Standard support hanger load compensation and monitoring operation and maintenance management system

Abstract

The invention discloses a standard support hanger load compensation and monitoring operation and maintenance management system, and relates to the technical field of constant-force and variable-force spring support hangers, and the system comprises an on-site monitoring layer which is used for carrying out the real-time collection of displacement, load and vibration at the constant-force and variable-force spring support hanger, and forming the state data of the support hanger; the network transmission layer is used for transmitting the support hanger state data to a data aggregation unit through a hybrid communication network and realizing stable power supply and communication; the platform application layer is used for carrying out storage, fusion analysis and state evaluation on the support hanger state data and outputting early warning information and load compensation suggestions, and the working method of the system comprises the steps of S1, deployment and calibration; displacement, load (strain gauge) and vibration are synchronously acquired based on support and hanger acquisition nodes, and data are gathered to a platform through LoRa and two-bus hybrid networking for multi-source fusion analysis, state evaluation and load compensation suggestion output.

Description

Technical Field

[0001] This invention relates to the field of constant force and variable force spring supports and hangers, specifically to a standard support and hanger load compensation and monitoring operation and maintenance management system. Background Technology

[0002] Constant force and variable force spring supports are used in power plant equipment, boiler water heating, and petrochemical pipeline systems to provide approximately constant support loads. Abnormal operation of these supports can impose additional loads on high-temperature and high-pressure pipelines and equipment, posing safety risks. Currently, most constant force and variable force spring supports are only manually inspected and adjusted before factory installation, lacking automated online monitoring after installation. Manual inspections are also prone to delays and significant errors.

[0003] Under complex operating conditions, the displacement, load, and vibration of constant force and variable force spring supports exhibit coupled changes. Typical failures are often related to vibration fatigue, manufacturing defects, and selection and installation errors. Discrete inspections of single indicators are insufficient to reveal the risk evolution process in advance. On the other hand, difficulties in on-site wiring and power supply, as well as insufficient communication stability, make it difficult to implement continuous data collection and unified analysis from multiple suspension points, thus failing to form quantitative basis and handling recommendations for maintenance and adjustment. Summary of the Invention

[0004] The purpose of this invention is to provide a standard support and hanger load compensation and monitoring operation and maintenance management system to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a standard support and hanger load compensation and monitoring operation and maintenance management system, including a field monitoring layer, used to collect displacement, load and vibration in real time at constant force and variable force spring supports and hangers and form support and hanger status data; The network transmission layer is used to transmit the status data of the support bracket to the data aggregation unit through a hybrid communication network and to achieve stable power supply and communication. The platform application layer is used to store, fuse, analyze, and assess the status data of the supports and hangers, and output early warning information and load compensation suggestions.

[0006] According to the above technical solution, the field monitoring layer includes acquisition nodes, displacement sensors, strain gauges, vibration sensors, signal conditioning units, time synchronization units, and low-power power management units. The acquisition nodes are used to carry out sensor access and data sampling, the displacement sensors are used to acquire the displacement of the supports and hangers, the strain gauges are used to acquire the load characterization signals of the supports and hangers, the vibration sensors are used to acquire the mechanical vibration signals of the supports and hangers, the signal conditioning units are used to amplify and filter the sensor signals, the time synchronization units are used to time-align the multi-source data, and the low-power power management units are used to reduce the power consumption of the acquisition nodes and ensure continuous operation. The network transmission layer includes a LoRa wireless communication unit, a two-bus communication and power supply unit, and a data aggregation unit. The LoRa wireless communication unit is used for long-distance, low-power data transmission to nodes with difficult wiring. The two-bus communication and power supply unit is used to centrally power the front-end sensors and complete data communication. The data aggregation unit is used to aggregate the uploaded data from LoRa and the two-bus and forward it to the platform application layer. The platform application layer includes a data storage unit, an equipment management unit, a threshold early warning unit, an intelligent diagnosis and status assessment unit, a visualization unit, and a report output unit. The data storage unit stores historical monitoring data, the equipment management unit manages the acquisition nodes and communication links, the threshold early warning unit triggers alarms under abnormal operating conditions, the intelligent diagnosis and status assessment unit performs health assessments based on historical data and fault characteristics and generates load compensation suggestions, the visualization unit provides display interfaces for computer terminals and mobile terminals, and the report output unit outputs inspection and maintenance decision reports.

[0007] According to the above technical solution, the working method of this system includes: S1. Deployment and Calibration: Install acquisition nodes at constant force and variable force spring supports and connect displacement sensors, strain gauges and vibration sensors; complete the initial configuration of zero point, range and communication link; form a benchmark state record for subsequent evaluation.

[0008] S2. Continuous Acquisition and Transmission: The acquisition nodes continuously acquire displacement, load characterization signals and vibration signals and mark them with a unified time stamp; the data is then uploaded to the data aggregation unit via LoRa wireless link and two-bus link; the data aggregation unit forwards the data to the platform application layer.

[0009] S3. Data Preprocessing and Feature Construction: The platform performs integrity verification on uploaded data and performs noise suppression and drift correction; it constructs feature sequences for anomaly identification based on displacement, load and vibration; and it forms a temporal state profile of the support and hanger.

[0010] S4. Intelligent Diagnosis and Status Assessment: The platform assesses the health status of supports and hangers based on historical data and fault characteristics, identifies abnormal working conditions, classifies abnormal types, and correlates the changes in displacement, load, and vibration; and outputs fault warning results and risk levels.

[0011] S5. Compensation suggestion output and closed-loop recording: The platform generates load compensation suggestions and maintenance and disposal suggestions for lifting points based on the evaluation results; it forms traceable records of alarms and suggestions and outputs reports; it compares and records the disposal effects in subsequent data collection to support verification.

[0012] According to the above technical solution, S1 specifically refers to: S1-1, Range and Sampling Configuration: Configure the displacement measurement range to 0mm to 1250mm, the load measurement range to 0 tons to 20 tons and corresponding to 0kN to 200kN, and the sampling frequency to 1 to 60 times per minute. The displacement range is used to limit the effective measurement range of the displacement sensor, the load range is used to limit the effective conversion range of the strain gauge measurement link, and the sampling frequency is used to limit the data update frequency of the acquisition node. S1-2, Load conversion calibration: For the first Each support bracket is in constant motion The strain values ​​output by the acquired strain gauges are denoted as: The converted load is denoted as And adopt the calibration relationship ,in For the first The calibration factor for strain to load of each support and hanger. Zero-point offset; S1-3, Zero-point displacement calibration: ... Each support bracket is in constant motion The collected displacement is denoted as The zero-point calibration value is recorded as And obtain the displacement value after zero-point correction. .

[0013] According to the above technical solution, S2 specifically refers to: S2-1, Time Alignment: Mapping the sampling results of displacement, load, and vibration to a unified time series. ,in For sampling sequence number, For the first The next sampling time; S2-2, Sampling Interval: Define the sampling interval And according to the sampling frequency Limited to the range of 1 second to 60 seconds, among which Used to constrain the temporal resolution of continuous sampling; S2-3, under a unified time series Uploaded to the data aggregation unit, where For the first Each support bracket is in constant motion The vibration acceleration is measured. The upload link includes a LoRa wireless link and a two-bus link. The data aggregation unit aggregates the data from the two types of links and forwards it to the platform application layer.

[0014] According to the above technical solution, S3 specifically refers to: S3-1, Filtered Load: For the load sequence Apply the filtering operator to obtain the filtered load; S3-2, Vibration Intensity Characteristics: Set the window length to... The sliding window, and calculate the first... Each support bracket is in constant motion The root mean square of the vibration, ,in Characteristics of vibration intensity; S3-3, Construction of Early Warning Input: [This will...] , and Composition of state vector The state vector is then input into the intelligent diagnosis and state assessment unit for anomaly identification and graded early warning.

[0015] According to the above technical solution, S4 specifically refers to: S4-1, System Resultant Force: For the same moment Down The resultant force of the system is obtained by summing the filtered loads of each support and hanger: ; S4-2, Contribution Rate: Calculate the... Load contribution rate of each support and hanger This is used to characterize the proportion of the support and hanger in the resultant force of the system; S4-3, Redistribution Decision: In a length of... Calculate the average contribution rate within the evaluation interval And calculate the change in contribution rate. ,in The mean of the previous sub-interval. For the mean of the subsequent sub-intervals, when there exists And exist When determining whether load redistribution exists in the system, To determine the threshold, and Number the different supports and hangers.

[0016] According to the above technical solution, S5 specifically refers to: S5-1, Target Load: For the first Each support and hanger generates the target load. and make Falling into the allowed range ,in and The lower and upper limits of the system's combined force are allowed. S5-2, Compensation Recommendation: Calculate Load Deviation ; S5-3, Suggested Mapping and Compliance Verification: [The following text appears to be incomplete and requires further By suggesting mapping functions Convert into recommendations for maintenance and adjustment ,in The mapping function, determined based on calibration data, was obtained through retesting after maintenance procedures were performed. Calculate the target indicators ,when The time was used to determine whether the compensation recommendation met the standard, among which An allowable error threshold is set, and the results of achieving the target are written into a closed-loop record to support subsequent condition assessment and lifetime prediction.

[0017] A standard support bracket includes a housing, lugs, a spring assembly, a swing arm, a connecting beam, a main compression spring, a guide rod, a lower bearing seat, a lower connecting rod, and a lower connector. A displacement sensor is fixed in a rigid fixed area inside or outside the housing, with its fixed end not moving with the mechanism. Its measuring end is fixed to the lower bearing seat and connected to its edge. The measurement direction is along the axial direction of the guide rod, and the output corresponds to the vertical displacement of the lower bearing seat. A strain gauge is attached to the outer surface of the smooth section of the lower connecting rod, located below the lower bearing seat and above the lower connector on the load transmission path. The strain gauge's sensitive grid direction is arranged along the axial direction of the lower connecting rod, used to measure axial strain and convert it to load. A vibration sensor is fixed to a rigid plane area outside the housing, near the lugs. The Z-axis of the triaxial vibration sensor is aligned with the axial direction of the lower connecting rod, used to extract the vertical main vibration component while retaining the lateral component for anomaly identification.

[0018] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention uses a three-layer architecture of field monitoring layer, network transmission layer and platform application layer to continuously collect and uniformly analyze displacement, load (load) and vibration, realize real-time monitoring, fault warning and health assessment of the working status of constant force and variable force spring supports, and reduce the risk of missed detection caused by untimely manual inspection and errors. By combining power supply and communication via two buses and using LoRa for long-distance, low-power transmission, the platform improves deployment feasibility and communication reliability in areas with concentrated pipelines, inconvenient power supply, and difficult wiring. Based on historical data and fault characteristics, the platform outputs status assessment results and load compensation suggestions for lifting points, providing quantitative basis for maintenance, adjustment, and replacement, thereby reducing the probability of major accidents and lowering installation and maintenance costs. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the overall modular structure of the present invention; Figure 2 This is a schematic diagram of the standard support and hanger structure of the present invention. Detailed Implementation

[0020] 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.

[0021] Please see Figure 1 and Figure 2 The present invention provides a technical solution: a standard support and hanger load compensation and monitoring operation and maintenance management system, including a field monitoring layer, used to collect displacement, load and vibration in real time at constant force and variable force spring supports and hangers and form support and hanger status data; The network transmission layer is used to transmit the status data of the support and hanger to the data aggregation unit through a hybrid communication network and to achieve stable power supply and communication. The platform application layer is used to store, integrate, analyze, and assess the status data of supports and hangers, and output early warning information and load compensation suggestions. The field monitoring layer includes acquisition nodes, displacement sensors, strain gauges, vibration sensors, signal conditioning units, time synchronization units, and low-power power management units. The acquisition nodes are used to handle sensor access and data sampling, the displacement sensors are used to acquire the displacement of the supports and hangers, the strain gauges are used to acquire the load characterization signals of the supports and hangers, the vibration sensors are used to acquire the mechanical vibration signals of the supports and hangers, the signal conditioning units are used to amplify and filter the sensor signals, the time synchronization units are used to time-align the multi-source data, and the low-power power management units are used to reduce the power consumption of the acquisition nodes and ensure continuous operation. The network transmission layer includes a LoRa wireless communication unit, a two-bus communication and power supply unit, and a data aggregation unit. The LoRa wireless communication unit is used for long-distance low-power data transmission to nodes with difficult wiring. The two-bus communication and power supply unit is used to centrally power the front-end sensors and complete data communication. The data aggregation unit is used to aggregate the uploaded data from LoRa and the two-bus and forward it to the platform application layer. The platform application layer includes a data storage unit, an equipment management unit, a threshold early warning unit, an intelligent diagnosis and status assessment unit, a visualization unit, and a report output unit. The data storage unit stores historical monitoring data, the equipment management unit manages the acquisition nodes and communication links, the threshold early warning unit triggers alarms under abnormal operating conditions, the intelligent diagnosis and status assessment unit performs health assessments based on historical data and fault characteristics and generates load compensation suggestions, the visualization unit provides display interfaces for computer terminals and mobile terminals, and the report output unit outputs inspection and maintenance decision reports. The working method of this system includes: S1. Deployment and Calibration: Install acquisition nodes at constant force and variable force spring supports and connect displacement sensors, strain gauges and vibration sensors; complete the initial configuration of zero point, range and communication link; form a benchmark state record for subsequent evaluation.

[0022] S2. Continuous Acquisition and Transmission: The acquisition nodes continuously acquire displacement, load characterization signals and vibration signals and mark them with a unified time stamp; the data is then uploaded to the data aggregation unit via LoRa wireless link and two-bus link; the data aggregation unit forwards the data to the platform application layer.

[0023] S3. Data Preprocessing and Feature Construction: The platform performs integrity verification on uploaded data and performs noise suppression and drift correction; it constructs feature sequences for anomaly identification based on displacement, load and vibration; and it forms a temporal state profile of the support and hanger.

[0024] S4. Intelligent Diagnosis and Status Assessment: The platform assesses the health status of supports and hangers based on historical data and fault characteristics, identifies abnormal working conditions, classifies abnormal types, and correlates the changes in displacement, load, and vibration; and outputs fault warning results and risk levels.

[0025] S5. Compensation Recommendation Output and Closed-Loop Recording: Based on the evaluation results, the platform generates load compensation recommendations and maintenance handling recommendations for lifting points; it creates traceable records of alarms and recommendations and outputs reports; and it compares and records the handling effects in subsequent data collection to support verification. S1 specifically refers to: S1-1, Range and Sampling Configuration: Configure the displacement measurement range to 0mm to 1250mm, the load measurement range to 0 tons to 20 tons and corresponding to 0kN to 200kN, and the sampling frequency to 1 to 60 times per minute. The displacement range is used to limit the effective measurement range of the displacement sensor, the load range is used to limit the effective conversion range of the strain gauge measurement link, and the sampling frequency is used to limit the data update frequency of the acquisition node. S1-2, Load conversion calibration: For the first Each support bracket is in constant motion The strain values ​​output by the acquired strain gauges are denoted as: The converted load is denoted as And adopt the calibration relationship ,in For the first The calibration factor for strain to load of each support and hanger. Zero-point offset; S1-3, Zero-point displacement calibration: ... Each support bracket is in constant motion The collected displacement is denoted as The zero-point calibration value is recorded as And obtain the displacement value after zero-point correction. ; Conventional practices often involve measuring only a single quantity on external pipes or suspension points (e.g., displacement or load), or relying primarily on manual reading of travel scales and manual force measurement. This leads to a disconnect between the signal and the internal mechanism of the standard support, making it difficult to determine whether the load has truly changed or whether the reading is spurious due to mechanism jamming / hysteresis. This solution places the fixed end of the displacement sensor in the rigid area of ​​the housing and binds the value-taking end to the travel of the moving seat; it places the strain gauge on the smooth section of the straight rod under axial force on the output rod; and it fixes the triaxial vibration sensor on the rigid plane of the housing with the Z-axis aligned with the output axis. Structurally, this locks x (travel), F (output load characterization), and a (vibration excitation / response) onto the key force and motion link of the same standard support, achieving synchronous characterization of the actual force at the output end, the actual movement of the mechanism, and the intensity of the external excitation. This step serves as an anchor for data reliability and interpretability in this scheme: all subsequent diagnostic, modeling, and compensation recommendations are based on the co-location, co-direction, and same kinematic pair correlation of these three data sources; its originality lies in not simply attaching the sensor, but using the force path and guide kinematic chain of the standard support as a benchmark to provide reproducible landing point and direction constraints, thereby upgrading the monitoring from external phenomenon measurement to observable mechanism state.

[0026] S2 specifically refers to: S2-1, Time Alignment: Mapping the sampling results of displacement, load, and vibration to a unified time series. ,in For sampling sequence number, For the first The next sampling time; S2-2, Sampling Interval: Define the sampling interval And based on the sampling frequency Limited to the range of 1 second to 60 seconds, among which Used to constrain the temporal resolution of continuous sampling; S2-3, under a unified time series Uploaded to the data aggregation unit, where For the first Each support bracket is in constant motion The vibration acceleration, the upload link includes LoRa wireless link and two bus link, the data aggregation unit aggregates the data from the two types of links and forwards them to the platform application layer; Conventional methods typically set fixed threshold alarms for loads or displacements. When vibration shocks occur, transient peak values ​​easily trigger false alarms. Conversely, slow degradation caused by jamming or fatigue may be drowned out by noise, resulting in either too many alarms or no alarms when they should. This solution uses vibration intensity characteristics as a condition-gated signal: when strong vibration is detected, the diagnostic strategy automatically switches to an impact / transient resistance assessment method (e.g., emphasizing trend consistency and continuity, reducing sensitivity to single-point peak values); when weak vibration occurs, a high-resolution drift / hysteresis identification method is used (e.g., emphasizing displacement-load consistency and the accumulation of small deviations). The working principle of this step is to use 'a' (vibration intensity) to determine the signal confidence window, splitting the same threshold system into two sets of condition-adaptive judgment logics. Its role in the solution is to significantly reduce false alarms and improve the detection rate of slow-variable anomalies such as jamming, attenuation, and installation deviations. Its originality lies in treating vibration not as an isolated monitoring quantity, but as a scheduling quantity for diagnostic logic, forming a closed loop of consistent condition identification, strategy switching, and threshold interpretation, enabling the system to stably output usable conclusions even under strong disturbance environments.

[0027] S3 specifically refers to: S3-1, Filtered Load: For the load sequence Apply the filtering operator to obtain the filtered load; S3-2, Vibration Intensity Characteristics: Set the window length to... The sliding window, and calculate the first... Each support bracket is in constant motion The root mean square of the vibration, ,in Characteristics of vibration intensity; S3-3, Construction of Early Warning Input: [This will...] , and Composition of state vector The state vector is then input into the intelligent diagnosis and state assessment unit for anomaly identification and graded early warning. Conventional methods often judge overload based on individual standard supports: as long as one support does not exceed the rated range, it is considered normal. However, in large piping systems / equipment suspended by multiple points, the load will migrate between the points. This may lead to hidden risks where a single support appears normal, but the system is actually unbalanced, or even cause localized overloads to go undetected for a long time. This solution integrates data from multiple points simultaneously on the platform side: first, it establishes constraints and comparison benchmarks at the system's resultant force level; then, it tracks the changing trend of each point's proportion in the system's resultant force, identifying migration patterns where some points are continuously rising and others are continuously falling, thereby locating the direction of load redistribution and the relevant set of points. The principle behind this step is to upgrade the assessment of each standard support from absolute value to relative contribution and trend assessment, capturing the coupling relationship at the group level. Its function is to discover system-level hidden dangers that cannot be covered by single-machine thresholds, and to provide a basis for subsequent compensation recommendations on which support should be adjusted first and to what extent. The unique feature is that it extends the monitoring of standard supports from the equipment level to the system level, using group consistency and contribution rate trends to identify non-local and non-linear fault symptoms such as load migration, which is difficult to achieve with conventional single-point monitoring.

[0028] S4 specifically refers to: S4-1, System Resultant Force: For the same moment Down The resultant force of the system is obtained by summing the filtered loads of each support and hanger: ; S4-2, Contribution Rate: Calculate the... Load contribution rate of each support and hanger This is used to characterize the proportion of the support and hanger in the resultant force of the system; S4-3, Redistribution Decision: In a length of... Calculate the average contribution rate within the evaluation interval And calculate the change in contribution rate. ,in The mean of the previous sub-interval. For the mean of the subsequent sub-intervals, when there exists And exist When determining whether load redistribution exists in the system, To determine the threshold, and Number the different supports and hangers; Conventional solutions, even if they can identify anomalies, often only provide qualitative suggestions for repair / adjustment, lacking actionable adjustment amounts and sequences. On-site adjustments still require repeated trial and error based on experience, easily leading to secondary imbalances: adjusting point A may passively alter point B, ultimately resulting in more confusion. This solution further transforms diagnostic output into actionable compensation suggestions: first, it provides the target load range or target state for each lifting point; then, based on the calibrated compensation demand-adjustment mapping table, it outputs operable adjustment amounts (e.g., the equivalent rotation of the adjusting nut or preload displacement); and combines this with the load redistribution results to plan the adjustment sequence (prioritizing lifting points with abnormally increased contribution or higher risk). Finally, it verifies compliance through retesting and forms a closed-loop record. Its working principle extends monitoring and diagnosis to the complete chain of decision-making, execution, and verification; in the solution, it plays the role of turning data value into operation and maintenance actions, significantly reducing trial and error costs; its originality lies in transforming compensation suggestions from qualitative information into quantitative suggestions with execution units, using sequential planning to suppress secondary coupling disturbances, and then using retesting to form a closed-loop evidence chain, so that the system has the self-consistent adjustment and guidance capabilities that can be used in engineering, rather than remaining at the monitoring and display level.

[0029] S5 specifically refers to: S5-1, Target Load: For the first Each support and hanger generates the target load. and make Falling into the allowed range ,in and The lower and upper limits of the system's combined force are allowed. S5-2, Compensation Recommendation: Calculate Load Deviation ; S5-3, Suggested Mapping and Compliance Verification: [The following text appears to be incomplete and requires further By suggesting mapping functions Convert into recommendations for maintenance and adjustment ,in The mapping function, determined based on calibration data, was obtained through retesting after maintenance procedures were performed. Calculate the target indicators ,when The time was used to determine whether the compensation recommendation met the standard, among which An allowable error threshold is set, and the results of achieving the target are written into a closed-loop record to support subsequent condition assessment and lifetime prediction.

[0030] A standard support bracket includes a housing 1, a lifting lug 2, a spring assembly 3, a swing arm 6, a connecting beam 7, a main compression spring 8, a guide rod 9, a lower support 10, a lower connecting rod 11, and a lower connector 12. A displacement sensor is fixed in a rigid fixing area inside or outside the housing 1, with its fixed end not moving with the mechanism. The measuring end is fixed to the lower support 10 and connected to its edge. The measurement direction is along the axial direction of the guide rod 9 along the measuring axis of the displacement sensor, and the output corresponds to the vertical displacement of the lower support 10. The strain gauge is attached to the outer surface of the smooth section of the lower connecting rod 11. This smooth section is located on the load transmission path below the lower bearing seat 10 and above the lower connector 12. The strain gauge's sensitive grid direction is arranged along the axial direction of the lower connecting rod 11 to measure axial strain and convert load. The vibration sensor is fixed to the rigid plane area on the outside of the housing 1, near the lifting lug 2. The Z-axis of the triaxial vibration sensor is aligned with the axial direction of the lower connecting rod 11 to extract the vertical main vibration component, while retaining the lateral component for anomaly identification.

[0031] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "include," "contain," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0032] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A standard support and hanger load compensation and monitoring operation and maintenance management system, characterized in that: include: The on-site monitoring layer is used to collect displacement, load and vibration data in real time at constant force and variable force spring supports and generate support status data. The network transmission layer is used to transmit the status data of the support bracket to the data aggregation unit through a hybrid communication network and to achieve stable power supply and communication. The platform application layer is used to store, fuse, analyze, and assess the status data of the supports and hangers, and output early warning information and load compensation suggestions.

2. The standard support and hanger load compensation and monitoring operation and maintenance management system according to claim 1, characterized in that: The field monitoring layer includes acquisition nodes, displacement sensors, strain gauges, vibration sensors, signal conditioning units, time synchronization units, and low-power power management units. The acquisition nodes are used to handle sensor access and data sampling, the displacement sensors are used to acquire the displacement of the supports and hangers, the strain gauges are used to acquire the load characterization signals of the supports and hangers, the vibration sensors are used to acquire the mechanical vibration signals of the supports and hangers, the signal conditioning units are used to amplify and filter the sensor signals, the time synchronization units are used to time-align multi-source data, and the low-power power management units are used to reduce the power consumption of the acquisition nodes and ensure continuous operation. The network transmission layer includes a LoRa wireless communication unit, a two-bus communication and power supply unit, and a data aggregation unit. The LoRa wireless communication unit is used for long-distance, low-power data transmission to nodes with difficult wiring. The two-bus communication and power supply unit is used to centrally power the front-end sensors and complete data communication. The data aggregation unit is used to aggregate the uploaded data from LoRa and the two-bus and forward it to the platform application layer. The platform application layer includes a data storage unit, an equipment management unit, a threshold early warning unit, an intelligent diagnosis and status assessment unit, a visualization unit, and a report output unit. The data storage unit stores historical monitoring data, the equipment management unit manages the acquisition nodes and communication links, the threshold early warning unit triggers alarms under abnormal operating conditions, the intelligent diagnosis and status assessment unit performs health assessments based on historical data and fault characteristics and generates load compensation suggestions, the visualization unit provides display interfaces for computer terminals and mobile terminals, and the report output unit outputs inspection and maintenance decision reports.

3. The standard support and hanger load compensation and monitoring operation and maintenance management system according to claim 2, characterized in that: The working method of this system includes: S1. Deployment and Calibration: Install acquisition nodes at constant force and variable force spring supports and connect displacement sensors, strain gauges and vibration sensors; complete the initial configuration of zero point, range and communication link; form a benchmark state record for subsequent evaluation. S2. Continuous Acquisition and Transmission: The acquisition nodes continuously acquire displacement, load characterization signals and vibration signals and mark them with a unified time stamp; the data is then uploaded to the data aggregation unit via LoRa wireless link and two-bus link; the data aggregation unit forwards the data to the platform application layer. S3. Data Preprocessing and Feature Construction: The platform performs integrity verification on uploaded data and performs noise suppression and drift correction; it constructs feature sequences for anomaly identification based on displacement, load and vibration; and it forms a temporal state profile of the support and hanger. S4. Intelligent Diagnosis and Status Assessment: The platform assesses the health status of supports and hangers based on historical data and fault characteristics, identifies abnormal working conditions, classifies abnormal types, and correlates the changes in displacement, load, and vibration; and outputs fault warning results and risk levels. S5. Compensation suggestion output and closed-loop recording: The platform generates load compensation suggestions and maintenance and disposal suggestions for lifting points based on the evaluation results; it forms traceable records of alarms and suggestions and outputs reports; it compares and records the disposal effects in subsequent data collection to support verification.

4. The standard support and hanger load compensation and monitoring operation and maintenance management system according to claim 3, characterized in that: Specifically, S1 is: S1-1, Range and Sampling Configuration: Configure the displacement measurement range to 0mm to 1250mm, the load measurement range to 0 tons to 20 tons and corresponding to 0kN to 200kN, and the sampling frequency to 1 to 60 times per minute. The displacement range is used to limit the effective measurement range of the displacement sensor, the load range is used to limit the effective conversion range of the strain gauge measurement link, and the sampling frequency is used to limit the data update frequency of the acquisition node. S1-2, Load conversion calibration: For the first Each support bracket at all times The strain values ​​output by the acquired strain gauges are denoted as The converted load is denoted as And adopt the calibration relationship ,in For the first The calibration factor for strain to load of each support and hanger. Zero-point offset; S1-3, Zero-point displacement calibration: ... Each support bracket at all times The collected displacement is denoted as The zero-point calibration value is recorded as And obtain the displacement value after zero-point correction. .

5. The standard support and hanger load compensation and monitoring operation and maintenance management system according to claim 4, characterized in that: Specifically, S2 is: S2-1, Time Alignment: Mapping the sampling results of displacement, load, and vibration to a unified time series. ,in For sampling sequence number, For the first The next sampling time; S2-2, Sampling Interval: Define the sampling interval And according to the sampling frequency Limited to the range of 1 second to 60 seconds, among which Used to constrain the temporal resolution of continuous sampling; S2-3, under a unified time series Uploaded to the data aggregation unit, where For the first Each support bracket at all times The vibration acceleration is measured. The upload link includes a LoRa wireless link and a two-bus link. The data aggregation unit aggregates the data from the two types of links and forwards it to the platform application layer.

6. The standard support and hanger load compensation and monitoring operation and maintenance management system according to claim 5, characterized in that: Specifically, S3 is: S3-1, Filtered Load: For the load sequence Apply the filtering operator to obtain the filtered load; S3-2, Vibration Intensity Characteristics: Set the window length to... The sliding window, and calculate the first... Each support bracket at all times The root mean square of the vibration, ,in Characteristics of vibration intensity; S3-3, Construction of Early Warning Input: [This will...] , and Composition of state vector The state vector is then input into the intelligent diagnosis and state assessment unit for anomaly identification and graded early warning.

7. The standard support and hanger load compensation and monitoring operation and maintenance management system according to claim 6, characterized in that: Specifically, S4 is: S4-1, System Resultant Force: For the same moment Down The resultant force of the system is obtained by summing the filtered loads of each support and hanger: ; S4-2, Contribution Rate: Calculate the... Load contribution rate of each support and hanger This is used to characterize the proportion of the support and hanger in the resultant force of the system; S4-3, Redistribution Decision: In a length of... Calculate the average contribution rate within the evaluation interval And calculate the change in contribution rate. ,in The mean of the previous sub-interval. For the mean of the subsequent sub-intervals, when there exists And exist When determining whether load redistribution exists in the system, To determine the threshold, and Number the different supports and hangers.

8. The standard support and hanger load compensation and monitoring operation and maintenance management system according to claim 7, characterized in that: Specifically, S5 is: S5-1, Target Load: For the first Each support and hanger generates the target load. and make Falling into the allowed range ,in and The lower and upper limits of the system's combined force are allowed. S5-2, Compensation Recommendation: Calculate Load Deviation ; S5-3, Suggested Mapping and Compliance Verification: [The following text appears to be incomplete and requires further By suggesting mapping functions Convert into recommendations for maintenance and adjustment ,in The mapping function, determined based on calibration data, was obtained through retesting after maintenance procedures were performed. Calculate the target indicators ,when The time was used to determine whether the compensation recommendation met the standard, among which An allowable error threshold is set, and the results of achieving the target are written into a closed-loop record to support subsequent condition assessment and lifetime prediction.

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