A cloud-based hanging basket contraction state detection system

Abstract

The present application relates to the technical field of state detection, in particular to a hanging basket contraction state detection system based on cloud, which comprises a data reporting module, a time sequence verification module, a contraction detection module, a trend inference module and an event index module. Based on the key nodes of the hanging basket, the displacement sensor monitoring data is analyzed, and the time characteristics of the collected data are identified item by item. In the present application, the multi-source monitoring data of each node of the hanging basket is automatically time-labeled on site, and then is collected to the cloud platform through wireless mode. The data is efficiently screened and standardized time-sequenced verified in the cloud, the contraction state change is automatically identified through dynamic trend analysis in the continuous period, the structural linkage anomaly and the attribution characteristics are classified, the event correlation index and the spatial position mapping are supported, the multi-level data archiving strengthens the information comparison and tracking between cross nodes, and the digitalization and intelligent management level of the whole process of the bridge hanging basket contraction are improved.

Description

Technical Field

[0001] This invention relates to the field of condition detection technology, and in particular to a cloud-based hanging basket retraction condition detection system. Background Technology

[0002] The field of condition monitoring involves real-time monitoring and information collection of the operating status, performance parameters, and working environment of various equipment or structures, and is widely used in industrial automation, transportation, and engineering construction. Among these, the traditional hanging basket contraction condition monitoring system refers to the use of sensors to measure changes in the length of the steel wire rope during bridge construction. The physical quantities collected by the sensors are periodically read manually, and the data is recorded and managed using local displays or data loggers. This allows for manual statistical analysis of changes in the hanging basket structure, completing the information collection and management of the hanging basket contraction status.

[0003] Existing technologies for monitoring the contraction of hanging baskets mainly rely on manual reading and local equipment recording. The data collection methods are scattered, and the data consistency is poor. Data from some nodes is prone to delays, omissions, or incomplete records, resulting in a lack of unified analysis conditions. Local anomalies are difficult to detect and report in a timely manner, and information flow is fragmented. This makes it difficult to adapt to the dynamic monitoring needs of multiple nodes and long cycles. Furthermore, the statistical process of abnormal events is prone to missed detections and misjudgments, affecting engineering safety management and intelligent decision-making. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a cloud-based basket shrinkage state detection system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a cloud-based hanging basket retraction state detection system, the system comprising:

[0006] The data reporting module is based on the key nodes of the hanging basket. It analyzes the monitoring data of the displacement sensor, identifies the time characteristics of the collected data, adds time stamps to the collected data, and pushes the data to the cloud platform in node order through the wireless communication unit to obtain unified time-series collected data.

[0007] The timing verification module filters the time stamps of each node based on the unified timing data, determines the sampling time interval, and removes data frames that do not conform to the sampling benchmark to obtain a standard timing data group.

[0008] Based on the standard time-series data set, the shrinkage detection module judges the changing trend of the shrinkage rate of the hanging basket node, analyzes the rate change within a continuous period, and retrieves displacement and tension data after detecting an anomaly. Combined with the linkage between the anchoring zone and the sliding unit, it obtains key shrinkage anomaly characteristics.

[0009] Based on the key contraction anomaly characteristics, the trend inference module compares displacement, tension and temperature data within the anomaly period, analyzes the trend and synchronicity of change, determines the temperature classification, and obtains the attribution trend index.

[0010] Based on the attribution trend index, the event index module calls the monitoring data within the abnormal period, combines the spatial distribution of force transmission nodes and tension control units, and indexes the information in a unified manner to obtain abnormal association information.

[0011] The present invention improves upon the following: the unified time-series acquisition data includes a synchronization data identifier, sampling start and end time, and spatial location identifier; the standard time-series data group includes a data sequence index, time period coverage, and data synchronization accuracy; the key contraction anomaly features include rate change distribution, anomaly trend type, and structural linkage level; the attribution trend index includes anomaly attribution category, synchronization trend coefficient, and temperature influence factor; and the anomaly association information includes event retrieval code, archive node marker, and spatial location information.

[0012] The present invention is improved in that the data reporting module includes:

[0013] The data stream receiving submodule is based on the key nodes of the hanging basket. It analyzes the monitoring content of the displacement sensor collected, identifies the node code and measurement parameters in the message, determines whether there are missing items in the sampled data, identifies data content with complete fields, and completes the information of each group of data according to the spatial location and collection time to obtain the spatiotemporal sampling set of the node.

[0014] The time stamping submodule performs time stamping processing on each group of data based on the spatiotemporal sampling set of the nodes and a unified clock reference, and adjusts the temporal structure of the stamped data to obtain a standardized time series dataset.

[0015] The wireless push management submodule, based on the standardized time series dataset, determines the node arrangement and time stamp structure, configures communication channel parameters, sequentially pushes the node encoding, measurement parameters and unified time stamps in the data, analyzes the transmission link status and removes abnormal segments to obtain unified time series acquisition data.

[0016] The present invention is improved in that the timing verification module includes:

[0017] The time stamp extraction submodule identifies the time stamp parameters in each data frame based on the unified time-series collected data, determines whether the time stamp format is consistent, verifies the integrity of the field content, identifies data frames that meet the time-series consistency standard, and obtains a consistent time stamp set.

[0018] The sampling interval determination submodule calculates the time interval between adjacent data frames based on the consistent time stamp set, compares the offset of the time interval of each node with the unified clock reference, filters data frames within the allowable range, removes segments with abnormal time intervals, and obtains the synchronization time interval sequence.

[0019] The sequence number re-encoding management submodule adjusts the order of each data frame based on the synchronization time interval sequence, rearranges the sequence numbering according to the time sequence and node spatial order, optimizes the data index structure, and obtains a standard time-series data group.

[0020] The present invention is improved in that the shrinkage detection module includes:

[0021] The rate trend extraction submodule analyzes the continuous periodic displacement data of each hanging basket node based on the standard time series data group, calculates the contraction rate change of each node in adjacent time periods, determines the direction and magnitude of rate change within the period, identifies nodes whose rate change is higher than a preset change threshold, and obtains a rate anomaly identifier.

[0022] The node linkage comparison submodule compares the displacement direction and tension change trend of key nodes and adjacent nodes in the same period based on the rate anomaly identifier, determines the displacement consistency and tension reversal relationship between spatially adjacent nodes, and obtains the linkage consistency identifier.

[0023] The anomaly feature identification submodule, based on the linkage consistency identifier and combined with the response behavior of the contraction section hanging basket anchorage area and the guide rail sliding unit, filters out nodes with enhanced response linkage to obtain key contraction anomaly features.

[0024] The present invention is improved in that the trend inference module includes:

[0025] The trend direction identification submodule, based on the key contraction anomaly features, collects displacement monitoring data, wire rope tension monitoring data, and temperature monitoring data within the anomaly period, determines the time change trend of each monitoring data, compares the change direction of the displacement curve and tension curve, and combines the fluctuation of the temperature monitoring curve to obtain a multi-source monitoring change trend vector group.

[0026] The synchronization relationship judgment submodule compares the direction of change of displacement trend and tension trend based on the multi-source monitoring change trend vector group, judges the synchronization based on whether the two changes are consistent within the time series, filters out time segments with asynchronous changes, and analyzes the distribution characteristics of synchronization state to obtain trend synchronization matching index.

[0027] The temperature attribution classification submodule, based on the trend synchronization matching index and the trend of the temperature curve within the abnormal period, determines the degree of correlation between temperature changes and trend synchronization status, adjusts the correspondence between temperature changes and tension changes within the synchronization segment, and obtains the attribution trend index.

[0028] The present invention is improved in that the event index module includes:

[0029] Based on the attribution trend index, the source node data retrieval submodule determines the triggering source node and its adjacent nodes within the abnormal period, judges whether the displacement monitoring data and tension monitoring data fields of each node are complete within the abnormal period, identifies node data with complete data fields, and obtains the abnormal node monitoring information group.

[0030] Based on the abnormal node monitoring information group, the spatial structure matching submodule combines the spatial distribution of the force transmission nodes of the hanging basket anchorage section and the wire rope tension control unit to determine the spatial relationship and connection path between nodes, organize the mapping sequence of each node in the structural system, summarize the spatial linkage relationship, and obtain the force transmission structure association sequence.

[0031] The association index construction submodule adjusts the sampling number, time stamp, and spatial location code of the abnormal node monitoring information group according to the force transmission structure association sequence, arranges them into a unified retrieval field, archives all monitoring content within the period, assigns structured retrieval tags, and obtains abnormal association information.

[0032] The present invention is improved in that the key node of the hanging basket refers to the position on the hanging basket structure that plays a role in safety, deformation monitoring or force control, and the shrinkage rate of the hanging basket node refers to the rate of change of the shrinkage length of the key node of the hanging basket per unit time during the shrinkage control process.

[0033] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0034] In this invention, multi-source monitoring data from each node of the hanging basket are automatically time-stamped on-site and then uniformly collected to the cloud platform wirelessly. The data is efficiently filtered and standardized for time sequence verification in the cloud. Changes in the contraction status are automatically identified through dynamic trend analysis within a continuous period. Structural linkage anomalies and attribution characteristics are classified and categorized. Event association indexing and spatial location mapping are supported. Multi-level data archiving enhances information comparison and tracking between nodes, improves the transparency of the project status, and enhances the digital and intelligent management level of the entire process of bridge hanging basket contraction. Attached Figure Description

[0035] Figure 1 This is a system flowchart of the present invention;

[0036] Figure 2 This is a flowchart of the data reporting module in this invention;

[0037] Figure 3 This is a flowchart of the timing verification module in this invention;

[0038] Figure 4This is a flowchart of the shrinkage detection module in this invention;

[0039] Figure 5 This is a flowchart of the trend inference module in this invention;

[0040] Figure 6 This is a flowchart of the event index module in this invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0042] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0043] Example: Please refer to Figure 1 This invention provides a technical solution: a cloud-based hanging basket retraction status detection system comprising:

[0044] The data reporting module is based on the key nodes of the hanging basket. It analyzes the monitoring data of the displacement sensor, identifies the time characteristics of the collected data item by item, adds time stamps to the collected data, and pushes the time-stamped data to the cloud platform in real time according to the node order through the wireless communication unit to obtain unified time-series collected data.

[0045] The timing verification module is based on unified timing acquisition data, filters time stamp information, determines the relative time interval of sampling time of each node, compares the sampling time interval with the unified benchmark one by one, removes data frames that do not meet the sampling benchmark, and re-numbers the remaining data to obtain standard timing data groups.

[0046] The shrinkage detection module is based on standard time-series data sets to determine the trend of shrinkage rate of the hanging basket nodes. It analyzes the change of shrinkage rate of a single node in a continuous cycle. After an anomaly is detected, it retrieves the displacement and wire rope tension monitoring data of the node and adjacent nodes in the same cycle, compares the direction of displacement change and the trend of tension change between nodes, and combines the response behavior of the hanging basket anchorage area and the guide rail sliding unit in the shrinkage section to obtain key shrinkage anomaly characteristics.

[0047] The trend inference module is based on key contraction anomaly characteristics. It compares displacement, wire rope tension and temperature monitoring data within the anomaly period, analyzes the time change direction of each data, compares whether the displacement and tension trends are synchronized, and at the same time judges the classification of temperature changes to obtain the attribution trend index.

[0048] The event index module, based on attribution trend indicators, calls the monitoring data of the trigger source node and adjacent node within the abnormal cycle. Combined with the spatial distribution of the force transmission nodes of the hanging basket anchoring section and the wire rope tension control unit, it performs a unified structured index on various information to obtain abnormal association information.

[0049] Unified time-series data acquisition includes synchronization data identifiers, sampling start and end times, and spatial location identifiers. Standard time-series data sets include data sequence indexes, time period coverage, and data synchronization accuracy. Key contraction anomaly characteristics include rate change distribution, anomaly trend type, and structural linkage level. Attribution trend indicators include anomaly attribution categories, synchronization trend coefficients, and temperature influence factors. Anomaly association information includes event retrieval codes, archive node markers, and spatial location information.

[0050] In the data reporting module, key nodes of the hanging basket refer to locations on the hanging basket structure that play an important role in safety, deformation monitoring, or force control, such as the front end, middle, anchoring end, and connection points of the hanging basket. These are usually key areas for sensor deployment. Displacement sensor monitoring data refers to the data on the local or overall displacement changes of the hanging basket, which are acquired in real time by displacement sensors (such as linear displacement gauges and draw-wire displacement gauges) installed at key nodes. Time characteristics refer to the occurrence time information (such as sampling time point) attached to each piece of collected data, usually a timestamp, used to ensure subsequent data synchronization and time-series analysis. The wireless communication unit refers to the hardware module used for wirelessly uploading data to the cloud platform, commonly including 4G modules, NB-IoT modules, LoRa, etc., to realize the wireless connection between the data collected on site and the cloud. Node order refers to the logical or physical arrangement order of different key nodes of the hanging basket (e.g., from front to back, from left to right), ensuring that the uploaded data can correctly restore the spatial position and temporal relationship in the cloud.

[0051] In the timing verification module, the time stamp information refers to the sampling time information uploaded synchronously with each group of collected data, i.e., the timestamp, which is the basis for ensuring multi-node data synchronization and analysis; the relative time interval refers to the difference in sampling time between different node data, used to judge the synchronization of data acquisition; the unified benchmark refers to the set system time synchronization reference, such as a unified clock standard for the entire system (which can be GPS time, server time, etc.), as the judgment standard for multi-node data synchronization; the comparison one by one refers to comparing the sampling time of each node data and the time interval of each data to determine whether it meets the synchronization requirements; the data frame that does not meet the sampling benchmark refers to the part of the data whose sampling time deviates from the unified benchmark time beyond the allowable range, and this part of the data will be discarded to avoid analysis errors; the re-numbering of sampling sequence refers to re-numbering the retained data according to the sampling time or node position to facilitate subsequent correlation analysis.

[0052] In the shrinkage detection module, the shrinkage rate of the hanging basket node refers to the rate of change of the shrinkage length per unit time of the key node of the hanging basket during the shrinkage control process. It is an important parameter for the adjustment and stress monitoring of the hanging basket structure. A single node refers to a single key node of the hanging basket (such as an end or anchorage area), used for single-point anomaly analysis or multi-point trend comparison. The change in shrinkage rate refers to the trend of the shrinkage rate of the hanging basket node over time within a series of continuous sampling periods, such as whether there is a sudden acceleration or deceleration. Displacement and wire rope tension monitoring data refer to two types of key data used to analyze structural safety and stress conditions: the displacement (deformation) of the node and the stress (tension) data of the node or wire rope. The direction of displacement change refers to... Whether the displacement of a node changes positively (elongation / outward movement) or negatively (contraction / inward movement) over time is used to analyze structural trends; the tension change trend refers to whether the tension data of the wire rope or structure increases or decreases over time, which is used in conjunction with displacement analysis for anomaly identification; the contraction section of the hanging basket anchorage zone refers to the area or node that specifically undertakes the force anchorage function during the contraction control of the hanging basket, usually the fixed point or force point of the hanging basket structure; the guide rail sliding unit refers to the device (such as rollers, slide rails, etc.) on the hanging basket that can realize sliding operation, used to realize spatial displacement and linkage during the contraction of the hanging basket; the response behavior refers to the actual working state and parameter changes of the anchorage zone and sliding unit under contraction, force, linkage and other actions.

[0053] In the trend inference module, the abnormal period refers to the continuous sampling time interval identified during the detection and analysis process that contains data anomalies or sudden changes; the direction of time change refers to the trend of a certain monitored data (such as displacement, tension, and temperature) over time, whether it increases or decreases; whether the displacement and tension trends are synchronized refers to whether the trends of displacement data and tension data over time are consistent, such as both increasing, both decreasing, or opposite, which is usually used to determine the structural stress logic; the classification of temperature changes refers to the trend of temperature data during the analysis of anomalies, classifying it as related to or unrelated to displacement and tension changes, providing clues for anomaly attribution.

[0054] In the event index module, the trigger source node monitoring data refers to all relevant monitoring data (displacement, tension, etc.) of the key node that serves as the starting point of the anomaly when it is detected; the adjacent node monitoring data refers to all monitoring data collected from nodes that are directly connected to the trigger source node in space or structure and have a linkage relationship; the force transmission node of the hanging basket anchorage section refers to the anchorage node in the hanging basket structure that is specifically responsible for transmitting or distributing the structural force, and is an important reference point when archiving abnormal events; the wire rope tension control unit refers to the equipment or monitoring unit in the hanging basket structure used to adjust and monitor the force on the wire rope, and can reflect changes in structural force in real time; the structured index refers to the unified and hierarchical data archiving and retrieval of all the above monitoring and analysis data based on multiple characteristics such as time, space, and structural location.

[0055] Please see Figure 2 The data reporting module includes:

[0056] The data stream receiving submodule is based on the key nodes of the hanging basket. It analyzes the monitoring content of the displacement sensor collected, identifies the node code and measurement parameters in the message, determines whether there are missing items in the sampled data, identifies data content with complete fields, and completes the information of each group of data according to the spatial location and collection time to obtain the spatiotemporal sampling set of the node.

[0057] First, identify and extract the pre-defined key node locations in the structure. These locations are typically at the front, middle, anchoring ends, and left and right connection points. Each node is assigned a unique number; for example, the left node at the front is assigned 001, and the right node at the middle is assigned 005. Data packets are continuously read from the acquisition terminal. The content fields of each packet are broken down to extract information such as the node number, measurement parameter value, and sampling time. The validity of the packets is then checked according to the acquired field structure. For the node number field, each character is checked to ensure it is a valid number within the set range. If the number is empty, the format is incorrect, or it is not registered in the number table, the data is discarded. For the measurement parameter field, the normal physical range of the displacement sensor is set to 0 to 300 mm. If the reading exceeds this range or is missing, the data is marked as abnormal and excluded. The subsequent processing flow involves, after confirming that the message fields are complete and valid, calling the corresponding node's 3D spatial location table to match the node number with the actual spatial location. For example, the coordinates corresponding to node number 005 are 12.5 meters, 2.3 meters, and 1.2 meters. The displacement value and sampling time are recorded accordingly. For example, if the measured value is 123.4 millimeters, the sampling time is 10:15:32 AM. Then, it is checked whether duplicate data from the same node are received at the same sampling time. If multiple records with the number 005 are found, only the first one received is retained, and other duplicate content is removed to maintain data uniqueness. Finally, the retained data is organized according to the node number order and sorted from earliest to latest by sampling time to form a complete data set containing node spatial information and sampling time, which facilitates subsequent time standardization and wireless uploading.

[0058] The time stamping submodule is based on the spatiotemporal sampling set of nodes. It performs time stamping processing on each group of data according to a unified clock reference, and adjusts the temporal structure of the stamped data to obtain a standardized time series dataset.

[0059] Upon receiving the spatiotemporal sample set of nodes, a unified time reference standard is first loaded. The current unified time issued by the cloud platform is used as the reference point for the time stamps of all nodes. The original sampling time information is extracted from each set of node data and time offset is performed between it and the unified time to generate a standardized time label in a unified format. For example, if the unified time is 00:00 AM and the node sampling time is 10:15:32 AM, it is converted into a standard time value of 36932 seconds after the start of the unified time. Each piece of data is uniformly labeled with a time value in this way. Then, all node data are sorted in ascending order of time values, and the adjacency relationship between times is checked to see if it conforms to the set sampling period. Under the premise that the sampling interval is set to once per second, the allowable error range is 0. Within 2 seconds, the time interval between two adjacent data points must be between 0.8 and 1.2 seconds. If the time interval is not consistent, the data will be marked as abnormal. If data is missing at a certain second, it will be interpolated using the values ​​of the valid time points before and after it. For example, if data exists at 10:15:30 and 10:15:32, but is missing at 10:15:31, the average of the values ​​of the two time points will be used to fill in the intermediate time point. This ensures that the data interval of all nodes is consistent and is matched with the numbering order of the spatial node positions to form a set of data sequences with consistent sampling time and standard node arrangement. At the same time, consecutive sequence numbers are reassigned to the data records, for example, numbered sequentially from 1 to 1000, for subsequent data uploading and time sequence verification.

[0060] The wireless push management submodule is based on a standardized time series dataset. It determines the node arrangement and time stamp structure, configures communication channel parameters, and sequentially pushes the node encoding, measurement parameters and unified time stamp in the data. It analyzes the transmission link status and removes abnormal segments to obtain unified time series acquisition data.

[0061] First, all node numbers are counted to determine if any are missing. For example, if all nodes numbered 001 to 030 are expected to be collected, and only nodes 001 to 028 are received at a certain time point, then that data frame is marked as incomplete and the push for that time point is temporarily suspended. The sensor type field of each record is extracted, such as displacement or tension, and a coding identifier is set according to different types. This is used to generate the data frame structure to be sent. Each data frame contains a node number, sensor type identifier, and a unified time stamp. Then, the time intervals are checked in ascending order according to the unified time stamp. If the jump between consecutive time points exceeds two seconds, it is considered an abnormal jump segment, and this segment of data will not be pushed. For example, if the current time series is 100 seconds, 101 seconds, 102 seconds, and 105 seconds, skipping seconds 103 and 104 will cause the jump to 105 seconds. The 105-second record was identified as abnormal. After removing the data corresponding to that time point, the wireless communication link status was checked to obtain the current signal strength and packet loss ratio. The judgment conditions were set as follows: the signal strength must not be lower than -90 dBmW and the packet loss rate must not be higher than 10%. If the current link does not meet the conditions, the upload was stopped and the system automatically switched to a backup communication channel, such as switching from the primary 4G channel to the NB-IoT channel. Under the communication channel that meets the conditions, the number of data items that can be packaged and uploaded each time is calculated based on the data size of each frame and the maximum upload packet size specified by the communication module. For example, if a single frame is 64 bytes and the maximum upload packet capacity is 512 bytes, then a maximum of 8 records can be uploaded each time. The data is encapsulated and pushed to the cloud platform in sequence to complete the upload process of a set of node data under standard time, forming a unified time-series data acquisition structure.

[0062] Please see Figure 3 The timing verification module includes:

[0063] The time stamp extraction submodule is based on unified time-series acquired data, identifies the time stamp parameters in each data frame, determines whether the time stamp format is consistent, verifies the integrity of the field content, identifies data frames that meet the time-series consistency standard, and obtains a consistent time stamp set.

[0064] The process involves reading each data frame individually and extracting its timestamp field. A common format is a timestamp of year, month, day, hour, minute, and second, such as 2024-05-01, 10:15:32. The timestamp field is validated for character length to ensure it meets format requirements. For example, a timestamp must be 19 characters long. If a data frame's timestamp field has only 15 characters or lacks the "second" digit, it's considered formatting incorrect. Further checks are performed to identify any illegal characters, such as letters, special symbols, or spaces. These fields are marked as non-numeric and removed. After extracting the complete field content from valid data frames, the process continues to check if the timestamp is consistent with other data frames. The criteria are whether the time format is consistent and whether the time fields are continuous. If some data uses the hour + minute + second structure and another part uses the complete year, month, and day format, it is considered that there is a problem with the use of mixed time stamps. The inconsistent data format must be excluded. For the remaining data frames that use a unified time format, a timestamp reference template is loaded. The time field of each data frame is compared with the template, and data frames that meet the requirements of a unified time resolution are recorded. For example, if the sampling frequency is once per second, the timestamp must be in seconds as the smallest unit. If the timestamp precision is insufficient or there are millisecond units, the second-level time points are re-aligned. All data frames with consistent format, complete time field content, and precision that meet the standard are extracted to construct a consistent time stamp set.

[0065] The sampling interval judgment submodule calculates the time interval between adjacent data frames based on the consistent time stamp set, compares the offset of the time interval of each node with the unified clock reference, filters data frames within the allowable range, removes segments with abnormal time intervals, and obtains the synchronization time interval sequence.

[0066] After arranging the data frames in ascending time order, the difference between adjacent time points is calculated. The sampling interval between each pair of data frames is calculated group by group, with a baseline sampling period of 1 second and an allowable offset range of ±0.2 seconds. This means determining whether the time difference between adjacent data points falls within the range of 0.8 to 1.2 seconds. If the time interval between two consecutive frames in a data segment is 1.1 seconds, it is considered compliant sampling. If a segment has a time difference of 1.4 seconds or 0.5 seconds, that segment is marked as an abnormal sampling segment and removed from the dataset. The system compares the absolute difference of each time difference value with the standard period to calculate whether it exceeds the allowable range. If the allowed range is exceeded, the two frames and the intermediate segment are all marked as abnormal areas. During execution, the node number is used to help determine whether the time interval is an internal problem of the same node or a synchronization mismatch between different nodes. If a node is found to have an abnormal continuous time interval, it is determined to be a node sampling abnormality. If multiple nodes have abnormal intervals at the same time, it is caused by synchronization deviation. After removing non-compliant time intervals, the remaining continuous and stable data frames are assembled into a new time interval data sequence. For example, after removing the two frames at 103 seconds and 104 seconds from the continuous time interval data from 100 seconds to 150 seconds, the remaining data constitutes the synchronization time interval sequence.

[0067] The sequence number re-encoding management submodule adjusts the order of each data frame based on the synchronization time interval sequence, rearranges the sequence numbering according to the time sequence and node spatial order, optimizes the data index structure, and obtains a standard time-series data group.

[0068] All the selected valid data frames are rearranged in ascending order of sampling time. Then, according to the node numbering sorting rules, the data of multiple nodes at the same time point are arranged in ascending order of spatial location number. For example, at time point 10:00:01, nodes 001, 002, and 003 correspond to the front, middle, and back ends, respectively. Therefore, the data should be arranged in the order of 001, 002, and 003. During the reordering process, each record is assigned a new sequence number. The number consists of two parts: the first part is the time frame number, and the second part is the sub-number of the node within that time frame. For example, the sub-number of the node in frame 001 is... The 3-node data is numbered 001-03, and the numbering is incremented frame by frame and node by node in this manner for subsequent rapid data retrieval and backtracking. In addition, the original data index structure has been updated, changing the index method from the original order of sensor acquisition to a "time priority + spatial order" structure. A new index field has been added to the data frame to record the new number information, so that the data can be quickly classified and processed in batches according to the number when uploaded to the cloud platform or imported into the database. For example, when a user queries the data numbered 002-02, they can directly locate the second node data at the second time point, thus completing a standard time series data group with a unified format, clear numbering, and spatiotemporal consistency.

[0069] Please see Figure 4 The shrinkage detection module includes:

[0070] The rate trend extraction submodule analyzes the continuous periodic displacement data of each hanging basket node based on the standard time series data group, calculates the contraction rate change of each node in adjacent time periods, determines the direction and magnitude of rate change within the period, identifies nodes whose rate change exceeds the preset change threshold, and obtains rate anomaly indicators.

[0071] Displacement sequences of each hanging basket node within a continuous sampling period are extracted from the dataset. A time-series coordinate table is established based on the displacement records of each node at different time points. For example, the displacement records of a certain node at 100 seconds, 101 seconds, 102 seconds, and 103 seconds are 124.6 mm, 125.2 mm, 125.8 mm, and 126.5 mm, respectively. Subsequently, the displacement increment between adjacent time points is calculated cycle by cycle, and then divided by the time interval to obtain the contraction rate value per unit time. In the above data, the contraction rates per second are 0.6 mm / s, 0.6 mm / s, and 0.7 mm / s, respectively. All rate values ​​of the node in the current cycle are stored in the rate change sequence, and the direction and magnitude of rate change are calculated based on the difference between consecutive points within the sequence. If the rate value increases point by point, it is judged as an accelerating trend; if it decreases point by point, it is judged as a decelerating trend; if the fluctuation is drastic, it is considered an accelerating trend. To prevent unstable changes, a rate change threshold of 0.3 mm / s is set. The system checks if the difference between the maximum and minimum rate changes within a given period exceeds this threshold. If it does, the node is considered to have an abnormal rate. For example, in the data above, the maximum rate is 0.7 mm / s, the minimum is 0.6 mm / s, and the change is 0.1 mm / s. A rate below the threshold is considered normal. If the change is 0.4 mm / s, it is considered abnormal. In this logic, the rate change threshold is set to 0.3 mm / s, referencing the average stable fluctuation value of historical data. This value is derived from the average standard deviation of the rate during long-term stable operation of multiple nodes, statistically obtained from the upper and lower limits. Within this range, a rate fluctuation less than 0.3 mm / s indicates a stable state; exceeding this range indicates mechanical abnormalities or environmental interference. All nodes identified with abnormal rate changes are marked, forming a rate abnormality flag.

[0072] The node linkage comparison submodule, based on the rate anomaly identifier, compares the displacement direction and tension change trend of key nodes and adjacent nodes in the same period to determine the displacement consistency and tension reversal relationship between spatially adjacent nodes, and obtains the linkage consistency identifier.

[0073] First, extract complete displacement change direction information for the marked rate anomaly nodes within the corresponding period, i.e., determine whether the displacement value at two consecutive time points increases or decreases. If the node displacement value continuously decreases within the current time period, it is defined as a negative change; if it continuously increases, it is defined as a positive change. Then, call the list of adjacent nodes that are structurally directly connected to the node, extract the displacement value and wire rope tension value of the adjacent nodes within the same time period, and determine their displacement change direction in the same way. At the same time, calculate the tension value change trend to determine whether the tension is increasing, decreasing, or basically unchanged. For example, if the displacement direction of the rate anomaly node is negative, and the displacement direction of the adjacent nodes is negative and the tension trend is increasing, record that this group of nodes has a linkage relationship with the same direction and opposite tension. If the displacement direction is... If the direction of tension change is also consistent, it is determined that the structure has not generated effective mechanical linkage. In the comparison process, the judgment rule for directional consistency is set as follows: the number of times the displacement change direction of adjacent nodes is the same exceeds 70% of the total cycle, that is, at least 7 times in 10 cycles the direction is the same to be considered consistent. The tension change trend is set as follows: the proportion of cycles in which the tension change direction is opposite to the displacement direction in continuous cycles reaches more than 80% to be considered as effective linkage. For example, if the node contraction is accompanied by the tension increase in 8 times in 10 cycles, it is determined that the linkage relationship is established, otherwise it is a non-linkage state. Combined with the spatial topology map, it is determined whether the nodes are physically connected adjacent points. If the trend of non-structurally directly related nodes is consistent, they will not participate in the linkage identification output. A linkage consistency identification is generated for the location of structural anomalies and response behavior analysis.

[0074] The anomaly feature identification submodule, based on the linkage consistency identifier and combined with the response behavior of the contraction section hanging basket anchorage zone and the guide rail sliding unit, uses the following formula:

[0075] ;

[0076] By filtering nodes with enhanced response linkages, key contraction anomaly features are obtained, among which... This represents the structural response ratio of a single node within a key contraction anomaly feature. Indicates the number of nodes in the node linkage group. Indicates the first The magnitude of the shrinkage rate change of each node within the analysis period. Indicates the first The guide rail sliding displacement amplitude of each node within the analysis period Indicates the first The equivalent displacement amplitude of each node calculated due to tension changes;

[0077] The acquisition method involves analyzing the displacement data of the same hanging basket node at two consecutive sampling times, calculating the displacement change per unit time, i.e. the contraction rate of each cycle, and determining the amplitude of the rate change by comparing the differences in the contraction rates of consecutive cycles. The contraction rates of the node in adjacent time periods are compared, and the absolute amount of change is taken as the amplitude. The acquisition method involves detecting the spatial position change of the guide rail sliding unit on the hanging basket structure within the analysis period. Displacement sensors are used to collect guide rail sliding data at each moment, and the amplitude of the sliding distance change within the period is calculated. The spatial position difference between the end point and the beginning point of the guide rail sliding in this period is used as the amplitude. The method of obtaining the data involves monitoring the change in wire rope tension at the node during the analysis period. Using the relationship between force and deformation (such as the known stiffness or elastic modulus of the structure), the tension change is converted into the equivalent displacement amplitude of the node. The tension change during this period is then converted into equivalent displacement using structural parameters (such as...). ,in (For stiffness), then take the absolute amplitude. This refers to the quantitative result of the response linkage of various influencing factors during the structural contraction process for a certain node of the hanging basket structure within the analysis period, by comprehensively considering the changes in the contraction rate of the node, the sliding displacement amplitude of the guide rail, and the equivalent displacement corresponding to the tension changes. The larger the value, the more significant the response of the node to the contraction rate, sliding and force changes, indicating that its linkage in structural adjustment is active or abnormal. The smaller the value, the less sensitive the structural response of the node is in the current period, showing weak coordination or abnormality.

[0078] The structural response data of the identified nodes are acquired sequentially during the analysis period, including the magnitude of changes in the node shrinkage rate. , guide rail sliding displacement amplitude And the equivalent displacement amplitude calculated from the change in wire rope tension. ,in, From the change in tension With nodal stiffness coefficient The formula is obtained through calculation. Taking three groups of nodes as an example, their original data is as follows:

[0079] The tension change at node N2 is 520 N, and the stiffness is 80000 N / m;

[0080] The tension change at node N3 is 400 N, and the stiffness is 75000 N / m;

[0081] The tension change at node N4 is 460 N, and the stiffness is 76000 N / m;

[0082] The corresponding calculation yields: , , ;

[0083] The original values ​​for the contraction rate change and slip displacement amplitude of each node are:

[0084] , ;

[0085] , ;

[0086] , ;

[0087] All participating items were processed using maximum value normalization. The normalized data are as follows:

[0088] , , ;

[0089] , , ;

[0090] , , ;

[0091] Substitute the above data into the calculation formula:

[0092] ;

[0093] The first item is ;

[0094] Item 2 is ;

[0095] Item 3 is ;

[0096] After averaging, we get:

[0097] ;

[0098] Structural response ratio The determination is made using the interval division method. The intervals are set based on the distribution of historical monitoring data and engineering experience, and are defined as follows:

[0099] when When the node group exhibits low structural response coordination within the contraction control cycle, and the coupling between contraction rate, slip behavior, and tension changes is weak, it is classified as a "weakened linkage response segment".

[0100] when When the node group's response linkage is in a normal and stable state, there is a limited degree of dynamic coordination among the participating quantities, which is classified as the "linkage response stable segment".

[0101] when When this occurs, it indicates that multiple response factors within the structure undergo significant changes simultaneously and exhibit high consistency over time, manifesting as a significant enhancement in coordinated contraction and mechanical feedback, which is categorized as the "enhanced linkage response segment".

[0102] The formula obtained Comparing it to the above interval, it can be seen that it falls into The "linkage response enhancement segment" indicates that the structural behavior of the node group exhibits a complex linkage characteristic of significant rate fluctuations, increased guide rail slippage, and synchronously enhanced tension response within this period, thus meeting the identification criteria for abnormal behavior of key structures.

[0103] Please see Figure 5 The trend inference module includes:

[0104] The trend direction identification submodule, based on key contraction anomaly features, collects displacement monitoring data, wire rope tension monitoring data, and temperature monitoring data within the anomaly period, determines the time change trend of each monitoring data, compares the change direction of displacement curves and tension curves, and combines the fluctuation of temperature monitoring curves to obtain a multi-source monitoring change trend vector group.

[0105] The identified abnormal cycle is located, and three types of monitoring data are retrieved node by node within that cycle: node displacement data, wire rope tension data, and ambient temperature data. All data are in the format of a numerical sequence corresponding to a unified timestamp. The displacement data undergoes a time-series trend analysis, comparing the displacement values ​​at each adjacent time point. If the value at the later time point is greater than the value at the previous time point, it is recorded as a positive change; otherwise, it is recorded as a negative change. For example, if the displacement value is 124.5 mm at 10:00:00 and 125.2 mm at 10:00:01, it is considered a positive change. After completing the directional analysis of the entire time series, a displacement change direction sequence is constructed. The wire rope tension data is processed according to the same rules, determining whether the tension value increases or decreases per second to obtain the tension change direction sequence. Subsequently, using the unified timestamp as a reference, the data is analyzed by time... Point-to-point displacement changes and tension changes are paired one-to-one to form a direction pairing vector in the time series. For example, if the displacement changes positively and the tension changes negatively at the 100th second, the direction combination at that time point is "positive-negative". Then, the temperature data at the corresponding time point is read to determine the fluctuation range and direction of the temperature in the continuous period. If the temperature value changes by no more than 1 degree Celsius within three seconds, it is considered to be stable. If it rises continuously, it is determined to be a warming trend. The three types of change directions at each time point are combined into a three-dimensional change vector, such as "positive-negative-rising" or "negative-negative-stable". Then, the change vectors of all time points are combined into a complete multi-source monitoring change trend vector group. This vector group records the trend combination relationship between the node structural state and the external environment during the abnormal period, which is used for subsequent linkage state identification and attribution analysis.

[0106] The synchronization relationship judgment submodule is based on the multi-source monitoring change trend vector group, compares the change direction of displacement trend and tension trend, judges synchronization based on whether the two changes are consistent within the time series, filters out time segments with asynchronous changes, and analyzes the distribution characteristics of synchronization status to obtain trend synchronization matching index.

[0107] The system retrieves the corresponding displacement and tension change directions at each time point and performs a direction consistency check at each time point. If both are positive or both are negative, it is recorded as "synchronous." If one is positive and the other is negative, it is marked as "asynchronous." For example, at time point 105, the node's displacement direction is decreasing and the tension direction is increasing, which is recorded as an asynchronous state. The number and proportion of "synchronous" and "asynchronous" time points are counted throughout the entire abnormal cycle. The effective threshold for the judgment cycle is set to a minimum of 10 time points. If the proportion of asynchronous states exceeds 30%, it is determined that the node has a trend asynchrony problem within this cycle. The asynchronous time points are further grouped into time segment groups, and each group... Three or more consecutive asynchronous time points are considered valid asynchronous time segments. For example, if the time points from the 102nd to the 105th second are consecutively marked as asynchronous, then this segment is recorded as a segment. After marking, the distribution characteristics of all segments within the entire abnormal cycle are analyzed to determine whether the asynchronous time segments are concentrated in a specific period, such as whether they all appear in the first half of the cycle or near the area of ​​sudden temperature change. Parameters such as their start time, duration, and total proportion are statistically analyzed and recorded in the structural analysis template. The synchronization status within the cycle, the time period labels of synchronous and asynchronous segments, and their spatial locations are integrated into a set of trend synchronization matching indicators. This indicator will serve as the basic reference information for structural stress logic analysis and anomaly attribution.

[0108] The temperature attribution classification submodule determines the degree of correlation between temperature changes and trend synchronization status based on the trend synchronization matching index and the trend of temperature curves within the abnormal period. It then adjusts the correspondence between temperature changes and tension changes within the synchronization segment to obtain the attribution trend index.

[0109] First, the temperature change trend data for each time point within the abnormal period is retrieved and mapped one-to-one with the trend synchronization state. For example, the 105th second is recorded as a trend asynchrony with the temperature rising. It is then determined whether the temperature change overlaps with the trend asynchrony state. The judgment rule is defined as follows: if the temperature trend is rising or falling within a continuous time segment and the trend asynchrony state persists, it is determined that there is a temperature-related influence. This judgment logic is executed in all asynchrony segments, recording whether the temperature change and tension change directions are consistent or opposite. If the temperature rises while the tension decreases, it is recorded as "reverse temperature drive"; if the temperature rises and the tension also rises, it is recorded as "positive temperature drive". Each driving direction is then compared with the displacement change direction. Yes, a "temperature-tension-displacement" corresponding pattern table is constructed, and the frequency of occurrence of each combination is statistically analyzed. If the frequency of a certain pattern exceeds 50% of the total abnormal period, the temperature change is defined as having a dominant influence on the current trend synchronization state. This relationship is classified and attributed to form a multi-dimensional attribution trend index. The index structure includes a temperature correlation coefficient, a driving direction label, and a synchronization disruption degree score. The score range is set from 0 to 1. The closer the value is to 1, the stronger the influence of temperature on the synchronicity of trend changes. For example, if the temperature rise and trend asynchrony are highly consistent in a certain abnormal period, the score result is 0.87, which is identified as a high correlation attribution type. This attribution trend index is used to input subsequent temperature control intervention or structural response adjustment strategies.

[0110] Please see Figure 6 The event index module includes:

[0111] The source node data retrieval submodule determines the triggering source node and its adjacent nodes within the abnormal cycle based on the attribution trend index, judges whether the displacement monitoring data and tension monitoring data fields of each node are complete within the abnormal cycle, identifies node data with complete data fields, and obtains the abnormal node monitoring information group.

[0112] First, the start and end times of the abnormal cycle are extracted from the abnormal trend indicators, and the main node marked as having an abnormal trend within that cycle, i.e., the triggering source node, is identified. Using the abnormal node number recorded in the attribution indicators, the spatial topology information in the structural model is loaded to find the upstream and downstream node numbers directly adjacent to this node in the spatial structure. The source node and all its adjacent nodes form a node group. Subsequently, all sampled data of the node within the abnormal cycle are retrieved sequentially from the standard time-series data group, and its displacement monitoring value and tension monitoring value are extracted in ascending time order. The completeness of each data group is determined by whether each record simultaneously contains a timestamp field and a node number field. If either the displacement or tension field is missing, the record is marked as incomplete data and will not be included in subsequent analysis. A data recording rate of no less than 95% is used as the criterion for data completeness. For example, 120 data points should be collected within a 120-second abnormal period. If a node is missing 5 or fewer data points, its data fields are considered complete. If more than 5 data points are missing, the node record is removed. This data completeness judgment combines the sampling period configuration parameters and the node response record log, retaining node data with complete fields, and organizing the data into a structured list according to node number and time label, forming an abnormal node monitoring information group that can be used for further analysis within the abnormal period.

[0113] The spatial structure matching submodule, based on the abnormal node monitoring information group, combined with the spatial distribution of the force transmission nodes of the hanging basket anchorage section and the wire rope tension control unit, determines the spatial relationship and connection path between nodes, organizes the mapping sequence of each node in the structural system, summarizes the spatial linkage relationship, and obtains the force transmission structure association sequence.

[0114] First, the node topology diagram of the hanging basket anchorage section and the distribution table of the tension control device in the structural configuration file are called. Each monitoring node number is mapped one-to-one with its position number in the physical structure. For example, node A05 is mapped to the left slide rail connection point in the front anchorage area of ​​the hanging basket, and node B12 is mapped to the position of the wire rope guide wheel in the rear anchorage area. The relative positions of the nodes in spatial coordinates are compared one by one to determine whether adjacent nodes are connected by physical channels such as wire ropes, slide rails, or anchor plates. If there is a direct component connection between two nodes, it is defined as "Level 1 Linkage". If there is an indirect connection through intermediate components, it is marked as "Level 2 Linkage". If there is no structural or force connection between nodes, it is considered... To prevent invalid linkage, a node connection path matrix is ​​established. At the same time, the arrangement order number of each node in the structural system is recorded in the mapping sequence. For example, the nodes are numbered A01 to A10 in the front-to-back direction and L01 to L05 in the left-to-right direction. The numbers are mapped onto the structural diagram to construct a node spatial sequence table. Based on this, combined with the layout of the tension control unit, all nodes within the influence range of the control unit are marked, and it is counted whether the nodes appear in the current abnormal node monitoring information group. If there is overlap, it is marked as a controlled area linkage. The above node mapping relationship, connection path and corresponding position label in the structure are summarized and organized to form a force transmission structure association sequence covering all controlled nodes.

[0115] The associated index construction submodule adjusts the sampling number, time stamp, and spatial location code of the abnormal node monitoring information group according to the force transmission structure association sequence, arranges them into a unified retrieval field, archives all monitoring content within the period, assigns structured retrieval tags, and obtains abnormal association information;

[0116] First, each record in the abnormal node monitoring information group is read, and its original sampling number field is updated. The new number consists of three parts: structure mapping sequence number, unified time label, and node spatial number. The structure mapping sequence number is renumbered according to the spatial path from front to back. The unified time label is rounded down to the nearest second and converted to a standard format. The node spatial number references the node position code generated in the spatial structure matching, such as M001 for the front anchor point and M002 for the middle connection point. After updating the number, the monitoring information is rearranged according to the sorting rule of "time priority, structural position order" to form a new data record sequence. Each record is then assigned a structured retrieval field, which includes: node number, mapping location code, time tag, data type (displacement or tension), channel number, and anomaly marker status. For example, a record might be formatted as [M003, A08, 2024-05-01, 10:12:08, tension, CH4, anomaly]. After the database is built, all records are added to the anomaly period database, using the period number as the primary retrieval index. A structured retrieval tag group is also generated. This tag group supports rapid location and retrieval based on multiple dimensions such as node, time, type, and structural location, forming anomaly association information that can be used for structural analysis and retrospective review.

[0117] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A cloud-based hanging basket retraction status detection system, characterized in that, The system includes: The data reporting module is based on the key nodes of the hanging basket. It analyzes the monitoring data of the displacement sensor, identifies the time characteristics of the collected data, adds time stamps to the collected data, and pushes the data to the cloud platform in node order through the wireless communication unit to obtain unified time-series collected data. The timing verification module filters the time stamps of each node based on the unified timing data, determines the sampling time interval, and removes data frames that do not conform to the sampling benchmark to obtain a standard timing data group. Based on the standard time-series data set, the shrinkage detection module judges the changing trend of the shrinkage rate of the hanging basket node, analyzes the rate change within a continuous period, and retrieves displacement and tension data after detecting an anomaly. Combined with the linkage between the anchoring zone and the sliding unit, it obtains key shrinkage anomaly characteristics. The shrinkage detection module includes: The rate trend extraction submodule analyzes the continuous periodic displacement data of each hanging basket node based on the standard time series data group, calculates the contraction rate change of each node in adjacent time periods, determines the direction and magnitude of rate change within the period, identifies nodes whose rate change is higher than a preset change threshold, and obtains a rate anomaly identifier. The node linkage comparison submodule compares the displacement direction and tension change trend of key nodes and adjacent nodes in the same period based on the rate anomaly identifier, determines the displacement consistency and tension reversal relationship between spatially adjacent nodes, and obtains the linkage consistency identifier. The anomaly feature identification submodule, based on the linkage consistency identifier and combined with the response behavior of the contraction section hanging basket anchorage area and the guide rail sliding unit, filters nodes with enhanced response linkage to obtain key contraction anomaly features. Based on the key contraction anomaly characteristics, the trend inference module compares displacement, tension and temperature data within the anomaly period, analyzes the trend and synchronicity of change, determines the temperature classification, and obtains the attribution trend index. The trend inference module includes: The trend direction identification submodule, based on the key contraction anomaly features, collects displacement monitoring data, wire rope tension monitoring data, and temperature monitoring data within the anomaly period, determines the time change trend of each monitoring data, compares the change direction of the displacement curve and tension curve, and combines the fluctuation of the temperature monitoring curve to obtain a multi-source monitoring change trend vector group. The synchronization relationship judgment submodule compares the direction of change of displacement trend and tension trend based on the multi-source monitoring change trend vector group, judges the synchronization based on whether the two changes are consistent within the time series, filters out time segments with asynchronous changes, and analyzes the distribution characteristics of synchronization state to obtain trend synchronization matching index. The temperature attribution classification submodule, based on the trend synchronization matching index and the trend of the temperature curve within the abnormal period, determines the degree of correlation between temperature changes and trend synchronization status, adjusts the correspondence between temperature changes and tension changes within the synchronization segment, and obtains the attribution trend index. Based on the attribution trend index, the event index module calls the monitoring data within the abnormal period, combines the spatial distribution of force transmission nodes and tension control units, and indexes the information in a unified manner to obtain abnormal association information.

2. The cloud-based hanging basket retraction state detection system according to claim 1, characterized in that, The unified time-series data acquisition includes synchronization data identifiers, sampling start and end times, and spatial location identifiers. The standard time-series data set includes data sequence indexes, time period coverage, and data synchronization accuracy. The key contraction anomaly features include rate change distribution, anomaly trend type, and structural linkage level. The attribution trend indicators include anomaly attribution category, synchronization trend coefficient, and temperature influence factor. The anomaly association information includes event retrieval code, archive node marker, and spatial location information.

3. The cloud-based hanging basket retraction state detection system according to claim 1, characterized in that, The data reporting module includes: The data stream receiving submodule is based on the key nodes of the hanging basket. It analyzes the monitoring content of the displacement sensor collected, identifies the node code and measurement parameters in the message, determines whether there are missing items in the sampled data, identifies data content with complete fields, and completes the information of each group of data according to the spatial location and collection time to obtain the spatiotemporal sampling set of the node. The time stamping submodule performs time stamping processing on each group of data based on the spatiotemporal sampling set of the nodes and a unified clock reference, and adjusts the temporal structure of the stamped data to obtain a standardized time series dataset. The wireless push management submodule, based on the standardized time series dataset, determines the node arrangement and time stamp structure, configures communication channel parameters, sequentially pushes the node encoding, measurement parameters and unified time stamps in the data, analyzes the transmission link status and removes abnormal segments to obtain unified time series acquisition data.

4. The cloud-based hanging basket retraction state detection system according to claim 1, characterized in that, The timing verification module includes: The time stamp extraction submodule identifies the time stamp parameters in each data frame based on the unified time-series collected data, determines whether the time stamp format is consistent, verifies the integrity of the field content, identifies data frames that meet the time-series consistency standard, and obtains a consistent time stamp set. The sampling interval determination submodule calculates the time interval between adjacent data frames based on the consistent time stamp set, compares the offset of the time interval of each node with the unified clock reference, filters data frames within the allowable range, removes segments with abnormal time intervals, and obtains the synchronization time interval sequence. The sequence number re-encoding management submodule adjusts the order of each data frame based on the synchronization time interval sequence, rearranges the sequence numbering according to the time sequence and node spatial order, optimizes the data index structure, and obtains a standard time-series data group.

5. The cloud-based hanging basket retraction state detection system according to claim 1, characterized in that, The event index module includes: Based on the attribution trend index, the source node data retrieval submodule determines the triggering source node and its adjacent nodes within the abnormal period, judges whether the displacement monitoring data and tension monitoring data fields of each node are complete within the abnormal period, identifies node data with complete data fields, and obtains the abnormal node monitoring information group. Based on the abnormal node monitoring information group, the spatial structure matching submodule combines the spatial distribution of the force transmission nodes of the hanging basket anchorage section and the wire rope tension control unit to determine the spatial relationship and connection path between nodes, organize the mapping sequence of each node in the structural system, summarize the spatial linkage relationship, and obtain the force transmission structure association sequence. The association index construction submodule adjusts the sampling number, time stamp, and spatial location code of the abnormal node monitoring information group according to the force transmission structure association sequence, arranges them into a unified retrieval field, archives all monitoring content within the period, assigns structured retrieval tags, and obtains abnormal association information.

6. The cloud-based hanging basket retraction state detection system according to claim 1, characterized in that, The key nodes of the hanging basket refer to the positions on the hanging basket structure that play a role in safety, deformation monitoring, or stress control. The shrinkage rate of the hanging basket node refers to the rate of change of the shrinkage length of the key node of the hanging basket per unit time during the shrinkage control process.

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