Transmission monitoring method and system for petroleum drilling machine chain

Abstract

The invention discloses a transmission monitoring method and system for a petroleum drilling machine chain, and relates to the related technical field of chain monitoring, and the method comprises the steps: carrying out the key structure extraction of the petroleum drilling machine chain, and building a dynamic structure topological graph; configuring a multi-mode sensor on a petroleum drilling machine chain, and establishing monitoring mapping; executing attribute feature updating of edges and points in the dynamic structure topological graph; establishing a multi-body flexible mechanical model of chain transmission, dynamically simulating the stress state of a chain link, and establishing a first transmission early warning; establishing a physical sensitivity attention factor based on an identification result; and updating the dynamic structure topological graph, and establishing a second transmission early warning. The technical problems that in the prior art, a petroleum drilling machine chain is single in monitoring means, key structure changes cannot be accurately captured, the chain transmission state is difficult to accurately evaluate in real time, and transmission fault early warning accuracy and real-time performance are insufficient are solved. The technical effect of improving the real-time performance and accuracy of petroleum drilling machine chain transmission fault monitoring is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of chain monitoring, and particularly relates to a transmission monitoring method and system for a chain of an oil drilling rig. BACKGROUND

[0002] As a key component in the transmission system of the drilling rig, the chain of the oil drilling rig undertakes the heavy task of transmitting power and driving various devices of the drilling rig to operate. The working condition of the chain transmission is extremely complex, and faces many challenges such as high load, high speed, harsh environment (such as high temperature, high humidity, much dust and corrosive medium). The traditional monitoring method of the chain transmission of the oil drilling rig has limitations. On the one hand, the understanding of the complex structure of the chain only stays on the surface, and cannot deeply understand the key structure and the dynamic changes formed between structures based on the physical connection relationship, which leads to the difficulty in constructing a model accurately reflecting the actual working state of the chain, and the inability to timely and accurately capture potential fault hidden dangers that may occur in the running process of the chain, such as the risk of wear, deformation or even fracture of part of the chain links due to long-term stress. On the other hand, the single type sensor monitoring method cannot comprehensively and comprehensively reflect the real state of the chain transmission, and the monitoring data lacks effective association with the overall structure and mechanical properties of the chain, which cannot provide sufficient basis for the evaluation of the running state of the chain. When facing complex and changeable working conditions, the judgment based on limited data is often inaccurate, which cannot meet the strict requirements of modern oil exploitation for safe and efficient operation of equipment.

[0003] Therefore, in the related art at present, there is a technical problem that the monitoring method of the chain of the oil drilling rig is single, cannot accurately capture the changes of the key structure, and is difficult to accurately evaluate the real-time and precise transmission state of the chain, resulting in insufficient accuracy and real-time of the transmission fault early warning. SUMMARY

[0004] The present application provides a transmission monitoring method and system for a chain of an oil drilling rig, which solves the technical problem that the monitoring method of the chain of the oil drilling rig is single, cannot accurately capture the changes of the key structure, and is difficult to accurately evaluate the real-time and precise transmission state of the chain, resulting in insufficient accuracy and real-time of the transmission fault early warning, and achieves the technical effect of improving the real-time and accuracy of the transmission fault monitoring of the chain of the oil drilling rig.

[0005] This application provides a transmission monitoring method for an oil drilling rig chain. The method includes: extracting key structures from the oil drilling rig chain and establishing a dynamic structural topology map based on the key structure extraction results and physical connection relationships; configuring multi-mode sensors on the oil drilling rig chain and establishing a monitoring mapping of the multi-mode sensor monitoring nodes in the dynamic structural topology map; collecting monitoring data using the multi-mode sensors and updating the edge and point attribute features within the dynamic structural topology map according to the monitoring mapping; establishing a multi-body flexible mechanical model of the chain transmission based on the oil drilling rig chain, using the monitoring data as dynamic parameter input for the model, dynamically simulating the stress state of the chain links, and establishing a first transmission warning based on the deviation between the simulated stress state and the actual state; identifying the chain tension center node, boundary node, and sprocket link node of the oil drilling rig chain, and establishing a physical sensitivity attention factor based on the identification results; updating the self-identification weight of the dynamic structural topology map through the physical sensitivity attention factor, and establishing a second transmission warning based on the first transmission warning using the updated dynamic structural topology map.

[0006] This application also provides a transmission monitoring system for an oil drilling rig chain. The system includes: a dynamic structure topology map establishment module for extracting key structures from the oil drilling rig chain and establishing a dynamic structure topology map based on the extracted key structures and physical connection relationships; a monitoring mapping establishment module for configuring multi-mode sensors on the oil drilling rig chain and establishing a monitoring mapping of the multi-mode sensor monitoring nodes in the dynamic structure topology map; an attribute feature update module for updating the edge and point attribute features within the dynamic structure topology map based on the monitoring mapping after collecting monitoring data using the multi-mode sensors; and a first transmission early warning establishment module for establishing early warnings based on the oil drilling rig chain chain. A multi-body flexible mechanical model of the chain drive is established, and monitoring data is used as the input of dynamic parameters of the model to dynamically simulate the stress state of the chain links. A first transmission warning is established based on the deviation between the simulated stress state and the actual state. A physical sensitivity attention factor establishment module is used to identify the chain tension center node, boundary node, and sprocket link node of the oil drilling rig chain, and establish physical sensitivity attention factors based on the identification results. A second transmission warning establishment module is used to update the self-identification weight of the dynamic structure topology map through the physical sensitivity attention factors, and then establish a second transmission warning based on the first transmission warning through the updated dynamic structure topology map.

[0007] This application proposes a transmission monitoring method and system for oil drilling rig chains. The method involves extracting key structures from the oil drilling rig chain and establishing a dynamic structural topology map. Multi-mode sensors are configured on the chain, and a monitoring mapping is established. Edge and point attribute features within the dynamic structural topology map are updated. A multi-body flexible mechanical model of the chain transmission is established to dynamically simulate the stress state of the chain links and establish a first transmission early warning. Based on the identification results, a physical sensitivity attention factor is established. The self-identification weights of the dynamic structural topology map are updated to establish a second transmission early warning. This method solves the technical problems of existing oil drilling rig chain monitoring methods being singular, unable to accurately capture key structural changes, and difficult to perform real-time and accurate assessment of the chain transmission state, resulting in insufficient accuracy and real-time performance of transmission fault early warning. This method effectively improves the real-time performance and accuracy of oil drilling rig chain transmission fault monitoring. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0009] Figure 1 This is a schematic flowchart of a transmission monitoring method for an oil drilling rig chain, provided as an embodiment of this application.

[0010] Figure 2 This is a schematic diagram of a transmission monitoring system for an oil drilling rig chain, provided as an embodiment of this application.

[0011] Explanation of reference numerals in the attached diagram: Dynamic structure topology graph establishment module 10, monitoring mapping establishment module 20, attribute feature update module 30, first transmission early warning establishment module 40, physical sensitivity attention factor establishment module 50, second transmission early warning establishment module 60. Detailed Implementation

[0012] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0013] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0015] This application provides a method for monitoring the transmission of an oil drilling rig chain, such as... Figure 1 As shown, the method includes: Step S100: Extract the key structure of the oil drilling rig chain and establish a dynamic structural topology based on the key structure extraction results and physical connection relationships.

[0016] Preferably, key structures of the oil drilling rig chain are extracted. Specifically, high-definition industrial cameras are used to photograph or videotape the oil drilling rig chain from multiple angles. Edge detection algorithms, such as the Canny operator and the Sobel operator, are used to perform edge detection on the chain images and extract the edge contour information of the chain. This allows for the clear delineation of the shape and position of key structures such as chain links, sprocket engagement points, and tensioning devices. Structures that have a critical impact on transmission performance and operating status are then extracted. Chain links are the basic building blocks of the chain, and their wear and deformation directly affect the chain's strength and transmission accuracy. The sprocket engagement point is where the chain and sprocket interact; the stress conditions at this point are complex, making it prone to wear, tooth skipping, and other problems, affecting transmission efficiency. The tensioning device maintains appropriate chain tension, ensuring that the chain does not become slack or over-tensioned during transmission, which would affect the chain's service life and transmission stability. Then, based on the key structure extraction results and combined with the physical connection relationships of chain links, sprocket engagement points, and tensioning devices, a graphical model reflecting the structural characteristics and dynamic changes of the oil drilling rig chain is constructed, namely a dynamic structural topology diagram. Specifically, key structures such as chain links, sprocket engagement points, and tensioning devices are represented by nodes, while physical connections (such as connections between chain links, connections between chain links and sprocket engagement points, and connections between tensioning devices and the chain) are represented by edges. This topology construction transforms the complex structure of the oil drilling rig chain into an intuitive graphical structure, facilitating the analysis and monitoring of the chain's motion state. Furthermore, the key structural states and connection relationships during chain operation dynamically change with time and operating conditions; therefore, the topology diagram is dynamic and can reflect the actual operation of the chain in real time.

[0017] Step S200: Configure multi-mode sensors on the oil drilling rig chain and establish a monitoring mapping of the multi-mode sensor monitoring nodes on the dynamic structural topology.

[0018] Preferably, based on the working environment and monitoring requirements of the oil drilling rig chain, various types of sensors are selected and rationally arranged at key parts of the chain to obtain the chain's operating status information from different angles. This may include installing force sensors on the chain tensioning device or key links to measure the tension and pressure on the chain, to understand the chain's stress condition and determine if there are overloads or uneven stress distribution; and placing displacement sensors at the joints of the chain links or near the sprockets to monitor the extension, displacement, and rotation of the chain links, in order to detect chain-related issues. To prevent potential chain malfunctions such as chain elongation and sprocket eccentricity, temperature sensors are distributed throughout the chain, especially in areas prone to frictional heat, such as the joints between chain links and the meshing points between the sprocket and the chain. These sensors monitor chain temperature changes in real time to determine if excessive friction or other factors are causing abnormal temperature increases. Vibration sensors are installed on the chain's support structure or chain links to detect vibrations during operation. Analyzing the vibration signals helps identify abnormal vibration patterns, such as those caused by chain link wear or sprocket malfunctions.

[0019] Preferably, a monitoring mapping of multi-mode sensor monitoring nodes in the dynamic structural topology is then established. This involves establishing a correspondence between each sensor and the corresponding node or edge in the dynamic structural topology, so that the data collected by the sensors can accurately reflect the state changes of specific structural elements in the topology. Specifically, the data collected by force sensors, displacement sensors, etc., are correlated with chain link nodes, sprocket meshing point nodes, tensioning device nodes, etc., in the dynamic structural topology. For example, the force data measured by a force sensor installed on a chain link corresponds to the mechanical properties of that chain link node in the topology; the chain link displacement information monitored by the displacement sensor is also associated with the position change attributes of the corresponding chain link node in the topology. Through sensor data, the attribute characteristics of the corresponding nodes in the topology can be updated in real time, intuitively reflecting the actual state of key structures such as chain links and sprocket meshing points. The connections between chain links and the connection between chain links and sprocket meshing points are represented as edges in the topology graph. Sensors monitor the relative motion and friction between chain links to reflect the attribute characteristics of the edges. For example, micro-motion sensors installed at the chain link connections monitor the minute relative displacements and friction between chain links and correlate them with the attributes of the edges representing the chain link connections in the topology graph. Thus, the sensor data updates the attributes of the edges, such as the tightness of the connection and the magnitude of the friction, thereby more comprehensively describing the physical connection state of the key chain structures in the dynamic structural topology graph.

[0020] Furthermore, step S200 also includes step S210, acquiring interference environment data of the oil drilling rig chain, performing interference intensity evaluation of the interference environment data, and establishing interference intensity evaluation results; step S220, determining whether the interference intensity evaluation results meet the interference threshold; step S230, if the interference intensity evaluation results do not meet the interference threshold, then directly configuring multi-mode sensors based on the oil drilling rig chain.

[0021] Preferably, the working environment of oil drilling rigs is complex, with various factors that may interfere with chain drive monitoring, such as vibration, electromagnetic interference, high temperature, and dust. Various sensors and monitoring devices are used to collect data on these potentially interfering environmental factors, obtaining interference environmental data. For example, vibration sensors are used to measure the vibration amplitude and frequency of the drilling rig during operation; electromagnetic monitoring equipment records the intensity and changes of the surrounding electromagnetic field; temperature sensors monitor the ambient temperature; and dust concentration detectors measure the dust content in the air. This comprehensively describes the interference environment in which the oil drilling rig chain operates. Then, an interference intensity evaluation is performed on the interference environmental data. This involves a comprehensive assessment of the various interference environmental data based on the characteristics of different interference factors and their potential impact on monitoring. For example, the frequency and amplitude of vibration are compared with the sensitive frequency range of the chain drive monitoring; the electromagnetic field strength is compared with the electromagnetic interference immunity threshold of the monitoring system; and so on. The resulting interference intensity evaluation directly reflects the degree of interference.

[0022] Preferably, based on the performance and anti-interference capability of the oil drilling rig chain drive monitoring system, an interference threshold is set as the judgment standard. The interference intensity evaluation result is then compared with the interference threshold to determine whether the current interference environment will have a significant impact on the monitoring system. If the interference intensity evaluation result is less than or equal to the interference threshold, it indicates that the interference environment is within the tolerance range of the monitoring system and will not cause serious interference to the monitoring results, allowing it to work normally. Conversely, if the interference intensity evaluation result is greater than the interference threshold, it indicates that the interference will affect the accuracy and reliability of the monitoring. In this case, multi-mode sensors are directly configured on the oil drilling rig chain. Through the collaborative work of various types of sensors, the operating status information of the chain can be obtained more comprehensively and accurately, overcoming the impact of the interference environment on monitoring, thereby improving the accuracy and reliability of chain data monitoring under interference environments.

[0023] Furthermore, step S220 also includes step S221, if the interference intensity evaluation result meets the interference threshold, then a redundancy configuration instruction is generated; step S222, the multi-mode sensor of the oil drilling rig chain is configured according to the redundancy configuration instruction, and a verification mechanism between modes is set, and the multi-mode sensor configuration is completed according to the verification mechanism.

[0024] Preferably, if the interference intensity evaluation result meets the preset interference threshold, it indicates that the current interference environment will not seriously affect the monitoring of the oil drilling rig chain. Under normal circumstances, the chain's operating status information can be obtained relatively accurately, and redundant configuration instructions are generated to redundantly configure the multi-mode sensors to enhance monitoring reliability and prevent possible sensor failures or interference. Then, according to the generated redundant configuration instructions, additional multi-mode sensors are installed on the oil drilling rig chain. These may be combinations of different types of sensors, such as force sensors, displacement sensors, temperature sensors, vibration sensors, etc. Data acquisition from multiple modes comprehensively monitors the chain's operating status, ensuring that if one sensor fails or its data is abnormal, other sensors can still operate normally, guaranteeing the continuity and accuracy of the monitoring data.

[0025] Step S300: After collecting monitoring data using the multi-mode sensor, update the edge and point attribute features in the dynamic structural topology graph according to the monitoring mapping.

[0026] Preferably, multi-mode sensors are configured at key parts of the oil drilling rig chain, enabling them to acquire various monitoring data from different dimensions during chain operation. Specifically, this includes monitoring the tension and pressure on various parts of the chain; monitoring the extension and displacement of chain links and the rotation of sprockets, capturing changes in chain position; acquiring the temperature of various parts of the chain in real time; detecting vibration during chain operation, and analyzing the presence of abnormal vibration modes. The monitoring mapping of multi-mode sensor monitoring nodes in the dynamic structural topology map clarifies the correspondence between each sensor and specific nodes (such as chain links, sprocket meshing points, tensioning devices, etc.) or edges (representing the physical connection relationships between nodes) in the dynamic structural topology map. Then, based on the data collected by the sensors, the physical state attributes of the nodes in the topology map are updated. For example, if a force sensor measures an increase in the force on a chain link, the force attribute value of that chain link node in the topology map will be updated accordingly; if a displacement sensor detects a change in the position of the sprocket meshing point, the position attribute of that node in the topology map will also change accordingly. Similarly, based on data collected by sensors, the edge attributes representing the node connections in the topology graph are updated. For example, the attributes of the edges representing the chain links are updated based on the relative displacement and friction between the links, such as the tightness of the connection and the magnitude of the friction force, thereby more accurately describing the physical connection state between the key structures of the chain. By continuously updating the edge and node attribute features in the dynamic structural topology graph based on data collected by multi-mode sensors, the actual operating status of the oil drilling rig chain can be reflected in real time and accurately, thus ensuring the accuracy of chain fault monitoring and early warning.

[0027] Step S400: Based on the oil drilling rig chain, establish a multi-body flexible mechanical model of the chain drive, input the monitoring data as the dynamic parameters of the model, dynamically simulate the stress state of the chain links, and establish a first transmission early warning based on the deviation between the simulated stress state and the actual state.

[0028] Preferably, the oil drilling rig chain consists of multiple components such as chain link bodies, sprocket bodies, chain link connecting pins, tensioning devices, and guide rail bases. Then, the material properties, geometry, and connection methods of each component are comprehensively considered, such as the stiffness of the chain link bodies and sprocket bodies, the rotational characteristics of the chain link connecting pins, the elasticity of the tensioning devices, and the supporting effect of the guide rail base on the chain. The flexibility of each component of the chain is also considered, that is, the ability to reflect their elastic deformation and dynamic response under stress. Based on these, a multi-body flexible mechanical model that can accurately describe the transmission process of the oil drilling rig chain is established. Then, various monitoring data collected by multimodal sensors, including information on the force, displacement, vibration, and temperature of the chain links, are input as dynamic parameters into the multibody flexible mechanical model. This allows the model to perform dynamic simulation based on actual operating conditions. In other words, the multibody flexible mechanical model performs numerical calculations and simulations to dynamically simulate the force state of the chain links at different times and under different working conditions. This includes considering the effects of various forces during chain transmission, such as chain tension, the meshing force of the sprocket on the chain links, the friction between the chain links, the tension generated by the tensioning device, and the inertial and elastic forces caused by chain vibration and deformation.

[0029] Preferably, by solving the corresponding mechanical equations, the magnitude, direction, and distribution of the force on each link at each moment can be calculated to reflect the changes in the force state of the link in real time and predict possible abnormal force conditions. For example, the force data of the link measured by the force sensor can be used as the input of the external force on the link body in the model, the displacement data of the link measured by the displacement sensor can be used to update the position and deformation state of the link in the model, and the vibration data measured by the vibration sensor can affect the dynamic response characteristics of the components in the model, so that the multibody flexible mechanical model can more accurately simulate the mechanical behavior of the chain in actual work. Finally, the stress state of the chain links obtained from dynamic simulation is compared with the stress state of the chain links actually measured by multi-mode sensors, and the deviation between the two is calculated. If the deviation is within the allowable range, it means that the simulation results of the model are in good agreement with the actual situation, and the chain drive system is operating normally. If the deviation exceeds the set threshold, it means that there may be a problem, such as chain wear, sprocket failure, tensioning device failure, or abnormality of other components, which will cause the actual stress on the chain links to be inconsistent with the stress predicted by the model, thereby triggering the first transmission warning, reminding the operator to pay attention to the possible problems of the chain drive system, so as to take corresponding measures to check, repair or adjust in time, avoid more serious failures and accidents, and ensure the safe and reliable operation of the oil drilling rig chain.

[0030] Furthermore, step S400 also includes step S410, decomposing the oil drilling rig chain into multiple original structures, the multiple original structures including chain link bodies, sprocket bodies, chain link connecting pins, tensioning devices, and guide rail bases; step S420, after modeling the multiple original structures into rigid and flexible bodies, assembling the modeling results into a system on the MBS platform, the system assembly including constructing the chain link connection relationship of the oil drilling rig chain through revolute joints and nonlinear contact joints; step S430, establishing a multibody flexible mechanical model based on the system assembly results.

[0031] Preferably, the oil drilling rig chain is decomposed into multiple original structures, namely the chain link body, sprocket body, chain link connecting pin, tensioning device, and guide rail base. This facilitates accurate modeling of each component, thereby more accurately describing the mechanical behavior of the entire chain. Then, rigid body and flexible body models are performed on the multiple original structures. Specifically, for components that deform relatively little during operation and can be approximated as rigid bodies, such as the sprocket body, rigid body modeling can be used. This means treating it as a rigid body with a certain mass and moment of inertia, ignoring its internal elastic deformation, and focusing on its overall motion and force conditions by defining its geometric parameters. For components such as mass distribution and center of mass, a rigid body model is established to describe their motion in space. For components that undergo significant elastic deformation during operation, such as chain links and connecting pins, flexible body modeling is required. This involves discretizing these components into a large number of elements and nodes using the finite element method. By defining the material's elastic modulus, Poisson's ratio, density, and other mechanical property parameters, as well as the geometry and connection method of the elements, a flexible body model is established to reflect the internal elastic deformation and stress distribution. This allows for a more accurate simulation of the deformation of these components under stress and their impact on the dynamic behavior of the entire chain system.

[0032] Preferably, the models of each original structure are then assembled on a Multibody System Dynamics (MBS) platform. Revolute joints are used to connect the chain links, allowing relative rotation around a specific axis. This simulates the articulated motion of the chain links in actual operation. By defining the position, axial direction, and motion constraints of the revolute joints, the basic connection structure of the chain is constructed, ensuring that the chain links can rotate relative to each other according to actual working conditions. Simultaneously, nonlinear contact pairs are used to simulate the complex contact between the chain link and the sprocket, and between the chain link connecting pin and the chain link. Specifically, the nonlinear contact pairs consider the normal force, friction, and contact deformation between the contact surfaces. By defining the geometry, material properties, and contact algorithm parameters of the contact surfaces, the meshing process between the chain link and the sprocket, and the fit relationship between the chain link connecting pin and the chain link, are accurately simulated, thus more realistically reflecting the mechanical behavior of the oil drilling rig chain during transmission. Finally, a multibody flexible mechanical model was established, which can accurately describe the dynamic behavior of the oil drilling rig chain under different working conditions, such as the tension change of the chain, the force and deformation of the chain links, and the rotation characteristics of the sprocket, thereby ensuring the accuracy and real-time performance of chain fault monitoring and early warning.

[0033] Furthermore, step S400 also includes step S440, setting the sprocket angular velocity input as the driving term and applying the initial preload of the tensioner to initialize the flexible mechanical model; step S450, inputting the monitoring data as model boundary parameters and disturbance terms into the multibody flexible mechanical model, and then performing dynamic simulation of the stress state of the chain links through finite element simulation to establish the simulated stress state; step S460, using the comparison module integrated in the multibody flexible mechanical model to compare the deviation between the simulated stress state and the actual state to establish a dynamic residual index; step S470, generating the first transmission warning based on the dynamic residual index.

[0034] Preferably, by setting the angular velocity of the sprocket and using it as the driving term, the movement of the chain driven by the sprocket is simulated under different working conditions. Then, the initial preload of the tensioner is applied, which simulates the initial tension applied to the chain by the actual tensioner after installation and debugging. The tensioner ensures that the chain maintains appropriate tension during transmission, avoiding problems caused by the chain being too loose or too tight. After setting the sprocket angular velocity and the initial preload of the tensioner, initialization calculations are performed based on the initial conditions, including setting the initial position, velocity, acceleration, and other state variables of each component, as well as initializing and configuring various parameters and equations in the model. The monitoring data is then input into the multibody flexible mechanical model as boundary parameters and disturbance terms to make the model more closely resemble actual operating conditions. The boundary parameters are used to restrict the boundary conditions of the model, and the disturbance terms are used to simulate various random factors that may occur in actual operation, such as local wear of the chain and external impact. The finite element method is then used to dynamically simulate the stress state of the chain links. That is, the chain links are discretized into multiple finite element elements. By solving the mechanical equations of the elements, the magnitude, direction and distribution of the forces on the chain links at different times and under different working conditions are calculated. Combined with the input monitoring data and the initial conditions of the model, the simulated stress state is established.

[0035] Preferably, the comparison module is used to compare the simulated chain link stress state with the actual monitored chain link stress state. By comparing the deviations of relevant parameters of the two states, such as the magnitude, direction, and point of application of the force, the deviation result is obtained, reflecting the degree of deviation between the model simulation result and the actual situation. Then, the deviation is quantified to establish a dynamic residual index, which is used to measure the degree of difference between the simulated stress state and the actual state. Finally, a dynamic residual index threshold is preset. When the calculated dynamic residual index exceeds this threshold, it indicates that the deviation between the simulated stress state and the actual state is too large, and there may be some abnormalities, such as chain wear, component failure, etc., which cause the actual stress on the chain to be inconsistent with the stress predicted by the model. Then, the first transmission warning is automatically generated to remind the operator to pay attention to the operating status of the oil drilling rig chain drive so that corresponding measures can be taken for inspection, maintenance or adjustment to avoid more serious failures and accidents and ensure the normal operation of the oil drilling rig.

[0036] Step S500: Identify the chain tension center node, boundary node, and sprocket link node of the oil drilling rig chain, and establish a physical sensitivity concern factor based on the identification results.

[0037] Preferably, the oil drilling rig chain is identified by defining the chain tension center node, boundary nodes, and sprocket link nodes. Specifically, the chain tension center node refers to the node location where the chain bears the main tension and the tension distribution is relatively concentrated during chain transmission. Identifying the chain tension center node through analysis and calculation of the chain's mechanical properties helps determine the area where the chain bears the maximum tension during transmission, such as the middle or connecting part of the link. Boundary nodes refer to nodes where the chain connects to other components or are located at the edge of the chain, such as the connection between the chain and the tensioning device, the guide rail base, and the nodes at both ends of the chain. Identifying boundary nodes helps to accurately analyze the interaction between the chain and surrounding components and the overall mechanical performance. Sprocket link nodes refer to the nodes where the chain meshes with the sprocket. Accurately identifying sprocket link nodes allows for better study of the power transmission process, wear conditions, and potential faults between the chain and the sprocket. After identifying these nodes, the forces acting on each node are analyzed, including the magnitude and direction of the forces borne by each node under different operating conditions. Based on the results of the node force analysis, a physical sensitivity concern factor is assigned to each node to represent its importance or sensitivity within the entire chain system. Generally, nodes subjected to greater forces and with more significant impacts have higher physical sensitivity concern factor values. By establishing physical sensitivity concern factors, key components of the oil drilling rig chain can be monitored in a focused manner, thereby ensuring the accuracy of chain fault monitoring and early warning.

[0038] Furthermore, step S500 also includes step S510, fitting the force fluctuation amplitude of the identification result, and establishing a first attention coefficient based on the force fluctuation amplitude fitting result; step S520, obtaining the structural stiffness change rate of the identification result, and establishing a second attention coefficient based on the structural stiffness change rate; step S530, retrieving historical abnormal data from the identification result, and establishing a third attention coefficient; step S540, after fusing the first attention coefficient, the second attention coefficient, and the third attention coefficient, establishing the physical sensitivity attention factor.

[0039] Preferably, the stress conditions of the identified chain tension center nodes, boundary nodes, sprocket link nodes, etc., are analyzed, and the fluctuation range of their stress with time or other relevant factors (such as changes in the drilling rig's working state) is statistically analyzed. Then, curve fitting is used to fit the fluctuation range to determine the first concern coefficient. Generally speaking, the larger the stress fluctuation range, the more drastic the force change at that node, and the greater the impact on the chain's performance and stability, thus the higher the first concern coefficient. For each identified node, the structural stiffness change at its location is analyzed, and the rate of change of structural stiffness with time is calculated to determine the second concern coefficient. The faster the rate of change of structural stiffness, the more significant the change in structural performance at that node, and the higher the second concern coefficient will be. The past operating data of the identified nodes is queried and analyzed to check for any anomalies. In common situations, such as past overloads, excessive wear, and deformation, a third concern coefficient is established based on historical anomaly data. If a node has a large number of historical anomaly records, it indicates that the node's reliability is relatively low and it is more prone to problems, so the third concern coefficient will be higher. The first, second, and third concern coefficients are then weighted and integrated, that is, corresponding weights are assigned according to the importance of different coefficients. For example, if the magnitude of force fluctuation is considered to have the greatest impact on chain performance, a larger weight can be assigned to the first concern coefficient; if the rate of change of structural stiffness and the importance of historical anomaly data are relatively low, a smaller weight can be assigned to the second and third concern coefficients. By integrating these factors to establish a physical sensitivity concern factor, the sensitivity and importance of each node to changes in the physical performance of the oil drilling rig chain can be comprehensively and holistically reflected.

[0040] Step S600: After updating the self-identification weight of the dynamic structure topology graph through the physical sensitivity attention factor, a second transmission warning is established based on the first transmission warning through the updated dynamic structure topology graph.

[0041] Preferably, the self-identification weight represents the importance of a node or its sensitivity to changes in the chain's operating state. The physical sensitivity attention factor is correlated with nodes in the dynamic structural topology diagram. The self-identification weight of the corresponding node is adjusted based on the value of the physical sensitivity attention factor. For example, for nodes with a high physical sensitivity attention factor, their self-identification weight in the dynamic structural topology diagram is increased; for nodes with a low physical sensitivity attention factor, their self-identification weight is appropriately decreased. This allows the dynamic structural topology diagram to more accurately reflect the actual importance of each node in the chain. Then, the updated dynamic structural topology diagram is used to process the first transmission warning. Specifically, combining the self-identification weight of nodes in the topology diagram with structural relationships, the impact of the nodes included in the first transmission warning on the chain transmission is comprehensively evaluated. If a node affected by the first transmission warning has a high self-identification weight in the updated dynamic structural topology diagram, it indicates that the abnormal state of that node may have a significant impact on the chain transmission. In this case, a second transmission warning is issued to further emphasize the severity of the problem and may prompt more proactive measures for fault diagnosis and handling, ensuring the safe and stable operation of the chain transmission.

[0042] Furthermore, step S600 also includes step S610, initially marking the first transmission warning in the updated dynamic structure topology graph; step S620, searching for K-order neighboring nodes in the dynamic structure topology graph using the initial mark as the search center, where K is an integer greater than 1; step S630, calculating the average residual of the K-order neighboring nodes, and if the average residual meets the dynamic threshold, updating the first transmission warning to a chain segment warning, and establishing the second transmission warning based on the chain segment warning.

[0043] Preferably, the nodes involved in the first transmission warning are clearly marked in the updated topology graph, i.e., initial marking is made, so that the parts related to the first transmission warning can be quickly located, and the position of the warning in the chain structure can be intuitively displayed. Then, with the initial marking as the search center, K-order neighboring nodes are searched in the dynamic structure topology graph, where K is an integer greater than 1, representing the search range level. A first-order neighboring node is a node directly connected to the search center node; a second-order neighboring node is a node directly connected to a first-order neighboring node but not directly connected to the search center, and so on. The K-order neighboring nodes are the set of nodes that can be reached after K steps of connection. By searching for K-order neighboring nodes, we can find surrounding nodes structurally associated with the first transmission warning, which helps to comprehensively analyze the potential impact range of the warning. Then, we calculate the average residual of the K-order neighboring nodes, which is the average of the deviation between the simulated and actual stress states of the calculated nodes, reflecting the overall degree of stress anomaly in the neighboring nodes. A dynamic threshold is set based on different working conditions and operating states of the chain. The calculated average residual is compared with this dynamic threshold. If the average residual meets (usually exceeds) the dynamic threshold, it indicates that the overall stress anomaly of the K-order neighboring nodes is relatively serious, and the first transmission warning is updated to a chain segment-level warning. A chain segment-level warning indicates that a fault or anomaly may affect a chain segment (including multiple links), better reflecting the severity and scope of the problem. Finally, a second transmission warning is established based on the chain segment-level warning to more accurately assess the operating status and potential risks of the chain drive system and issue corresponding alarms to remind operators to take more effective measures to handle potential faults and ensure the normal operation of the oil drilling rig chain.

[0044] Furthermore, step S620 also includes step S621, updating node weights through the updated dynamic structural topology graph; step S622, performing structural diffusion simulation of the first transmission warning based on heuristic traversal; step S623, calculating the cumulative propagation risk value of each node and establishing a simulation warning; and step S624, verifying the simulation warning and the chain segment warning to establish the second transmission warning.

[0045] Preferably, based on the updated dynamic structural topology graph, the weight of each node is re-evaluated so that the topology graph can more accurately reflect the actual situation of the chain drive. Then, a structural diffusion simulation of the first drive warning is performed based on heuristic traversal. That is, heuristic information (such as node weight, connection relationship, distance, etc.) is used to guide the search direction to search for nodes and paths that have a greater impact on the first drive warning or are more easily affected by the warning, while ensuring search efficiency and accuracy. Specifically, starting from the node involved in the first drive warning, heuristic traversal is used to simulate the diffusion process of the warning in the entire chain structure in the dynamic structural topology graph. For example, when a chain node triggers the first drive warning, through heuristic traversal, adjacent nodes can be explored along the edges connected to that node according to certain rules (such as prioritizing nodes with higher weights or closely connected paths). This simulates how the warning spreads from this initial node to other nodes and the areas that may be affected, helping to understand the potential impact range and propagation path of the warning and to discover other components that may be affected in advance.

[0046] Preferably, for each visited node, its cumulative risk value is calculated, including the node's weight, risk factors along the path from the first transmission warning node to the node (such as the reliability of connecting edges, fault history of passing nodes, etc.), and the node's own state parameters (such as stress conditions, structural stiffness, etc.). This allows for the establishment of a simulation-based early warning system. Based on the node's risk value, different levels of warning are assigned to different nodes. Nodes with higher cumulative risk values ​​receive stronger alarms, while nodes with lower risk values ​​may only receive general prompts or be recorded. This provides a more detailed assessment and warning of the risk status of each node in the chain system, improving the targeted nature of fault prevention and handling. Finally, the simulation-based and chain-segment-level early warnings are validated, including comparing actual monitoring data with the warning results to check if the warnings match the actual chain operation status, or referencing historical data and experience to assess the credibility of the warnings. A second transmission early warning system is established by combining the warning results and validation information to more accurately reflect the actual fault risks and operating status of the oil drilling rig chain drive, thereby ensuring the safe and stable operation of the oil drilling rig chain.

[0047] Furthermore, step S600 also includes step S640, using the updated dynamic structure topology graph to perform abnormal identification of graph embedding distance, reconstruction error, and propagation path mutation indicators based on monitoring data, and establishing an abnormal identification result; step S650, establishing a second transmission warning based on the abnormal identification result and the first transmission warning.

[0048] Preferably, graph embedding maps nodes and edges in the dynamic structure topology graph to a low-dimensional space. The graph embedding distance is the distance between nodes in the low-dimensional space. Based on monitoring data, the distance of each node in the topology graph in the graph embedding space is calculated. If the graph embedding distance of a node deviates significantly from the normal distance, it indicates that there is an anomaly in the graph embedding distance. The dynamic structure topology graph is reconstructed using the monitoring data. That is, the state information of each part of the chain (such as force, displacement, vibration, etc.) is substituted into the topology graph model to restore the running state of the chain. Then, the attributes of the nodes (such as position, force magnitude, etc.) and the attributes of the edges (such as connection, etc.) are compared. The system calculates the error between the reconstructed topology map and the original topology map (e.g., connection strength, force transmission efficiency). If the reconstruction error exceeds a threshold, it indicates a significant difference between the monitoring data and the topology model, potentially indicating anomalies. It analyzes the propagation paths of forces and vibrations between nodes in the dynamic structural topology map. Based on monitoring data, it calculates abrupt change indices along these paths, such as the abrupt change rate of forces and the degree of abrupt change in vibration frequency. If an abrupt change index on a certain propagation path suddenly increases or exhibits abnormal changes, it indicates a possible abnormal event along that path, such as component damage or loose connections, leading to abrupt changes in the propagation of physical quantities. These anomalies are then integrated to form an anomaly identification result, including the location of each anomaly point, the anomaly type (e.g., embedding distance anomaly, reconstruction error anomaly), and the degree of anomaly. The anomaly identification results are comprehensively analyzed with the first transmission warning to gain a more complete understanding of the chain drive's operating status and potential failure risks. Based on the results of the comprehensive analysis, it is determined whether a second transmission warning needs to be issued. If the anomaly identification results and the first transmission warning corroborate each other, or if the anomaly identification results show new or more serious problems, a second transmission warning is issued to provide more accurate and timely warnings of chain drive failures in oil drilling rigs and ensure the safe and stable operation of the chain drive in oil drilling rigs.

[0049] In the above text, refer to Figure 1 A method for monitoring the transmission of an oil drilling rig chain according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 A transmission monitoring system for an oil drilling rig chain is described according to an embodiment of the present invention.

[0050] According to an embodiment of the present invention, a transmission monitoring system for an oil drilling rig chain is provided to address the technical problems in the prior art, such as the limited monitoring methods for oil drilling rig chains, the inability to accurately capture key structural changes, and the difficulty in real-time and accurate assessment of the chain transmission status, resulting in insufficient accuracy and real-time performance in transmission fault warnings. This system achieves the technical effect of improving the real-time performance and accuracy of oil drilling rig chain transmission fault monitoring. Figure 2As shown, a transmission monitoring system for an oil drilling rig chain includes: a dynamic structure topology diagram establishment module 10, a monitoring mapping establishment module 20, an attribute feature update module 30, a first transmission early warning establishment module 40, a physical sensitivity attention factor establishment module 50, and a second transmission early warning establishment module 60.

[0051] The dynamic structure topology graph establishment module 10 is used to extract key structures from the oil drilling rig chain and establish a dynamic structure topology graph based on the key structure extraction results and physical connection relationships; the monitoring mapping establishment module 20 is used to configure multi-mode sensors on the oil drilling rig chain and establish a monitoring mapping of the multi-mode sensor monitoring nodes in the dynamic structure topology graph; the attribute feature update module 30 is used to update the edge and point attribute features in the dynamic structure topology graph according to the monitoring mapping after collecting monitoring data using the multi-mode sensors; and the first transmission early warning establishment module 40 is used to establish a multi-body flexible transmission system based on the oil drilling rig chain. The system employs a mechanical model that uses monitoring data as dynamic parameter input to dynamically simulate the stress state of chain links. A first transmission warning is established based on the deviation between the simulated stress state and the actual stress state. A physical sensitivity attention factor establishment module 50 identifies the chain tension center node, boundary node, and sprocket link node of the oil drilling rig chain, and establishes physical sensitivity attention factors based on the identification results. A second transmission warning establishment module 60 updates the self-identification weights of the dynamic structural topology diagram using the physical sensitivity attention factors, and then establishes a second transmission warning based on the first transmission warning using the updated dynamic structural topology diagram.

[0052] The specific configuration of the first transmission early warning establishment module 40 will be described in detail below. The first transmission early warning establishment module 40 further includes: decomposing the oil drilling rig chain into multiple original structures, including chain link bodies, sprocket bodies, chain link connecting pins, tensioning devices, and guide rail bases; after modeling the multiple original structures as rigid and flexible bodies, assembling the modeling results into a system on the MBS platform, the system assembly including constructing the chain link connection relationship of the oil drilling rig chain through revolute joints and nonlinear contact joints; and establishing a multi-body flexible mechanical model based on the system assembly results.

[0053] The specific configuration of the first transmission early warning establishment module 40 will be described in detail below. The first transmission early warning establishment module 40 further includes: setting the sprocket angular velocity input as the driving term and applying the initial preload of the tensioner to initialize the flexible mechanical model; inputting the monitoring data as model boundary parameters and disturbance terms into the multibody flexible mechanical model, and then performing dynamic simulation of the stress state of the chain links through finite element simulation to establish the simulated stress state; using a comparison module integrated in the multibody flexible mechanical model to compare the deviation between the simulated stress state and the actual state to establish a dynamic residual index; and generating the first transmission early warning based on the dynamic residual index.

[0054] The specific configuration of the physical sensitivity attention factor establishment module 50 will be described in detail below. The physical sensitivity attention factor establishment module 50 further includes: fitting the force fluctuation amplitude to the identification result, and establishing a first attention coefficient based on the force fluctuation amplitude fitting result; obtaining the structural stiffness change rate of the identification result, and establishing a second attention coefficient based on the structural stiffness change rate; retrieving historical abnormal data from the identification result, and establishing a third attention coefficient; and establishing the physical sensitivity attention factor by fusing the first attention coefficient, the second attention coefficient, and the third attention coefficient.

[0055] The specific configuration of the second transmission warning establishment module 60 will be described in detail below. The second transmission warning establishment module 60 further includes: initially marking the first transmission warning in the updated dynamic structure topology graph; searching for K-order neighboring nodes in the dynamic structure topology graph using the initial mark as the search center, where K is an integer greater than 1; calculating the average residual of the K-order neighboring nodes; if the average residual meets a dynamic threshold, updating the first transmission warning to a chain segment-level warning, and establishing the second transmission warning based on the chain segment-level warning.

[0056] The specific configuration of the second transmission early warning establishment module 60 will be described in detail below. The second transmission early warning establishment module 60 further includes: updating node weights through the updated dynamic structural topology graph; performing structural diffusion simulation of the first transmission early warning based on heuristic traversal; calculating the cumulative risk value of propagation for each node to establish a simulation early warning; and verifying the simulation early warning and the chain segment-level early warning to establish the second transmission early warning.

[0057] The specific configuration of the second transmission early warning establishment module 60 will be described in detail below. The second transmission early warning establishment module 60 further includes: using the updated dynamic structural topology map to identify anomalies in graph embedding distance, reconstruction error, and propagation path mutation indicators based on monitoring data, and establishing anomaly identification results; and establishing the second transmission early warning based on the anomaly identification results and the first transmission early warning.

[0058] The specific configuration of the monitoring mapping establishment module 20 will be described in detail below. The monitoring mapping establishment module 20 further includes: acquiring interference environment data of the oil drilling rig chain, performing interference intensity evaluation of the interference environment data, and establishing interference intensity evaluation results; determining whether the interference intensity evaluation results meet the interference threshold; if the interference intensity evaluation results do not meet the interference threshold, then directly configuring multi-mode sensors based on the oil drilling rig chain.

[0059] The specific configuration of the monitoring mapping establishment module 20 will be described in detail below. The monitoring mapping establishment module 20 further includes: generating a redundancy configuration instruction if the interference intensity evaluation result meets the interference threshold; configuring the multi-mode sensor of the oil drilling rig chain according to the redundancy configuration instruction, and setting an inter-mode verification mechanism; and completing the multi-mode sensor configuration according to the verification mechanism.

[0060] The transmission monitoring system for an oil drilling rig chain provided in this embodiment of the invention can execute the transmission monitoring method for an oil drilling rig chain provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0061] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0062] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring the transmission of an oil drilling rig chain, characterized in that, The method includes: Key structures of the oil drilling rig chain are extracted, and a dynamic structural topology is established based on the key structure extraction results and physical connection relationships. Multimode sensors are configured on the oil drilling rig chain, and a monitoring mapping of the multimode sensor monitoring nodes on the dynamic structural topology is established. After collecting monitoring data using the multi-mode sensor, the edge and point attribute features in the dynamic structural topology graph are updated according to the monitoring mapping. A multi-body flexible mechanical model of chain transmission is established based on the oil drilling rig chain. The monitoring data is used as the dynamic parameter input of the model to dynamically simulate the stress state of the chain links. A first transmission early warning is established based on the deviation between the simulated stress state and the actual state. The chain tension center node, boundary node, and sprocket link node of the oil drilling rig chain are identified, and a physical sensitivity concern factor is established based on the identification results; After updating the self-identification weights of the dynamic structure topology graph using the physical sensitivity attention factor, a second transmission early warning is established based on the first transmission early warning using the updated dynamic structure topology graph.

2. The transmission monitoring method for an oil drilling rig chain as described in claim 1, characterized in that, The establishment of a multi-body flexible mechanical model for chain transmission based on the oil drilling rig chain includes: The oil drilling rig chain is decomposed into multiple original structures, including a chain link body, a sprocket body, a chain link connecting pin, a tensioning device, and a guide rail base. After modeling multiple original structures as rigid bodies and flexible bodies, the modeling results are assembled into a system on the MBS platform. The system assembly includes constructing the link connection relationship of the oil drilling rig chain through revolute joints and nonlinear contact joints. A multibody flexible mechanical model is established based on the system assembly results.

3. The transmission monitoring method for an oil drilling rig chain as described in claim 2, characterized in that, The process of using monitoring data as dynamic parameter input to the model, dynamically simulating the stress state of the chain links, and establishing a first transmission early warning based on the deviation between the simulated stress state and the actual stress state includes: Set the sprocket angular velocity input as the driving term, apply the initial preload of the tensioner, and perform the initialization of the flexible mechanical model; After inputting the monitoring data as model boundary parameters and disturbance terms into the multibody flexible mechanical model, the dynamic simulation of the stress state of the chain links is carried out through finite element simulation to establish the simulated stress state. By using the comparison module integrated into the multibody flexible mechanical model, the deviation between the simulated stress state and the actual state is compared, and a dynamic residual index is established. The first transmission warning is generated based on the dynamic residual index.

4. The transmission monitoring method for an oil drilling rig chain as described in claim 1, characterized in that, The establishment of physical sensitivity attention factors based on the identification results includes: The identification results are fitted with the force fluctuation amplitude, and a first interest coefficient is established based on the force fluctuation amplitude fitting results. Obtain the structural stiffness change rate of the identification result, and establish a second interest coefficient based on the structural stiffness change rate; Historical anomaly data is retrieved from the identification results to establish a third attention coefficient; After fusing the first attention coefficient, the second attention coefficient, and the third attention coefficient, the physical sensitivity attention factor is established.

5. The transmission monitoring method for an oil drilling rig chain as described in claim 1, characterized in that, The step of establishing a second transmission warning based on the first transmission warning using the updated dynamic structural topology graph includes: The first transmission warning is initially marked in the updated dynamic structure topology diagram; In the dynamic structural topology graph, the initial marker is used as the search center to search for K-order neighboring nodes, where K is an integer greater than 1; Calculate the average residual of the K-order neighboring nodes. If the average residual meets the dynamic threshold, update the first transmission warning to a chain segment warning and establish the second transmission warning based on the chain segment warning.

6. The transmission monitoring method for an oil drilling rig chain as described in claim 5, characterized in that, The establishment of the second transmission early warning based on the chain segment-level early warning includes: Update node weights using the updated dynamic topology graph; Structural diffusion simulation for first-stage transmission early warning based on heuristic traversal; Calculate the cumulative risk value of propagation at each node and establish a simulation-based early warning system; The simulation warning and the chain segment warning are verified to establish the second transmission warning.

7. The transmission monitoring method for an oil drilling rig chain as described in claim 1, characterized in that, The step of establishing a second transmission warning based on the first transmission warning using the updated dynamic structural topology graph also includes: Using the updated dynamic structural topology graph, anomalies in graph embedding distance, reconstruction error, and propagation path mutation indicators are identified based on monitoring data, and anomaly identification results are established. The second transmission warning is established based on the anomaly identification result and the first transmission warning.

8. The transmission monitoring method for an oil drilling rig chain as described in claim 1, characterized in that, The configuration of multi-mode sensors on the oil drilling rig chain includes: Obtain interference environment data of the oil drilling rig chain, perform interference intensity evaluation on the interference environment data, and establish interference intensity evaluation results; Determine whether the interference intensity evaluation result meets the interference threshold; If the interference intensity evaluation result cannot meet the interference threshold, then a multi-mode sensor is directly configured based on the oil drilling rig chain.

9. The transmission monitoring method for an oil drilling rig chain as described in claim 8, characterized in that, The step of determining whether the interference intensity evaluation result meets the interference threshold includes: If the interference intensity evaluation result meets the interference threshold, a redundancy configuration instruction is generated; Configure the multi-mode sensor of the oil drilling rig chain according to the redundancy configuration instruction, set the inter-mode verification mechanism, and complete the multi-mode sensor configuration according to the verification mechanism.

10. A transmission monitoring system for an oil drilling rig chain, characterized in that, The system is used to implement the transmission monitoring method for an oil drilling rig chain according to any one of claims 1 to 9, the system comprising: The dynamic structure topology graph creation module is used to extract key structures from the oil drilling rig chain and to create a dynamic structure topology graph based on the key structure extraction results and physical connection relationships. The monitoring mapping establishment module is used to configure multi-mode sensors on the oil drilling rig chain and establish the monitoring mapping of the multi-mode sensor monitoring nodes in the dynamic structural topology. The attribute feature update module is used to update the edge and point attribute features in the dynamic structural topology graph according to the monitoring mapping after collecting monitoring data using the multi-mode sensor. The first transmission early warning module is used to establish a multi-body flexible mechanical model of the chain transmission based on the oil drilling rig chain, input the monitoring data as the dynamic parameters of the model, dynamically simulate the stress state of the chain links, and establish the first transmission early warning based on the deviation between the simulated stress state and the actual state. The physical sensitivity concern factor establishment module is used to identify the chain tension center node, boundary node, and sprocket link node of the oil drilling rig chain, and establish physical sensitivity concern factors based on the identification results; The second transmission warning establishment module is used to update the self-identification weight of the dynamic structure topology graph through the physical sensitivity attention factor, and then establish a second transmission warning based on the first transmission warning through the updated dynamic structure topology graph.

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