensor acquisition unit, and the working condition identifier of the target double-rocker arm pole is determined based on the operating data. All target acquisition nodes in the pole monitoring association map are determined based on the working condition identifier. The working condition sub-association map corresponding to the target double-rocker arm pole is determined based on the node linkage relationship between the target acquisition nodes and other target acquisition nodes. The working condition transmission path matching the working condition sub-correlation map is determined from the pole monitoring correlation map; based on the pole operation data of each target acquisition node on the working condition transmission path, the working condition change vector of each target acquisition node is determined; The standard working condition direction of each target acquisition node is determined based on the structural bearing direction and mechanical transmission direction of each part of the double rocker arm mast. The directional deviation value of each target acquisition node is determined based on the difference between the direction of the working condition change vector of each target acquisition node and the standard working condition direction. Based on the magnitude of the working condition change vector of each target acquisition node, the target acquisition nodes are divided into three categories, corresponding to the lifting preparation stage, the heavy load transmission stage, and the unloading and static placement stage, respectively.

[0016] Compared with the prior art, the beneficial effects of the present invention are: Through modules such as graph modeling, correlation modeling, and working condition coding, this invention enables the system to accurately acquire the operating data of the double rocker arm boom, monitor its working condition changes in real time, and generate corresponding working condition sub-correlation graphs to ensure a comprehensive understanding of the equipment's operating status. Moreover, the system can intelligently cluster target acquisition nodes based on the working condition deviation and the installation and deployment information of the sensor acquisition units, and filter out abnormal node clusters. This allows for the timely detection of potential abnormal areas of the equipment, avoiding equipment damage or safety accidents caused by faults. This invention analyzes the working condition change vectors and their magnitudes of each target acquisition node, and combines them with structural bearing capacity and mechanical transmission direction. The system can determine the standard working condition direction of the equipment and compare it with the actual changes, calculate the deviation, and further predict potential structural risks. Moreover, through the monitored change vectors and rate deviation values, as well as node cluster analysis, the system can dynamically identify potential risk nodes and predict the risk diffusion trajectory, thereby issuing early safety warnings and helping to carry out effective risk management. This invention classifies target acquisition nodes according to the magnitude of the working condition change vector, enabling the system to process different stages of the working condition separately. This makes monitoring and data analysis more accurate and efficient. Furthermore, through real-time monitoring of the working condition, anomaly detection, and early warning, the probability of equipment failure can be significantly reduced, while also reducing equipment downtime for maintenance, lowering operating costs, and improving the overall operating efficiency and safety of the equipment. Attached Figure Description

[0017] Figure 1This is a schematic diagram of the overall system architecture in one embodiment of the present invention; Figure 2 This is a schematic flowchart of the overall method in one embodiment of the present invention.

[0018] In the diagram: 1. Graph modeling module; 2. Association modeling module; 3. Working condition coding module; 4. Direction determination module; 5. Node classification module; 6. Deviation determination module; 7. Anomaly screening module; 8. Anomaly location module. Detailed Implementation

[0019] 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.

[0020] Example 1, please refer to Figure 1 This invention provides a technical solution: a dual-rocker arm pole monitoring system, comprising: The map modeling module 1 is used to generate a monitoring association map of the target double rocker arm pole based on the data acquisition type and installation information of the sensor acquisition unit of the target double rocker arm pole and the linkage relationship between the sensor acquisition unit and other sensor acquisition units. The association modeling module 2 is used to acquire the pole operation data from the sensor acquisition unit, determine the working condition identifier of the target double rocker arm pole based on the pole operation data, determine all target acquisition nodes in the pole monitoring association map based on the working condition identifier, and determine the working condition sub-association map corresponding to the target double rocker arm pole based on the node linkage relationship between the target acquisition nodes and other target acquisition nodes. The working condition coding module 3 is used to determine the working condition transmission path that matches the working condition sub-correlation map from the pole monitoring correlation map; and to determine the working condition change vector of each target acquisition node based on the pole operation data of each target acquisition node on the working condition transmission path. The direction determination module 4 is used to determine the standard working condition direction of each target acquisition node based on the structural bearing direction and mechanical transmission direction of each part of the double rocker arm mast, and to determine the direction deviation value of each target acquisition node based on the difference between the direction of the working condition change vector of each target acquisition node and the standard working condition direction. The node classification module 5 is used to classify the target acquisition nodes into three categories based on the magnitude of the working condition change vector of each target acquisition node, corresponding to the lifting preparation stage, the heavy load transmission stage, and the unloading and static placement stage, respectively.

[0021] It should be noted that, firstly, the system generates a monitoring correlation map based on information such as the data acquisition type and installation location of the sensor acquisition units on the target double-arm boom. This map shows the linkage relationship between the various sensor acquisition units. For example, if sensors are installed at different parts of the boom, the system will know which sensors have data correlations (e.g., a change in one sensor will affect the reading of another sensor). For example, a sensor installed at the base of the boom may affect the sensor data on the upper boom because the two have a mutual transmission relationship when subjected to force. Based on the operational data provided by the sensors, the system determines the current working condition of the target double-arm boom (e.g., whether it is in the lifting preparation stage, the heavy load stage, or the unloading and static stage). At this time, the system analyzes the collected data and determines the working condition identifier through a certain algorithm. For example, when the sensor data indicates that the boom is bearing a gravity load and is moving smoothly, the system will determine that it is currently in the "heavy load transmission stage". Based on the operating condition indicators, the system will identify all relevant target acquisition nodes. These nodes refer to the specific locations (i.e., sensor installation points) that the system needs to focus on, and they are closely related to the overall monitoring objectives of the system. For example, assuming that the sensors are installed in multiple locations on the rocker arm, the system will select the parts that need to be monitored based on the current working status. For example, during the heavy load transmission phase, it may be necessary to pay more attention to the location with the strongest load. The system generates a working condition sub-association map based on the linkage relationship between the target acquisition nodes. This map shows the specific response of each node under the change of working conditions. For example, the sensor at the root may detect the load change before the sensor on the upper boom. The system will generate an association map to show the time difference and influence relationship between the two. The system identifies paths from the monitoring correlation map that match the current working condition sub-correlation map, which are used to analyze the propagation of the working condition between different nodes. Through these paths, the force transmission of mechanical components can be understood more clearly. For example, if the working condition transmission path is from the root to the boom and then to the boom, the sensor data at these locations will change in this order, and the system will use these paths to evaluate the mechanical condition. Based on the load transmission path, the system calculates the "load change vector" for each target node, which is the magnitude and direction of the change at that node. This reflects the specific performance of each part under load changes. For example, if the load change vector of the root sensor is upward, it means that the load is being transmitted to the upper part. If the load change vector of the boom is biased to the right, it may indicate some kind of asymmetrical load distribution. Based on the structure and mechanical transmission method of each target acquisition node, the system calculates the deviation between the working condition change vector and the standard working condition direction; this can help determine whether abnormal mechanical behavior has occurred; for example, if the load change at the root deviates significantly from the standard direction (such as abnormal twisting), the system will warn of possible structural problems. The system categorizes the load changes of each target acquisition node into three types based on the magnitude (i.e., the magnitude of the change): the lifting preparation stage, the heavy load transmission stage, and the unloading and static stage. For example, if the load change of a sensor is very small, it may be in the "lifting preparation stage", while a sensor with a large load change belongs to the "heavy load transmission stage".

[0022] In an alternative embodiment, the system further includes: The deviation determination module 6 is used to determine the rate deviation value of each target acquisition node in each category based on the magnitude of the working condition change vector of each target acquisition node in each category, and to determine the working condition deviation degree of each target acquisition node in combination with the direction deviation value of each target acquisition node. The anomaly filtering module 7 is used to cluster each target acquisition node according to the deviation of the working condition of each target acquisition node and the installation and deployment information of each target acquisition node to obtain multiple node clusters. Based on the number of target acquisition nodes in the node cluster and the magnitude of the deviation of the working condition of all target acquisition nodes in the cluster, the abnormal node cluster is filtered. The anomaly location module 8 is used to determine the structural anomaly area of ​​the target double rocker arm mast based on the installation position of the sensor acquisition unit corresponding to the anomaly node cluster.

[0023] It should be noted that the system also calculates the rate deviation and operating condition deviation of each node; combining these indicators, the system performs cluster analysis to find abnormal node clusters; for example, if a node has a high operating condition deviation during the heavy load phase, it may mean that its load is different from the expectation, which may indicate that the component has failed. Based on the results of cluster analysis, the system will filter out node clusters that may have problems; these abnormal clusters can help identify structurally abnormal areas of the pole; for example, if multiple sensors in a certain part show excessive deviations, the system may determine that there is a structural problem in that part and remind maintenance personnel to check the area. Based on the location of the abnormal node cluster, the system ultimately determines the structural abnormality area of ​​the target double rocker arm mast for further maintenance or repair. For example, if the system finds a large deviation in operating conditions in a certain node cluster, and the cluster is located at the rocker arm joint, the system will report that there may be a structural abnormality in this area.

[0024] In an optional embodiment, the sensing and acquisition unit includes a stress sensor, a deformation sensor, a load sensor, a tilt sensor, and a vibration sensor; the pole monitoring correlation map corresponding to the target double rocker arm pole includes stress acquisition nodes, deformation acquisition nodes, load acquisition nodes, tilt acquisition nodes, and vibration acquisition nodes; The load condition transmission path matching the load condition sub-attachment map is determined from the pole monitoring correlation map, including: Based on the stress acquisition nodes, deformation acquisition nodes, load acquisition nodes, tilt angle acquisition nodes, and vibration acquisition nodes on the pole monitoring correlation map, the corresponding stress transmission paths, deformation transmission paths, load transmission paths, tilt angle transmission paths, and vibration transmission paths are obtained respectively. Based on the time series consistency and data correlation of all transmission paths, the working condition transmission path of the target double rocker arm boom is determined.

[0025] It should be noted that the sensing and acquisition unit includes: a stress sensor: used to detect and acquire stress data on the boom; a deformation sensor: used to monitor the deformation of the boom to ensure that it is not excessively bent or deformed; a load sensor: used to measure the weight or load carried by the boom; a tilt sensor: used to monitor the angle change of the boom to prevent excessive tilting; and a vibration sensor: used to detect the vibration of the boom during operation and to assess whether there is any abnormal vibration in the machinery. The pole monitoring correlation map is a comprehensive map generated based on all sensor data and the acquisition nodes of each sensor. This map can reflect the relationships between different nodes, such as: stress acquisition nodes: monitor stress changes in the pole, usually located in areas of high stress; deformation acquisition nodes: monitor whether the pole has deformed; load acquisition nodes: monitor the weight or load borne by the pole; tilt angle acquisition nodes: monitor angle changes in the pole; vibration acquisition nodes: monitor vibration data of the pole. Based on the aforementioned pole monitoring correlation map, the system extracts relevant data from each sensor node and generates the working condition transmission path according to the following steps: Stress transmission path: The stress transmission path in the pole is determined by the stress data obtained from stress sensors; for example, if the stress at a certain position at the bottom of the pole suddenly increases, it may cause other components above to be affected by greater stress; the system will determine this path through the transmission relationship of stress data; Deformation transmission path: Deformation sensor data reflects the deformation of the pole; assuming that the pole may deform under heavy load, and this deformation may be transmitted from one part to other parts; the system will track the entire deformation process through the deformation transmission path and identify possible anomalies; Load Load transmission path: Load sensors monitor the weight carried; when the load at a certain point changes, it affects the load distribution of the entire boom; based on the data collected by the load sensors, the system can determine how the load is transferred from one part to another. Tilt transmission path: Tilt sensors monitor changes in the boom's angle; when a part tilts excessively, it may affect the stability of the entire structure; the system determines the propagation path of the tilt based on the tilt data and analyzes the potential impact on the entire mechanical structure. Vibration transmission path: Vibration sensors monitor the vibration of mechanical components; based on the vibration data, the system can determine whether vibration may be transmitted from one part to other parts, leading to overall vibration imbalance. When calculating the transmission path of operating conditions, the system not only considers data from different sensors but also confirms the transmission path based on the time series consistency and data correlation of these data. Specifically: Time series consistency: If the data from one sensor changes first, and then the data from other sensors change subsequently, this means that the change in the data from the earlier sensor may be the cause of the subsequent changes; therefore, the system will determine the correlation between the data according to the time series. Data correlation: The system also analyzes the correlation between data from different sensors; for example, if the change in stress sensor data is closely related to the data from deformation sensor data, this indicates that the stress change may be the direct cause of deformation; by analyzing this correlation, the system can determine the specific operating state of the mechanical component. Practical Example: Suppose a target double-arm gantry crane is undergoing lifting operations. How would the system analyze its state using the above steps? During lifting, load sensors may detect an increase in load at the base of the gantry crane, while stress sensors monitor the stress generated as the load increases. The system first traces the transmission paths of these load and stress data to identify the load transmission path and potential anomalies. Simultaneously, deformation sensors monitor whether the gantry crane undergoes excessive deformation, especially under heavy loads. This deformation information helps the system determine whether adjustments to the machine's angle or load distribution are necessary. Tilt sensors may detect a slight tilt of the gantry crane during lifting. In this case, the system uses the tilt transmission path to determine the cause of the tilt and whether it may affect the operation of other components. Vibration sensors detect vibration during this process. The system analyzes the source of the vibration and whether it is caused by unbalanced loads or deformation of mechanical components, thus determining the vibration transmission path.

[0026] In an optional embodiment, based on the boom operation data of each target acquisition node along the working condition transmission path, the working condition change vector of each target acquisition node is determined, including: Take any target acquisition node as the core acquisition node, and trace the parameters along multiple pre-set uniformly distributed directions with the core acquisition node as the center. Obtain the boundary acquisition nodes whose pole operation data is continuously less than or equal to the pole operation data of the previous acquisition node. Calculate the deployment distance from the boundary acquisition node to the core acquisition node, obtain the parameter difference between the core acquisition node and the boundary acquisition node, and use the ratio of the parameter difference to the deployment distance as the modulus to construct the parameter change vector in each direction. The sum of the parameter change vectors in each direction of the core acquisition node is used as the operating condition change vector of the core acquisition node.

[0027] It should be noted that, firstly, the target acquisition node refers to the sensor point used to collect data under various working conditions; for example, acquisition nodes such as stress sensors, deformation sensors, and load sensors are located in different locations; the core acquisition node is a selected node among these nodes, which is usually the central point for data analysis; data changes at other nodes will be analyzed around the core acquisition node. Suppose we select a core acquisition node, such as a stress sensor node; in multiple directions surrounding the core node, the system will trace parameters according to preset directions (these directions are evenly distributed), that is, analyze the trend of data change in these directions, until it finds the boundary acquisition node; the boundary acquisition node is defined as: in a certain direction, the data value begins to be less than or equal to the data of the core acquisition node; these points represent the data limit value in a certain direction, beyond which the data may be abnormal or change too much and can no longer continue to change according to the normal pattern; Once the boundary acquisition nodes are located, the next steps are: Deployment distance: This refers to the actual distance between the core acquisition node and the boundary acquisition node. For example, assuming the core acquisition node is in the middle of the mast and the boundary acquisition node is at one end, the deployment distance is the physical distance between them. Parameter difference: This refers to the parameter difference between the core acquisition node and the boundary acquisition node. This is obtained by comparing the difference between the data collected by the two nodes. For example, assuming the stress data of the core acquisition node is 100N and the stress data of the boundary acquisition node is 80N, then the parameter difference is 20N. Next, the parameter difference... The ratio of the value to the deployment distance yields the modulus; the modulus is a numerical value that measures the intensity of sensor data change in that direction, representing the degree of change in sensor data from the core node to the boundary node in that direction; through the above steps, the parameter change vector in each direction can be obtained; the vector in each direction reflects the magnitude and direction of data change; different directions will have different change vectors; finally, the operating condition change vector of the core acquisition node is the vector sum of the change vectors in all directions; the vector sum represents the comprehensive effect of data change in each direction, and is a comprehensive description of the operating status of the core acquisition node in the entire monitoring area; Practical example: Suppose monitoring is performed on a double rocker arm mast, and a stress sensor is selected as the core acquisition node to analyze its working condition changes; First, the stress sensor is used as the core acquisition node, and tracing is performed in four evenly distributed directions (assuming the four directions are north, east, south, and west). Step 1: Trace stress data: In the north direction, it is found that when the stress data reaches a certain point, it begins to be less than the core node data; this point is the boundary acquisition node. Suppose that the stress data at this node is 90N, while the core node data is 100N. Step 2: Calculate the deployment distance: Assuming the distance from the core node to the boundary node is 2 meters, then the deployment distance is 2 meters. Step 3: Calculate the parameter difference: The stress data of the core node is 100N, the boundary node is 90N, and the parameter difference is 10N; Step 4: Calculate the modulus length: The modulus length is the ratio of the parameter difference to the deployment distance, i.e., 10N / 2 meters = 5N / meter; this represents the intensity of stress variation in the north direction. Step 5: Construct parameter change vectors: Repeat the same steps for other directions (east, south, west) to obtain the change vectors for each direction; assume the change vector for the east is 4N / m, for the south it is 3N / m, and for the west it is 6N / m; Step 6: Calculate the operating condition change vector: Finally, sum the change vectors in the four directions of north, east, south, and west to obtain the operating condition change vector of the core acquisition node; if the change vectors in the four directions are (5N / m, 4N / m, 3N / m, 6N / m), their vector sum may be a vector (there may be an angle depending on the direction), which describes the operating condition change of the core acquisition node in the entire monitoring area.

[0028] In an optional embodiment, the standard operating direction of each target acquisition node is determined based on the structural bearing direction and mechanical transmission direction of each part of the double rocker arm mast, including: Based on the structural design document of the double rocker arm pole, the structural bearing direction and mechanical transmission direction of each part are set. The structural design document is mapped to the pole monitoring correlation map to obtain the structural bearing direction and mechanical transmission direction of each target acquisition node. For each target acquisition node, the unit vector corresponding to the structural bearing direction of the node location and the unit vector corresponding to the mechanical transmission direction are vector superimposed to obtain a composite vector. The direction of the composite vector is used as the standard working condition direction of the target acquisition node.

[0029] It should be noted that the structural load-bearing direction refers to the direction of force distribution within the structure when the scaffold structure is subjected to external loads. This is determined by the mechanical analysis during the design phase and reflects the force direction at each part of the scaffold. For example, one part may bear tensile force, while another part may bear compressive force. The force transmission direction refers to the path through which external loads are transmitted to different parts of the structure, that is, how the force propagates throughout the entire structure. This path is usually transmitted through the connection points between structural components. For example, the force borne by one part may be transmitted to another part through a connection point. In this step, the structural load-bearing direction and force transmission direction of each part are determined based on the structural design documents of the double rocker arm boom. The structural design documents usually contain the dimensions, shape and stress analysis results of each part of the boom, which helps to identify the force direction and force transmission path of each part. The boom monitoring correlation map is a system diagram that associates each acquisition node with each structural part of the boom. Each target acquisition node has a specific load-bearing direction and force transmission direction for its corresponding structural part, and these two directions determine how to analyze the working condition changes of that node. For each target acquisition node, the following steps are required: Determine the unit vectors for the structural bearing direction and the force transmission direction: The unit vector for the structural bearing direction describes the direction of the force at that location; the unit vector for the force transmission direction describes the direction of the force transmission path. Each location's structural bearing direction and force transmission direction will be represented by a unit vector. The unit vector has a length of 1, representing the direction, and its direction reflects the direction of the force's action or transmission. Vector superposition: Add these two unit vectors to obtain a composite vector. This composite vector is the standard operating condition direction of the target acquisition node. The direction of the composite vector describes the force direction of the acquisition node, i.e., the operating condition state of the node under the combined action of the structural bearing direction and the force transmission direction. Practical Example: Suppose there is a double-rocker arm boom containing multiple sections, with the target data acquisition node located at one of these sections. The standard operating condition orientation is determined using the following steps: Target Data Acquisition Node Location: Assume the target data acquisition node is located in the middle of the double-rocker arm boom, near the connection point of the rocker arms; Structural Load Bearing Direction: Based on the design documents and monitoring data, assume the structural load bearing direction at this section is horizontal; that is, when this section bears external loads, the force is mainly distributed horizontally; assume the unit vector of the structural load bearing direction is (1,0), representing the horizontal direction; Force Transmission Direction: Assume the force transmission direction at this section is diagonally upward along the boom, because the load may be connected to other sections through the rocker arms, and... The force is transmitted diagonally; the unit vector of the force transmission direction is (0.707, 0.707), representing the diagonally upward transmission path; the unit vector of the structural bearing direction is (1, 0); the unit vector of the force transmission direction is (0.707, 0.707); adding these two vectors yields the composite vector: composite vector = (1, 0) + (0.707, 0.707) = (1.707, 0.707); the composite vector (1.707, 0.707) describes the standard working condition direction of the target acquisition node; this means that the force direction of the node is a composite result of the horizontal bearing capacity and the diagonally upward transmitted force; to normalize this composite vector into a unit vector, its magnitude can be calculated.

[0030] In an optional embodiment, the target acquisition nodes are divided into three categories based on the magnitude of the working condition change vector of each target acquisition node, corresponding to the lifting preparation stage, the heavy load transfer stage, and the unloading and static placement stage, respectively, including: Traverse the pole monitoring association map, and form a sequence of moduli of the working condition change vectors of the target acquisition nodes according to the traversal order. Divide the moduli sequence into three subsequences, and classify the target acquisition nodes corresponding to each element in each subsequence into the same category.

[0031] It should be noted that the magnitude of the working condition change vector refers to the magnitude of the working condition change of the target acquisition node, reflecting the range of force or displacement change of the node; the larger the magnitude, the more drastic the working condition change of the node and the more obvious the force change; the smaller the magnitude, the more stable the working condition change of the node and the smaller the force change. Based on the magnitude of the load change vector of each target acquisition node, these nodes are divided into three categories, corresponding to: Lifting preparation stage: This is the stage where the load-bearing capacity begins to be applied, usually indicating that the load change of the node is small and the magnitude is small; Heavy load transmission stage: In this stage, the magnitude of the load change of the node increases, and the magnitude increases, indicating that the load-bearing capacity transmission in the structure has reached a heavy load state; Unloading and settling stage: This stage usually occurs during unloading, where the magnitude of the load change decreases, and the magnitude gradually decreases, indicating that the node gradually returns to a static state; Pole monitoring association map: This map associates each target acquisition node with various parts of the pole, recording the load change of each node; Traversing the pole monitoring association map: Traversing the map in a certain order to obtain the magnitude of the load change vector of each node; The magnitudes of each node constitute a sequence, i.e., the magnitude sequence. Modulus length sequence segmentation: Based on the changing trend of the modulus length sequence, the sequence can be divided into three subsequences, each corresponding to a stage; the first subsequence corresponds to the lifting preparation stage, with a smaller modulus length, indicating that the working condition of the node changes slightly in this stage; the second subsequence corresponds to the heavy load transmission stage, with a larger modulus length, indicating that the node is under heavy load and the working condition changes drastically; the third subsequence corresponds to the unloading and settling stage, with a gradually decreasing modulus length, indicating that the working condition of the node slows down and gradually returns to a static state; Specific example: Suppose there is monitoring data for a double-arm boom, and the magnitudes of the working condition change vectors of the target acquisition nodes are as follows: Magnitude sequence = [0.1, 0.2, 0.3, 0.6, 1.2, 1.8, 1.5, 1.0, 0.4, 0.2]; according to the order of the boom monitoring correlation map, the magnitudes of the working condition change vectors of each target acquisition node are extracted sequentially to form a magnitude sequence; Based on the changing trend of the modulus length sequence, it is divided into three sub-sequences: Lifting preparation stage: In this stage, the modulus length is relatively small, and the working conditions of the nodes change slightly, typically within the modulus length range of 0.1 to 0.3. Based on the modulus length sequence, the first three values ​​are taken as nodes in the lifting preparation stage, and the sub-sequence is: [0.1, 0.2, 0.3]; Heavy load transmission stage: In this stage, the modulus length is relatively large, and the working conditions of the nodes change drastically, indicating the transmission of load-bearing capacity in this stage. Based on the modulus length sequence, modulus length values ​​from 1.2 to 1.8 represent this stage, and the sub-sequence is: [0.6, 1.2, 1.8, 1.5]; Unloading and settling stage: In this stage, the modulus length gradually decreases, indicating that the working conditions of the nodes slow down and gradually return to a static state. Based on the modulus length sequence, modulus length values ​​from 0.4 to 0.2 represent the unloading and settling stage, and the sub-sequence is: [1.0, 0.4, 0.2]; Based on the above segmentation results, the target acquisition nodes can be divided into three stages: Lifting preparation stage: corresponding to the module length sequence [0.1, 0.2, 0.3], the working conditions of these nodes change little, and they are in the initial stage of lifting; Heavy load transmission stage: corresponding to the module length sequence [0.6, 1.2, 1.8, 1.5], the working conditions of these nodes change significantly, and they are in the stage of bearing heavy loads and transmitting forces; Unloading and settling stage: corresponding to the module length sequence [1.0, 0.4, 0.2], the working conditions of these nodes gradually decrease, and they are in the stage of settling after unloading, and the nodes gradually return to a static state.

[0032] In an optional embodiment, the rate deviation value of each target acquisition node in each category is determined based on the magnitude of the operating condition change vector of each target acquisition node in each category, and the operating condition deviation degree of each target acquisition node is determined by combining the direction deviation value of each target acquisition node, including: Since the target acquisition node belongs to the second category, the magnitude of the working condition change vector of the target acquisition node is normalized to obtain the rate deviation value of the target acquisition node. In response to the target acquisition node belonging to the first or third category, the target acquisition node belonging to the same category as the target acquisition node and having the same standard operating condition direction is used as the reference acquisition node; The difference between the magnitudes of the operating condition change vectors of the target acquisition node and all its reference acquisition nodes is calculated, and the differences are summed with weights to obtain the rate deviation value of the target acquisition node; where, when performing the weighted summation, the weights are negatively correlated with the directional deviation values ​​of the reference acquisition nodes. The working condition deviation of the target acquisition node is obtained by weighted summing of the directional deviation and the rate deviation.

[0033] It should be noted that the rate deviation value represents the degree of deviation between the rate of change of the target acquisition node's operating condition and other reference nodes. The calculation method is as follows: For nodes belonging to the second category (i.e., the heavy load transmission stage), the rate deviation value is obtained by normalizing the magnitude of the node's operating condition change vector. Normalization means adjusting the magnitude value according to a certain standard range so that its value can be used for comparison and further calculation. For example, if the magnitude of the target node's operating condition change vector is 1.8, while the maximum magnitude of the entire second category of nodes is 3.0, then the rate deviation value of the target node may be: 1.8 / 3.0 = 0.6. For nodes belonging to either the first category (lifting preparation stage) or the third category (unloading and settling stage), when calculating the rate deviation value, not only is the magnitude of the node's operating condition change vector considered, but it is also compared with reference acquisition nodes of the same category and with the same standard operating condition direction. First, other nodes belonging to the same category as the target node and with the same direction as the standard operating condition direction are selected as reference nodes. Then, the difference in the magnitude of the operating condition change vector between the target node and all reference nodes is calculated. These differences reflect the deviation of the target node in rate relative to the reference nodes. The differences are weighted and summed to obtain the rate deviation value of the target node. When weighting, the smaller the direction deviation value of the reference node, the greater the weight; the greater the direction deviation value, the smaller the weight. Directional deviation measures the degree of deviation of the target acquisition node's operating condition change direction from the standard operating condition direction. Typically, it is determined by calculating the angle between the direction of the operating condition change vector and the standard operating condition direction; a larger deviation indicates a more severe directional deviation. Operating condition deviation is an indicator that combines rate deviation and directional deviation, reflecting the overall degree of deviation of the target node's operating condition in terms of both rate and direction. The calculation method involves a weighted sum of the target node's rate deviation and directional deviation to obtain the final operating condition deviation. During calculation, the weights of the rate deviation and directional deviation can be adjusted according to actual conditions; typically, these weights are set based on the specific requirements of the operating condition changes. Specific example: Suppose there are three target acquisition nodes, belonging to three different categories (first, second, and third); the magnitude and directional deviation of the working condition change vector of each node are as follows: Target node A (first category, lifting preparation stage): magnitude 0.3, directional deviation 0.2; Target node B (second category, heavy load transmission stage): magnitude 1.8, directional deviation 0.1; Target node C (third category, unloading and settling stage): magnitude 0.5, directional deviation 0.3; Target node A (Category 1): The magnitude difference of the load change vector between target node A and other reference nodes of the same category (e.g., node D) is 0.2, and the direction deviation is 0.1. When calculating the rate deviation using weighted summation, the direction deviation of node D is not much different from that of A, so its weight is larger, and the final rate deviation may be 0.15 (assuming the calculation result). Target node B (Category 2): Node B is in the heavy load transmission stage, so the rate deviation is obtained by normalizing the magnitude. For example, if the magnitude is 1.8 and the maximum value is 3.0, then the rate deviation is 0.6. Target node C (Category 3): The magnitude difference of the load change vector between target node C and other reference nodes of the same category (e.g., node E) is 0.4, and the direction deviation is 0.2. According to the weighted summation rule, the rate deviation may be 0.3. The directional deviation value is directly derived from the deviation between the direction of the node's working condition change vector and the standard working condition direction: the directional deviation value of target node A is 0.2; the directional deviation value of target node B is 0.1; and the directional deviation value of target node C is 0.3. Ultimately, the deviation from the operating condition is obtained by weighted summation of the speed deviation and the direction deviation. For example, for target node A, the deviation from the operating condition = speed deviation (0.15) * weight 1 + direction deviation (0.2) * weight 2; for target node B, the deviation from the operating condition = speed deviation (0.6) * weight 1 + direction deviation (0.1) * weight 2; for target node C, the deviation from the operating condition = speed deviation (0.3) * weight 1 + direction deviation (0.3) * weight 2. Depending on the actual situation, the weight values ​​can be set to 1:1 or other suitable ratios.

[0034] In an optional embodiment, based on the operating condition deviation of each target acquisition node and the installation and deployment information of each target acquisition node, the target acquisition nodes are clustered to obtain multiple node clusters, including: The deployment coordinates and operating condition deviations of each target acquisition node in the pole monitoring correlation map are used to construct the feature vector of each target acquisition node. Based on the feature vectors, the target acquisition nodes are clustered to obtain multiple node clusters.

[0035] It should be noted that before clustering, a feature vector needs to be constructed for each target acquisition node. The feature vector consists of two main components: operating condition deviation: this is the degree of deviation between the node's operating condition and the reference operating condition, as discussed earlier, representing the difference in the node's performance under different operating conditions; and deployment coordinates: these are the physical location coordinates of the node, usually represented by two-dimensional or three-dimensional coordinates, indicating the node's installation position in space. These two pieces of information are combined into a feature vector, representing the node's operating condition characteristics and spatial characteristics. After constructing the feature vector of each node, clustering algorithms (such as K-means, hierarchical clustering, etc.) can be used to cluster these nodes. The purpose of clustering is to group nodes with similar working conditions and locations into the same cluster, thereby discovering nodes with similar working conditions and geographical distribution. The basic idea of ​​clustering algorithms is to calculate the similarity between nodes: by calculating the distance between node feature vectors (such as Euclidean distance), determine which nodes have similar working conditions and locations; and assign nodes to different clusters according to similarity: nodes with high similarity are grouped into the same cluster, forming multiple node clusters.

[0036] In an optional embodiment, abnormal node clusters are filtered based on the number of target acquisition nodes in the node cluster and the degree of deviation of the operating conditions of all target acquisition nodes in the cluster, including: If the number of target acquisition nodes in the node cluster is greater than a preset first threshold, and the average deviation of the operating conditions of all target acquisition nodes in the cluster is greater than a preset second threshold, the node cluster is designated as an abnormal node cluster.

[0037] It should be noted that the first threshold sets a threshold for the number of nodes. Only when the number of target acquisition nodes in the node cluster exceeds this threshold will the cluster be considered as an abnormal cluster. The second threshold sets a threshold for the degree of deviation of operating conditions. Only when the average degree of deviation of operating conditions of all target acquisition nodes in the cluster exceeds this threshold will the nodes in the cluster be considered to be performing abnormally. These two thresholds work together to filter out node clusters that not only have a large number of nodes but also have large deviations in operating conditions.

[0038] In an optional embodiment, after determining the structural anomaly area of ​​the target double-rocker arm mast based on the installation location of the sensor acquisition unit corresponding to the abnormal node cluster, the method further includes: Based on the working condition transmission path and structural anomaly area of ​​all target acquisition nodes, determine all potential risk nodes corresponding to the target double rocker arm mast; potential risk nodes are the mast parts corresponding to acquisition nodes that are adjacent to the structural anomaly area and are subject to the preset anomaly influence range in the mast monitoring correlation map. For each potential risk node, the risk diffusion trajectory of the target double rocker arm boom is determined based on the working condition transmission path and the working condition deviation of the node. Based on the risk diffusion trajectory and boom operation data, the safety warning parameters corresponding to the potential risk node are determined.

[0039] It is important to note that, firstly, it is necessary to understand the operational condition transmission path of the target acquisition nodes. The operational condition transmission path refers to how an abnormality in the operational condition of one node affects other nodes through connected sensors, mechanical components, or other transmission paths. The operational condition transmission path is described by the correlation between nodes. For example, when the operational condition of one node becomes abnormal, other related nodes may also be affected. In this process, the structural abnormality areas of the target dual rocker arm mast have been identified. These abnormality areas are usually where certain parts of the mast have suffered obvious structural damage, wear, or other abnormalities, which may affect the safety and operating status of the entire system. Based on the operational transmission paths of all target acquisition nodes and the identified structural anomaly areas, the next step is to determine the potential risk nodes corresponding to the target double-rocker boom. These potential risk nodes refer to those nodes that are adjacent to the structural anomaly area in the boom monitoring correlation map and are located within a preset anomaly influence range. In other words, some nodes are physically close to the identified structural anomaly area, and these nodes may be affected by the anomaly transmission path. For each potential risk node, its specific location and function relative to the boom are further analyzed. Potential risk nodes correspond to nodes adjacent to the structural anomaly area in the boom monitoring correlation map. These nodes may detect certain abnormal operational changes through sensors or be directly associated with specific parts of the boom (such as supports, connection points, etc.). Therefore, these nodes may become key points in the propagation of the entire structural anomaly. Next, a detailed analysis of each potential risk node is required. The risk diffusion trajectory is determined by analyzing the operational condition transmission path and the node's operational condition deviation. This trajectory describes how an anomaly at a node affects other parts through the structure and transmission path. For example, an anomaly at a node might be transmitted to other parts of the target double-rocker arm mast via connecting rods, supports, and other components. Determining the risk diffusion trajectory depends not only on the operational condition transmission path but also on the operational condition deviation of each node. The operational condition deviation of a node measures the degree to which it deviates from its normal operating state. Nodes with larger deviations indicate more severe anomalies and may trigger wider impacts. Therefore, the operational condition deviation of a node directly affects the scope and speed of risk diffusion. Finally, based on the risk diffusion trajectory and the operational data of the boom, the safety warning parameters corresponding to potential risk nodes can be determined. These safety warning parameters are indicators that predict and alert to possible abnormal operating conditions. Based on real-time monitoring data, operating condition transmission paths, and risk diffusion trajectories, these parameters help determine the overall safety of the target double-rocker boom. For example, suppose a rapid increase in operating condition deviation is detected at a certain node, and this node is adjacent to a structurally abnormal area of ​​the target double-rocker boom. Through operating condition transmission path analysis, it may be found that the abnormality at this node will cause the entire boom support system to shift, thus affecting the stability of the boom. In this case, the system will issue a safety warning signal based on preset safety standards and thresholds. Specific example: Suppose an industrial piece of equipment includes a double-rocker boom structure, which supports large mechanical devices. Among multiple monitoring nodes of the equipment, the following data are identified: Node A: Deviation of 0.4, located at the boom support; Node B: Deviation of 0.8, located at the upper end of the boom, connected to the support via a connecting rod; Node C: Deviation of 0.6, located at the lower end of the boom, connected to the support. Suppose that analysis reveals that the structural anomalies in Node B and Node A are very close, and that an anomaly in Node A can affect Node B through a transmission path; further analysis reveals... The deviation of node B's operating condition is approaching a critical value, meaning its anomaly could lead to a failure in the boom structure; therefore, node B is identified as a potential risk node. Based on the risk diffusion trajectory analysis, the anomaly of node B will affect the stability of the entire double-rocker boom, potentially causing damage to other parts. Therefore, a safety warning parameter is set, assuming the system automatically issues a warning when the operating condition deviation exceeds 0.7. In this case, if the operating condition deviation of node B reaches 0.8, the system will issue an alarm based on real-time monitoring data, reminding operators to take emergency measures, such as stopping the machine for inspection and reinforcing the supports.

[0040] Example 2, please refer to Figure 2 A method for monitoring a dual-rocker arm pole, applicable to a dual-rocker arm pole monitoring system, comprising: S1. Based on the data acquisition type and installation information of the sensor acquisition unit of the target double rocker arm mast, as well as the linkage relationship between the sensor acquisition unit and other sensor acquisition units, generate the mast monitoring association map corresponding to the target double rocker arm mast. S2. Obtain the pole operation data from the sensor acquisition unit, determine the working condition identifier of the target double rocker arm pole based on the pole operation data; determine all target acquisition nodes in the pole monitoring association map based on the working condition identifier; determine the working condition sub-association map corresponding to the target double rocker arm pole based on the node linkage relationship between the target acquisition nodes and other target acquisition nodes. S3. Determine the working condition transmission path that matches the working condition sub-correlation map from the pole monitoring correlation map; based on the pole operation data of each target acquisition node on the working condition transmission path, determine the working condition change vector of each target acquisition node. S4. Determine the standard working condition direction of each target acquisition node based on the structural bearing direction and mechanical transmission direction of each part of the double rocker arm mast. Determine the directional deviation value of each target acquisition node based on the difference between the direction of the working condition change vector of each target acquisition node and the standard working condition direction. S5. Based on the magnitude of the working condition change vector of each target acquisition node, the target acquisition nodes are divided into three categories, corresponding to the lifting preparation stage, the heavy load transmission stage, and the unloading and static placement stage, respectively.

[0041] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A dual swing boom derrick monitoring system, characterized by, include: The map modeling module is used to generate a pole monitoring association map corresponding to the target double rocker arm pole based on the data acquisition type and installation information of the sensor acquisition unit of the target double rocker arm pole and the linkage relationship between the sensor acquisition unit and other sensor acquisition units. The association modeling module is used to acquire the pole operation data based on the sensing acquisition unit, and determine the working condition identifier of the target double rocker arm pole based on the pole operation data; Based on the working condition identifier, determine all target acquisition nodes in the pole monitoring association map; based on the node linkage relationship between the target acquisition nodes and other target acquisition nodes, determine the working condition sub-association map corresponding to the target double rocker arm pole; The working condition coding module is used to determine the working condition transmission path that matches the working condition sub-correlation map from the pole monitoring correlation map; and to determine the working condition change vector of each target acquisition node based on the pole operation data of each target acquisition node on the working condition transmission path. The orientation determination module is used to determine the standard working condition orientation of each target acquisition node based on the structural bearing direction and mechanical transmission direction of each part of the double rocker arm mast, and to determine the orientation deviation value of each target acquisition node based on the difference between the orientation of the working condition change vector of each target acquisition node and the standard working condition orientation. The node classification module is used to classify target acquisition nodes into three categories based on the magnitude of the working condition change vector of each target acquisition node, corresponding to the lifting preparation stage, the heavy load transfer stage, and the unloading and static placement stage, respectively. The deviation determination module is used to determine the rate deviation value of each target acquisition node in each category based on the magnitude of the working condition change vector of each target acquisition node in each category, and to determine the working condition deviation degree of each target acquisition node in combination with the direction deviation value of each target acquisition node. The anomaly filtering module is used to cluster each target acquisition node according to the deviation of its operating conditions and the installation and deployment information of each target acquisition node, to obtain multiple node clusters, and to filter out abnormal node clusters according to the number of target acquisition nodes in the node cluster and the magnitude of the deviation of the operating conditions of all target acquisition nodes in the cluster. The anomaly localization module is used to determine the structural anomaly area of ​​the target double rocker arm mast based on the installation location of the sensor acquisition unit corresponding to the anomaly node cluster.

2. A dual swing arm boom monitoring system according to claim 1, wherein, The sensing and acquisition unit includes a stress sensor, a deformation sensor, a load sensor, a tilt sensor, and a vibration sensor; the pole monitoring correlation map corresponding to the target double rocker arm pole includes stress acquisition nodes, deformation acquisition nodes, load acquisition nodes, tilt acquisition nodes, and vibration acquisition nodes; Determining the working condition transmission path from the pole monitoring correlation map to match the working condition sub-correlation map includes: Based on the stress acquisition nodes, deformation acquisition nodes, load acquisition nodes, tilt angle acquisition nodes, and vibration acquisition nodes on the pole monitoring correlation map, the corresponding stress transmission paths, deformation transmission paths, load transmission paths, tilt angle transmission paths, and vibration transmission paths are obtained respectively. Based on the time series consistency and data correlation of all transmission paths, the working condition transmission path of the target double rocker arm pole is determined.

3. A dual swing arm boom monitoring system according to claim 2, wherein, Based on the boom operation data of each target acquisition node along the aforementioned working condition transmission path, the working condition change vector for each target acquisition node is determined, including: Take any target acquisition node as the core acquisition node, and trace the parameters along multiple pre-set uniformly distributed directions with the core acquisition node as the center. Obtain the boundary acquisition nodes whose pole operation data is continuously less than or equal to the pole operation data of the previous acquisition node. Calculate the deployment distance from the boundary acquisition node to the core acquisition node, obtain the parameter difference between the core acquisition node and the boundary acquisition node, and use the ratio of the parameter difference to the deployment distance as the modulus to construct the parameter change vector in each direction. The sum of the parameter change vectors in each direction of the core acquisition node is used as the operating condition change vector of the core acquisition node.

4. A dual swing arm boom monitoring system according to claim 3, wherein, The standard operating conditions of each target acquisition node are determined based on the structural bearing direction and mechanical transmission direction of each part of the double rocker arm mast, including: Based on the structural design document of the double rocker arm pole, the structural bearing direction and mechanical transmission direction of each part are set. The structural design document is mapped to the pole monitoring correlation map to obtain the structural bearing direction and mechanical transmission direction of each target acquisition node. For each target acquisition node, the unit vector corresponding to the structural bearing direction of the node location and the unit vector corresponding to the mechanical transmission direction are vector superimposed to obtain a composite vector, and the direction of the composite vector is used as the standard working condition direction of the target acquisition node.

5. A dual swing arm boom monitoring system according to claim 4, wherein, Based on the magnitude of the working condition change vector of each target acquisition node, the target acquisition nodes are divided into three categories, corresponding to the lifting preparation stage, the heavy load transfer stage, and the unloading and static placement stage, respectively, including: Traverse the pole monitoring association map, and form a sequence of moduli of the working condition change vectors of the target acquisition nodes according to the traversal order. Divide the moduli sequence into three subsequences, and classify the target acquisition nodes corresponding to each element in each subsequence into the same category.

6. A dual swing arm boom monitoring system according to claim 5, wherein, The rate deviation value of each target acquisition node in each category is determined based on the magnitude of the operating condition change vector of each target acquisition node in each category. The operating condition deviation degree of each target acquisition node is determined by combining the direction deviation value of each target acquisition node, including: In response to the target acquisition node belonging to the second category, the magnitude of the working condition change vector of the target acquisition node is normalized to obtain the rate deviation value of the target acquisition node. In response to the target acquisition node belonging to the first or third category, a target acquisition node belonging to the same category as the target acquisition node and having the same standard operating condition direction is used as a reference acquisition node; Calculate the difference between the magnitudes of the operating condition change vectors of the target acquisition node and all its reference acquisition nodes, and perform a weighted summation of the differences to obtain the rate deviation value of the target acquisition node; wherein, when performing the weighted summation, the weights are negatively correlated with the directional deviation values ​​of the reference acquisition nodes; The operating condition deviation of the target acquisition node is obtained by weighted summing of the directional deviation value and the rate deviation value.

7. A dual swing arm boom monitoring system according to claim 6, wherein, Based on the operational deviation of each target acquisition node and its installation and deployment information, the target acquisition nodes are clustered to obtain multiple node clusters, including: The deployment coordinates and operating condition deviations of each target acquisition node in the pole monitoring correlation map are used to form the feature vector of each target acquisition node. Based on the feature vectors, the target acquisition nodes are clustered to obtain multiple node clusters.

8. A dual swing arm boom monitoring system according to claim 7, wherein, Based on the number of target acquisition nodes in the node cluster and the degree of deviation in operating conditions of all target acquisition nodes in the cluster, abnormal node clusters are filtered out, including: If the number of target acquisition nodes in the node cluster is greater than a preset first threshold, and the average deviation of the operating conditions of all target acquisition nodes in the cluster is greater than a preset second threshold, the node cluster is designated as an abnormal node cluster.

9. A dual swing arm boom monitoring system according to claim 8, wherein, After determining the structural anomaly area of ​​the target double-rocker arm mast based on the installation location of the sensor acquisition unit corresponding to the abnormal node cluster, the method further includes: Based on the working condition transmission path and structural anomaly area of ​​all target acquisition nodes, determine all potential risk nodes corresponding to the target double rocker arm pole; the potential risk node is the pole part corresponding to the acquisition node in the pole monitoring correlation map that is adjacent to the structural anomaly area and is subject to the preset anomaly influence range; For each potential risk node, the risk diffusion trajectory of the target double rocker arm boom is determined based on the working condition transmission path and the working condition deviation of the node. Based on the risk diffusion trajectory and boom operation data, the safety warning parameters corresponding to the potential risk node are determined.

10. A method of monitoring a dual swing boom pole, adapted to be used in a system of monitoring a dual swing boom pole according to any one of claims 1 to 9, characterized in that, include: Based on the data acquisition type and installation information of the sensor acquisition unit of the target double rocker arm pole, as well as the linkage relationship between the sensor acquisition unit and other sensor acquisition units, a pole monitoring association map corresponding to the target double rocker arm pole is generated. The pole-mounting operation data is acquired by the sensor acquisition unit, and the working condition identifier of the target double rocker arm pole is determined based on the pole-mounting operation data. All target acquisition nodes in the pole monitoring association map are determined based on the working condition identifier; Based on the node linkage relationship between the target acquisition node and other target acquisition nodes, the working condition sub-association map corresponding to the target double rocker arm pole is determined; The working condition transmission path matching the working condition sub-correlation map is determined from the pole monitoring correlation map; based on the pole operation data of each target acquisition node on the working condition transmission path, the working condition change vector of each target acquisition node is determined; The standard working condition direction of each target acquisition node is determined based on the structural bearing direction and mechanical transmission direction of each part of the double rocker arm mast. The directional deviation value of each target acquisition node is determined based on the difference between the direction of the working condition change vector of each target acquisition node and the standard working condition direction. Based on the magnitude of the working condition change vector of each target acquisition node, the target acquisition nodes are divided into three categories, corresponding to the lifting preparation stage, the heavy load transmission stage, and the unloading and static placement stage, respectively.

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