Special-shaped hanging basket safety monitoring system based on Internet of Things

Abstract

The invention relates to the technical field of industrial monitoring, in particular to a special-shaped hanging basket safety monitoring system based on the Internet of Things. According to the method, real-time identification of the torsion state is realized by dynamically constructing an included angle sequence through a gravity vector track and a displacement path which are synchronously collected in the vertical operation process of the hanging basket, and whether the posture deviates from a dominant gravity center path or not is further judged by comparing the diagonal load trend based on an angular point stress value; space coordinates and load values of operating personnel and equipment are introduced into a load distribution layer to construct a two-dimensional plane mapping graph, a trend transfer sequence is extracted through a gravity center vector trajectory, and a Markov chain model is embedded to analyze a jump path and probability of the trend transfer sequence, so that a state transfer deviation trend identification process is formed; and the duration interval and the time length of the offset path are extracted from the time dimension, whether the duration interval and the time length exceed a preset duration threshold value or not is counted, and then the operation time period when the hanging basket is in the abnormal posture is accurately marked.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial monitoring, and in particular to a special-shaped hanging basket safety monitoring system based on the Internet of Things. Background Art

[0002] The field of industrial monitoring technology involves real-time observation, data collection, and safety management of industrial site operating status. Core issues include sensor deployment, signal transmission, data analysis, and alarm response. The purpose is to ensure the safety of industrial equipment and operators and improve production efficiency.

[0003] Among them, the traditional special-shaped hanging basket safety monitoring system refers to the system used in the construction of building curtain walls and special-shaped structures, which monitors the operating status of the hanging basket, load distribution, tilt angle and other parameters, as well as the real-time monitoring and abnormal identification of the operating status of the special-shaped hanging basket in complex construction scenarios.

[0004] Traditional special-shaped hanging basket safety monitoring systems rely on independent monitoring of parameters such as the hanging basket's operating status, load distribution, and tilt angle. They lack a multi-dimensional data fusion mechanism and are unable to form a dynamic trend trajectory in posture monitoring, resulting in difficulty in timely identification of anomalies when there is slight torsion or slight offset caused by asymmetric loads. Especially in scenarios where workers move frequently or equipment distribution changes dynamically, the rapid shift of the center of gravity position can easily be misjudged or missed by the system. At the same time, this type of system has not formed an effective trend evolution modeling path, lacks in-depth analysis of changes in the center of gravity of the load in multiple time periods, and is difficult to identify posture hazards caused by continuous offsets. For example, when the load at a corner point on one side of the hanging basket gradually increases and lasts for a long time, but the single-point deviation does not exceed the standard, the system cannot identify its potential risk, resulting in failure to effectively warn in the early stages of tilt instability, thus posing a safety hazard. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose an Internet of Things-based special-shaped hanging basket safety monitoring system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions: The special-shaped hanging basket safety monitoring system based on the Internet of Things includes: Torsion posture recognition module: This module uses the IoT communication link to synchronously collect the gravity vector trajectory and the real-time displacement path of the hanging basket during each time period of the vertical movement of the hanging basket, identify the torsional posture of the hanging basket, and obtain the torsional posture recognition result; Posture offset verification module: obtains the stress values ​​of the corner points around the hanging basket platform in a specified time period, calculates the load changes of the corresponding diagonal angles, and performs opposite verification in combination with the torsion posture recognition results to obtain the posture offset judgment result; Load gravity center calculation module: Based on the posture deviation determination result, the real-time coordinates of the operator and the equipment and the corresponding equivalent load value are collected and the load gravity center is calculated to obtain the load gravity center sequence; Trend path identification module: inputs the load center of gravity sequence into the Markov chain model, identifies the state transition path of the load center of gravity in multiple time periods, compares it with the state transition probability threshold, and screens the offset trend sequence; Attitude abnormality monitoring module: Count the duration of the continuous offset segments of the offset trend sequence, compare it with the offset trend duration threshold, determine whether the attitude state of the hanging basket is in an abnormal operating state, and obtain the attitude stability monitoring results and upload them to the cloud monitoring platform.

[0007] As a further solution of the present invention, the torsional posture recognition result includes the fitting error of the angle change sequence, the posture rotation direction recognition type, and the posture torsion amplitude level. The posture offset judgment result is specifically the diagonal load difference characteristics, the load change trend direction, and the verification judgment label. The load center of gravity sequence includes the center of gravity plane coordinates, the center of gravity position offset vector, and the load distribution time series label. The offset trend sequence is specifically the state transition path sequence, the start and end time of the offset duration, and the state transition probability value. The posture stability monitoring result includes the abnormal posture judgment state, the continuous offset duration, and the monitoring time period identifier.

[0008] As a further solution of the present invention, the twisting posture recognition module includes: Gravity vector acquisition submodule: This module obtains acceleration vector data from the three-axis acceleration sensors placed along the gondola's path in each time period, synchronously transmits the data to the data receiving end via the IoT communication link, calculates the direction angle corresponding to each acceleration vector, and arranges the data in chronological order to obtain a sequence of gravity direction changes. Displacement path analysis submodule: Based on the gravity direction change sequence, the displacement vector information of each time period recorded by the displacement encoder is obtained, and an angle sequence is constructed by calculating the angle between the basket gravity direction vector and the basket displacement path vector. A linear fitting operation is performed on the sequence to identify the angle fitting deviation trend; Posture twist judgment submodule: According to the offset trend of the angle fitting, the slope trend of the angle change in a continuous time period is analyzed to determine whether there is a continuous offset segment, and the time interval is marked in combination with the trend span to generate a twist posture recognition result.

[0009] As a further solution of the present invention, the posture offset verification module includes: Stress data acquisition submodule: obtains the stress output data of the stress sensors arranged at the four corner points of the hanging basket platform within a specified time period, classifies the stress value corresponding to each corner point into four labels: left rear, right front, right rear, and left front, and generates a set of stress measurement values ​​at the corner points; Load difference calculation submodule: Based on the set of stress measurement values ​​at the corner points, construct load combinations for the left rear-right front and right rear-left front diagonal regions, calculate the stress difference sequence corresponding to the time period within each diagonal combination, obtain the relative trend of the difference change between the two combinations, and generate the diagonal load change trend; Directional consistency verification submodule: Based on the diagonal load change trend and the torsional posture recognition result, compare whether there is an interlaced offset phenomenon in the trend direction change direction within the same period, and determine whether the posture deviates from the center of gravity dominant path, and output the posture offset determination result.

[0010] As a further solution of the present invention, the load center of gravity calculation module includes: Operation load data acquisition submodule: Based on the posture deviation determination result, the coordinate information and equivalent load value of the operator and equipment in the corresponding time period recorded in the UWB positioning tag and RFID identification tag configured on the hanging basket platform are collected to obtain the operation load coordinate value pair set; Load distribution construction submodule: establishes a spatial distribution map of the operating load in a two-dimensional plane according to the operating load coordinate and load value pair set, maps the load value to the plane distribution map according to the coordinate position, and obtains a load position map; Center of gravity coordinate extraction submodule: calls the load position mapping diagram, uses the coordinate weight and load value as operation factors, calculates the load composite center of gravity coordinates of the hanging basket in the two-dimensional plane in the current time period, and outputs the center of gravity positions in continuous time periods in sequence as sequence data to generate a load center of gravity sequence.

[0011] As a further solution of the present invention, the trend path identification module includes: State partition construction submodule: calling the load center of gravity sequence, dividing the discrete state space according to the plane coordinate range of the two-dimensional platform, placing all the center of gravity points into the corresponding blocks according to their positions, and generating the load center of gravity state distribution column; Transition probability calculation submodule: Based on the load center of gravity state distribution column, the Markov chain model is applied to calculate the transition frequency between adjacent states, identify the jump probability changes between each path node, and obtain the state transition probability set; Deviation path screening submodule: Based on the state transition probability set, the jump probability of each state path is screened, and the transition path segments that are continuously higher than the state transition probability threshold are identified and extracted as deviation trend data to generate a deviation trend sequence.

[0012] As a further solution of the present invention, the posture abnormality monitoring module includes: Trend persistence statistics submodule: calls the offset trend sequence, analyzes the start and end ranges of the continuous offset segments in the time dimension in sequence, calculates the corresponding duration data according to the time span of each segment, and establishes statistical indexes for all segments to obtain a continuous offset duration group; Abnormal state judgment submodule: Based on the continuous offset duration group, each duration is compared item by item to identify whether there is a continuous segment that is greater than the offset trend duration threshold, and the state identifier of the corresponding time period is marked to generate an abnormal posture segment mark set; Monitoring result uploading submodule: Based on the abnormal posture segment mark set, a time-series monitoring result data structure of the hanging basket posture state is constructed, and encapsulated into a structured transmission format content, which is uploaded to the cloud monitoring platform through the Internet of Things link to generate posture stability monitoring results.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, real-time identification of torsional states is achieved by dynamically constructing an angle sequence based on the gravity vector trajectory and displacement path synchronously collected during the vertical operation of the hanging basket. A relative comparison of the diagonal load trend based on the stress values ​​of the corner points is performed to further determine whether the posture deviates from the dominant center of gravity path. Dual verification is effectively achieved by effectively combining the displacement trend and load distribution. At the load distribution level, the spatial coordinates and load values ​​of the operator and equipment are introduced to construct a two-dimensional plane mapping diagram. The trend transition sequence is extracted from the center of gravity vector trajectory and embedded in a Markov chain model to analyze its jump path and probability, thereby forming a state transition offset trend identification process. The duration and time length of the offset path are then extracted from the time dimension, and statistics are calculated to see whether it exceeds a preset duration threshold. The operating time period when the hanging basket is in an abnormal posture is then accurately marked. Structured results are then constructed and uploaded to a cloud platform for remote monitoring. The entire process connects multiple analysis links, including posture recognition, load offset verification, trend path modeling, and posture anomaly recognition, forming a spatiotemporal linkage chain for hanging basket posture stability analysis. This significantly improves the accuracy of identifying abnormal states of the hanging basket in complex construction scenarios, the timeliness of response, and the intelligence level of the monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the twisting posture recognition module of the present invention; Figure 3 This is a flow chart of the posture deviation verification module of the present invention; Figure 4 This is a flow chart of the load center of gravity calculation module of the present invention; Figure 5 is a flow chart of the trend path identification module of the present invention; Figure 6 This is a flow chart of the posture abnormality monitoring module of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present 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 only used to explain the present invention and are not intended to limit the present invention.

[0016] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0017] See also Figure 1 , the special-shaped hanging basket safety monitoring system based on the Internet of Things includes: Torsion posture recognition module: This module uses the IoT communication link to synchronously collect the gravity vector trajectory and the real-time displacement path of the hanging basket during each time period of the vertical movement of the hanging basket, identify the torsional posture of the hanging basket, and obtain the torsional posture recognition result; Posture offset verification module: obtains the stress values ​​of the corner points around the hanging basket platform in a specified time period, calculates the load changes at the corresponding diagonal angles, and performs opposite verification in combination with the torsion posture recognition results to obtain the posture offset judgment results; Load center of gravity calculation module: Based on the posture deviation judgment results, the real-time coordinates of the operator and equipment and the corresponding equivalent load values ​​are collected and the load center of gravity is calculated to obtain the load center of gravity sequence; Trend path identification module: Input the load center of gravity sequence into the Markov chain model, identify the state transition path of the load center of gravity in multiple time periods, compare it with the state transition probability threshold, and filter the offset trend sequence; Attitude anomaly monitoring module: Counts the duration of continuous offset segments in the offset trend sequence, compares it with the offset trend duration threshold, determines whether the posture state of the hanging basket is in an abnormal operating state, and obtains the posture stability monitoring results and uploads them to the cloud monitoring platform; The torsional posture recognition results include the fitting error of the angle change sequence, the posture rotation direction recognition type, and the posture torsion amplitude level. The posture offset judgment results are specifically the diagonal load difference characteristics, the load change trend direction, and the verification judgment label. The load center of gravity sequence includes the center of gravity plane coordinates, the center of gravity position offset vector, and the load distribution time series label. The offset trend sequence is specifically the state transition path sequence, the start and end time of the offset duration, and the state transition probability value. The posture stability monitoring results include the abnormal posture judgment status, the continuous offset duration, and the monitoring time period identifier.

[0018] See also Figure 2 , the twist posture recognition module includes: Gravity vector acquisition submodule: This module obtains acceleration vector data from the three-axis acceleration sensors placed along the gondola's path in each time period, synchronously transmits the data to the data receiving end via the IoT communication link, calculates the direction angle corresponding to each acceleration vector, and arranges the data in chronological order to obtain a sequence of gravity direction changes. Data is collected by placing three-axis acceleration sensors in the running path of the hanging basket. First, each sensor needs to be placed along the The three-axis direction is fixedly installed on the key nodes of the hanging basket (such as the connecting beam and the load platform). The sensor records the three-axis acceleration data every 0.1 seconds. The recorded original data is the acceleration value of the three axes (for example ),in: Respectively indicate along The acceleration component in the axial direction (unit: , 1 ≈9.81m / s²).

[0019] These values ​​are uploaded to the remote server synchronously through the IoT communication module (such as LoRa or NB-IoT module). The direction angle of the acceleration vector is calculated using the received three-axis acceleration values ​​at the data receiving end. The direction angle is calculated using the space vector angle, that is, the gravity vector is expressed as , and normalized to a unit vector, the angle between the direction angle and the vertical direction is calculated as follows: ; in: : The direction of gravity and the vertical direction (i.e. The angle between the axis direction) is in degrees, : The modulus of the three-axis acceleration vector, calculated as .

[0020] Taking actual data as an example, if ,but: , so we have: This type of calculation is performed according to the actual situation and corresponds to different time points. (like ), each Sorting sequence.

[0021] Displacement path analysis submodule: Based on the gravity direction change sequence, the displacement vector information of each time period recorded by the displacement encoder is obtained. The angle sequence is constructed by calculating the angle between the basket gravity direction vector and the basket displacement path vector. A linear fitting operation is performed on the sequence to identify the angle fitting deviation trend; Based on the sequence of gravity direction changes, the current displacement vector information of the basket is obtained for each sampling time point. The displacement vector is measured by the integrated displacement encoder. For example, at a certain moment, the displacement encoder records 0.05 meters, and the displacement vector is expressed as , indicating that at this moment the basket is only along Axial displacement occurs. 、 、 :Respectively indicate the hanging basket in 、 、 The displacement components in three directions, in meters (m), : Displacement vector, a space vector after the three-axis components are combined, in the form of , : The gravity direction vector obtained from the three-axis acceleration sensor at the same time point, in units of (standard acceleration due to gravity).

[0022] The angle between the displacement vector and the gravity direction vector is calculated using the following space vector angle formula: ; in: :express and The dot product of is calculated as: , : gravity vector modulus, , : displacement vector modulus, .

[0023] If the acceleration value at a certain moment is , the displacement is ,but: ; ; ; .

[0024] The above angle calculation is repeated at multiple time points to construct a time series . Set a set of sampling data as follows (unit: second, angle): , .

[0025] The fitting goal is to obtain a linear expression of the relationship between time and angle: ; in: : Slope, which indicates the degree of increase of the angle per second, in units of , :exist The initial value of the angle at , in units of .

[0026] The least squares method is used for fitting, and the fitting formula is as follows: ; in: : No. Sampling time points, :and The corresponding angle measurement value, : the average value of the time points, : the average value of the angle, : The number of data points, in this case .

[0027] First calculate the mean: , ; Then calculate the numerator and denominator: ; ; .

[0028] Calculate the intercept : .

[0029] Final fitting function for: .

[0030] Assume that the average angle rises by about , which shows that the movement path of the basket has a tendency to gradually deviate from the vertical direction during this period.

[0031] This linear trend is used by the subsequent posture judgment module to identify whether it is a structural posture change, an unstable state of the hanging basket, etc. All fitting processes are performed according to the actual sampling data without abstract judgment or empirical adjustment.

[0032] Posture twist judgment submodule: Based on the offset trend of the included angle, the slope trend of the included angle change in a continuous time period is analyzed to determine whether there is a continuous offset segment. The time interval is marked in combination with the trend span to generate the twist posture recognition result; Based on the fitted angle-time function , analyze its slope value , since this value is much higher than the engineering experience threshold , it can be preliminarily determined that there is a continuous attitude deviation section. To verify the continuity, the module traverses the continuous slope data in the sliding window. If the continuous The slope values ​​in each window satisfy , then the deviation trend is confirmed. : The minimum slope threshold for torsion judgment is set according to the historical stable basket test results. When the angle change is lower than this value, it can be regarded as a natural swing. The setting value is , corresponding to the boundary of the normal acceleration variation range, : The minimum number of continuous judgment windows is set to 3, corresponding to a stable trend of at least 3 seconds.

[0033] Set the sampling time range to Divided into three overlapping windows: Assume window 1: , the fitting slope , Window 2: , the fitting slope , Window 3: , the fitting slope The fitting slopes of the above three windows are all greater than , meeting the criteria for sustained excursion. Further evaluation of trend duration, , meeting the trend continuation minimum span requirement .

[0034] To further identify the offset amplitude, calculate the maximum angle within the interval Minimum as follows: ; ; .

[0035] Finally, the identification interval is marked as , and output the twist posture segment recognition result: the time interval is to The fitting slope is , the maximum angle variation is , as output and stored in the recognition data record.

[0036] See also Figure 3 , the posture offset verification module includes: Stress data acquisition submodule: obtains the stress output data of the stress sensors arranged at the four corner points of the hanging basket platform within a specified time period, classifies the stress value corresponding to each corner point into four labels: left rear, right front, right rear, and left front, and generates a set of stress measurement values ​​at the corner points; Stress sensors are installed at the four corners of the platform. Typically, the left rear, right front, right rear, and left front structural stress-sensitive points are selected. Installation points are located at the corners of the frame of the platform's structural components. Each stress sensor collects stress values ​​at that location every 0.2 seconds in megapascals (MPa). The system labels the stress values ​​obtained at each corner within each time period, for example, S1 for the left rear, S2 for the right front, S3 for the right rear, and S4 for the left front. Each record contains three pieces of information: acquisition time, location label, and stress value. The data is stored as a time series and aggregated into four stress data sets based on the four corner locations. For example, within a certain time period, the records are S1 = 22.4, S2 = 20.9, S3 = 23.1, and S4 = 21.3 MPa. This process continues throughout the monitoring cycle, and the stress time series of all acquisition points are output to generate a set of corner stress measurements.

[0037] Load difference calculation submodule: Based on the set of stress measurement values ​​at the corner points, it constructs load combinations for the left rear-right front and right rear-left front diagonal areas. It calculates the stress difference sequence corresponding to the time period within each diagonal combination, obtains the relative trend of the difference change between the two combinations, and generates the diagonal load change trend. First, the stress data sets of the four corner points are classified into two diagonal combinations according to the structural position, namely the left rear-right front combination and the right rear-left front combination. The difference sequence of the stress values ​​of the two corner points corresponding to each moment is calculated. For example, if the left rear is 22.4 MPa and the right front is 20.9 MPa at time t1, the difference of the combination is 1.5 MPa. If the right rear is 23.1 MPa and the left front is 21.3 MPa, the difference of the combination is 1.8 MPa, record the difference between the two combinations at each moment into two sequences, then count the changing trends of the difference between the two diagonal combinations in each time period, use the simple difference method to calculate the increase and decrease direction between the difference between each pair of adjacent time points, and draw the difference change curve of the two combinations according to the time axis. Finally, compare the rising, falling or stable trends of the two change curves. If the left rear-right front difference continues to rise and the right rear-left front difference continues to fall within a certain period of time, then their change directions are relatively divergent, or if the two change synchronously, it means that the trends are consistent. Based on the above comparison results, the trend change is output as the diagonal load change trend.

[0038] The direction consistency verification submodule: Based on the diagonal load change trend and the torsional posture recognition results, the module compares the trend direction within the same period to see if there is any staggered deviation, determines whether the posture deviates from the center of gravity dominant path, and outputs the posture deviation determination result. Obtain the diagonal load change trend, as well as the posture torsion segment and angle change records confirmed in the previous torsion posture recognition results, and extract the change trends of the two within the same time interval. For example, if the torsion recognition segment is 0 to 4 seconds, the stress difference trend of the corresponding diagonal combination within this time period is: the left rear-right front combination increases from 1.5 MPa to 2.3 MPa per second, and the right rear-left front combination decreases from 1.8 MPa to 1.2 MPa, indicating that the two groups of differences show staggered and opposite change trends. The module compares these two trend directions to determine whether there is a significant intersection phenomenon within the same time period. The judgment standard is set as follows: if the change directions of the two groups of differences show opposite trends for more than three consecutive sampling points, it is considered that there is a staggered offset phenomenon. Record whether this trend is consistent with the identified posture torsion direction. If the offset direction is opposite to the center of gravity dominant path, that is, the difference growth direction deviates from the torsion angle change direction, then it is determined that the basket posture has deviated from the center of gravity dominant path, and output the posture offset judgment result within this time interval.

[0039] See also Figure 4 , the load center of gravity calculation module includes: The operation load data collection submodule collects the coordinate information and equivalent load values ​​of the operator and equipment in the corresponding time period recorded in the UWB positioning tags and RFID identification tags configured on the hanging basket platform based on the posture deviation judgment results, and obtains the operation load coordinate-value pair set; The operation data of the UWB positioning tags and RFID identification tags installed on the hanging basket platform are collected. The UWB positioning tags are used to obtain the two-dimensional coordinate position of the operators in real time. The RFID identification tags are used to identify the construction equipment on the hanging basket and locate its current area. The system sets the sampling period to 0.5 seconds. The coordinate and load correspondence is collected once at each time point. The data format is a triplet of horizontal coordinate, vertical coordinate, and equivalent load value. The coordinate unit is meter, and the load unit is kilogram. The load value of the operator is determined by his weight and action state. The action state adopts the construction standard action level classification system to set the correction coefficient. The load value of the equipment is determined by the combination of the rated weight of the equipment body and its operation state correction factor. The rated weight is the standard mass of the equipment in a stationary state. The operation state correction coefficient is based on the equipment type and current action. Set to different gears, for example, the static correction coefficient of conventional electric drill equipment is 1.0, low-speed drilling is 1.1, and high-speed impact is 1.2. If the rated weight of a certain equipment is 6kg and it is currently in the high-speed impact state, its equivalent load is 6×1.2=7.2kg. Finally, the load values ​​of the operator and the equipment are summed and merged into the corresponding coordinate points. For example, if a construction worker weighs 70kg and is in the walking state, the correction coefficient is 1.0, the equivalent load of the electric drill he carries is 7.2kg, and the coordinates of the worker are (1.2, 0.8), then his total equivalent load is 70+7.2=77.2kg, and the recorded coordinate-load pair is (1.2, 0.8, 77.2). The system generates multiple such coordinate-load pairs for each sampling, and summarizes the data of all time periods into a set of coordinate-load pairs arranged in timestamp order.

[0040] Load distribution construction submodule: Based on the set of load coordinate and load value pairs, a spatial distribution map of the load in the two-dimensional plane is established, and the load value is mapped to the plane distribution map according to the coordinate position to obtain a load position map; Establish a two-dimensional coordinate system with the actual length and width of the hanging basket platform as the boundary, and project all the collected load coordinate value pairs into the coordinate system. The horizontal and vertical coordinates represent the horizontal and vertical positions on the platform respectively. The construction method is based on the set grid unit division. For example, if the grid granularity is set to 0.5 meters, if the hanging basket is 4 meters long and 2 meters wide, it is divided into 8 horizontal grids and 4 vertical grids, a total of 32 grid units. Each coordinate value pair will be classified according to the grid unit into which its coordinate value falls. If a sampling point is located at (1.2, 0.8), it corresponds to the 3rd horizontal grid and the 2nd vertical grid. The system is within the grid. Accumulate the load value of the point. If multiple objects or equipment fall into the same grid within the same time period, their load values ​​will be accumulated. For example, if the load values ​​recorded at three consecutive time points in the grid are 77.2kg, 68.5kg, and 72.9kg, the total load of the grid is 218.6kg. After all grids are accumulated, the system stores the center coordinates of each grid cell and the corresponding total load value to generate a load position data set used to describe the spatial distribution. The data set is then displayed in the form of a visual two-dimensional matrix. The horizontal and vertical coordinates represent the spatial position, and the values ​​in the matrix cells represent the accumulated load values, which together constitute a load position map.

[0041] Center of gravity coordinate extraction submodule: calls the load position mapping diagram, uses the coordinate weight and load value as operation factors, calculates the load composite center of gravity coordinates of the hanging basket in the two-dimensional plane in the current time period, and outputs the center of gravity positions in consecutive time periods in sequence as sequence data to generate a load center of gravity sequence; Based on the spatial distribution information of each grid cell in the load position map, the center point coordinates of each grid cell and its corresponding cumulative load are extracted as calculation inputs. The two-dimensional spatial weighted average method is performed within each sampling time period to obtain the current load composite center of gravity coordinates of the hanging basket platform. The specific execution process is as follows: Suppose that in a certain period of time, there are There are three valid grid cells involved in the calculation, each of which has the following three properties: : No. The horizontal coordinate of the center point of each grid cell (unit: meter), : No. The vertical coordinate of the center point of each grid cell (unit: meter), : No. The cumulative load value of each grid cell (unit: kilograms).

[0042] First, calculate the total load value of all grids in this time period, which is recorded as: .

[0043] Next, calculate the weighted coordinate values ​​of each grid cell in the horizontal and vertical directions, and perform the following normalization calculation for each direction: , .

[0044] in: : The horizontal load center coordinates of the hanging basket in the current time period (unit: meter), : The longitudinal load center coordinates of the hanging basket in the current time period (unit: meter), : No. The weight of each mesh element relative to the total load (unitless).

[0045] Suppose there are a total of Valid grid cells, the specific data are as follows: Grid 1: 、 、 , grid 2: 、 、 , grid 3: 、 、 , grid 4: 、 、 .

[0046] First calculate the total load: ; Calculate the weight ratio (normalized weight) of each grid: , , , .

[0047] Calculate the horizontal center of gravity coordinates : ; Calculate the longitudinal center of gravity coordinates : ; Finally, the coordinates of the composite center of gravity of the two-dimensional load of the hanging basket during this time period are: .

[0048] The system writes this point as the center of gravity coordinate corresponding to the current timestamp into the sequence data, and continues to repeat the above calculation process for all subsequent sampling time periods. Finally, the complete load center of gravity sequence is output in chronological order, which is used for further comparison analysis with the posture change trend or to construct the center of gravity offset trajectory diagram of the hanging basket.

[0049] See also Figure 5 , the trend path identification module includes: State partition construction submodule: call the load center of gravity sequence, divide the discrete state space according to the plane coordinate range of the two-dimensional platform, and place all the center of gravity points into the corresponding blocks according to their positions to generate the load center of gravity state distribution column; Based on the two-dimensional center of gravity coordinates of each time point in the load center of gravity sequence, referring to the physical size range of the hanging basket platform, a number of equidistant state blocks are divided in the two-dimensional coordinate system to construct a discrete state space. Each state block is defined as a rectangular unit of finite size to accommodate the center of gravity coordinate points falling into the area. For example, if the hanging basket platform is 4 meters long and 2 meters wide, and is divided into 8 and 4 unit areas in the horizontal and vertical directions respectively, the entire platform is divided into 32 rectangular state units. Each block is numbered from S1 to S32, corresponding to its spatial position in the coordinate system. All points in the centroid sequence are traversed systematically, and the horizontal and vertical coordinates of each centroid point are compared with the boundary coordinates of the state unit to determine the block to which it belongs. For example, if the coordinates of a centroid are (0.5714, 0.5356), it falls into the block with the second horizontal column and the second vertical row, numbered S6. The state at that time point is recorded as S6. The entire centroid sequence is processed in this way, and the centroid coordinates at each moment are mapped to a specific state number. Finally, a load centroid state distribution column arranged by time is formed, and consistency with the original timestamp is maintained for subsequent state path and jump analysis.

[0050] Transition probability calculation submodule: Based on the load center of gravity state distribution column, the Markov chain model is applied to calculate the transition frequency between adjacent states, identify the jump probability changes between each path node, and obtain the state transition probability set; Based on the load center of gravity state distribution column, the time series jumps of the center of gravity between each discrete state are counted to construct the Markov chain state transfer matrix. The specific execution process includes: first, extracting the state numbers of any two consecutive time points in the state distribution column to form a state transfer pair set. Suppose the center of gravity state sequence in a certain period is S1, S2, S3, S2, S3, S3, S4, S2, S3, then the extracted state transfer pairs are (S1→S2), (S2→S3), (S3→S2), (S2→S3), (S3→S3), (S3→S4), (S4→S2), (S2→S3), and the transfer frequency matrix is ​​established according to the combination of all possible state pairs, in which each unit Indicates the number of times the state Sa jumps to the state Sb, expressed as frequency, that is, the transfer frequency For example, state S2→S3 appears 3 times in the above sequence, and the frequency is , state S3→S3 only appears once, frequency , state S1→S2 appears once, frequency , state S3→S2 appears once, frequency .

[0051] Then, all jump frequencies starting from each state Sa are normalized and the state transition probability is obtained. , and its calculation formula is: ; in: : represents the probability of jumping from state Sa to state Sb (unitless), : Indicates the number of jumps from state Sa to Sb (unit: times) : represents the total number of jumps from state Sa to all possible states (unit: times), : is the index of the discrete state number, representing all possible state path labels.

[0052] Taking state S2 as the starting state, the jump pairs are (S2→S3), (S2→S3), (S2→S3), (S2→S3), a total of 4 times, so: , ,so .

[0053] Taking state S3 as the starting state, its transitions include: S3→S2, S3→S3, S3→S4, each occurring once: , , , ,so: , , .

[0054] In the same way, the entire state distribution column is traversed, and all legal jump pairs are recorded in the state transfer matrix. The final output is composed of all The state transition probability set composed of jump probabilities is used to analyze the behavioral tendencies and dynamic distribution paths of the center of gravity between different areas.

[0055] Deviation path screening submodule: Based on the state transition probability set, the jump probability of each state path is screened, and the transition path segments that are continuously higher than the state transition probability threshold are identified and extracted as deviation trend data to generate a deviation trend sequence; Read the state transition probability set, for all state pairs The corresponding jump probability A comparison and screening is performed item by item, and a transition probability threshold is set to a certain numerical limit to distinguish high-frequency jumps from random fluctuations. The system traverses each actual state path and extracts state path segments with multiple consecutive jump probabilities greater than the threshold as candidate offset segments. For example, if a threshold is set to 0.6 and the transition probabilities of three groups of paths S5→S6, S6→S10, and S10→S11 are found in the state sequence to be 0.75, 0.82, and 0.68, respectively, all greater than 0.6, then the path segment is determined to be a continuous offset path segment. The path's starting state, ending state, starting time point, ending time point, corresponding state number, and each segment's jump probability value are recorded. At the same time, if a brief jump probability below the threshold occurs in the middle of a continuous segment (for example, only once below 0.6), the system allows the entire path segment to be retained. If the probability falls below the threshold more than twice in total, the segment judgment is terminated. Ultimately, all high-frequency state transition path segments that meet the conditions are combined in chronological order to form an offset trend sequence.

[0056] See also Figure 6 , the posture abnormality monitoring module includes: Trend persistence statistics submodule: calls the offset trend sequence, analyzes the start and end ranges of the continuous offset segments in the time dimension in sequence, calculates the corresponding duration data according to the time span of each segment, and establishes statistical indexes for all segments to obtain the continuous offset duration group; Each extracted offset path segment is processed sequentially along the time dimension. First, the start state number, end state number, start timestamp, and end timestamp of each offset path segment are extracted. A calculation is performed to obtain the time span of the path segment. This calculation is performed using the time difference format: subtracting the start time from the end time to obtain the duration value in seconds or milliseconds. The timestamp format is standardized to standard UTC or Unix timestamp format. For example, if an offset path segment starts at 14:00:30 (UTC) and ends at 14:00:55, the segment duration is 25 seconds. The system records each calculation result as an offset segment duration record entry, including the segment number, start and end states, start and end times, and duration value. All segments are then sorted by start time to create a numbered index list. Numbering formats such as T01, T02, and T03 can be used to support subsequent positioning references and call queries. Finally, a duration group for all offset trend segments is output.

[0057] Abnormal state judgment submodule: Based on the continuous offset duration group, each duration is compared item by item to identify whether there is a continuous segment that is greater than the offset trend duration threshold, and the state identifier of the corresponding period is marked to generate an abnormal posture segment mark set; According to the continuous offset duration group, the duration of each offset segment is retrieved and judged item by item. The judgment method is based on the comparative analysis of the set offset trend duration threshold. The threshold is the maximum stable allowable offset time set according to the basket structure and operating conditions. For example, the threshold is set to 15 seconds, which means that if a segment continuously offsets for more than 15 seconds, it can be considered an abnormal state. The system compares the duration of each segment with 15 seconds in turn. If the duration of a segment T03 is 18 seconds and T07 is 21 seconds, both of which meet the condition of being greater than the threshold, these segments will be marked as abnormal, and the start and end time, duration, state number and judgment result of the segment will be recorded. The abnormal flag bit is set in the tag information, with a value of 1 for abnormality and 0 for normality. An abnormal state tag table is generated, and all abnormal segment numbers are written into the abnormal posture segment tag set.

[0058] Monitoring result upload submodule: Based on the abnormal posture segment tag set, it constructs the time-series monitoring result data structure of the hanging basket posture state, encapsulates it into a structured transmission format, and uploads it to the cloud monitoring platform through the Internet of Things link to generate posture stability monitoring results; Obtain the abnormal posture segment tag set, and build a time series monitoring structure for the hanging basket's posture status based on the fields such as the abnormal status segment number, time range, status number and abnormal identification value contained therein. The structure content is encapsulated in JSON or XML format, and the fields include key items such as timestamp, state_code, duration, status_flag, etc. The system arranges each record in chronological order and embeds the data source identification and device number information to form a complete data structure. Then, the network link is called through the IoT communication module integrated in the hanging basket platform for packet packaging and remote push. The upload address is the interface URL or MQTT topic channel specified by the cloud monitoring platform. The upload frequency can be set to 10 seconds / time or immediate push after each segment is completed. After the system completes the packet, it is sent to the cloud, and the receiving end parses it to generate the posture stability monitoring results.

[0059] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. The special-shaped hanging basket safety monitoring system based on the Internet of Things is characterized by: The system comprises: Torsion posture recognition module: This module uses the IoT communication link to synchronously collect the gravity vector trajectory and the real-time displacement path of the hanging basket during each time period of the vertical movement of the hanging basket, identify the torsional posture of the hanging basket, and obtain the torsional posture recognition result; Posture offset verification module: obtains the stress values ​​of the corner points around the hanging basket platform in a specified time period, calculates the load changes of the corresponding diagonal angles, and performs opposite verification in combination with the torsion posture recognition results to obtain the posture offset judgment result; Load gravity center calculation module: Based on the posture deviation determination result, the real-time coordinates of the operator and the equipment and the corresponding equivalent load value are collected and the load gravity center is calculated to obtain the load gravity center sequence; Trend path identification module: inputs the load center of gravity sequence into the Markov chain model, identifies the state transition path of the load center of gravity in multiple time periods, compares it with the state transition probability threshold, and screens the offset trend sequence; Attitude abnormality monitoring module: Count the duration of the continuous offset segments of the offset trend sequence, compare it with the offset trend duration threshold, determine whether the attitude state of the hanging basket is in an abnormal operating state, and obtain the attitude stability monitoring results and upload them to the cloud monitoring platform.

2. The special-shaped hanging basket safety monitoring system based on the Internet of Things according to claim 1 is characterized in that: The torsional posture recognition result includes the fitting error of the angle change sequence, the posture rotation direction recognition type, and the posture torsion amplitude level. The posture offset judgment result is specifically the diagonal load difference characteristics, the load change trend direction, and the verification judgment label. The load center of gravity sequence includes the center of gravity plane coordinates, the center of gravity position offset vector, and the load distribution time series label. The offset trend sequence is specifically the state transition path sequence, the offset duration start and end time, and the state transition probability value. The posture stability monitoring result includes the abnormal posture judgment state, the continuous offset duration, and the monitoring time period identifier.

3. The special-shaped hanging basket safety monitoring system based on the Internet of Things according to claim 1 is characterized in that: The twisting gesture recognition module includes: Gravity vector acquisition submodule: This module obtains acceleration vector data from the three-axis acceleration sensors placed along the gondola's path in each time period, synchronously transmits the data to the data receiving end via the IoT communication link, calculates the direction angle corresponding to each acceleration vector, and arranges the data in chronological order to obtain a sequence of gravity direction changes. Displacement path analysis submodule: Based on the gravity direction change sequence, the displacement vector information of each time period recorded by the displacement encoder is obtained, and an angle sequence is constructed by calculating the angle between the basket gravity direction vector and the basket displacement path vector. A linear fitting operation is performed on the sequence to identify the angle fitting deviation trend; Posture twist judgment submodule: According to the offset trend of the angle fitting, the slope trend of the angle change in a continuous time period is analyzed to determine whether there is a continuous offset segment, and the time interval is marked in combination with the trend span to generate a twist posture recognition result.

4. The special-shaped hanging basket safety monitoring system based on the Internet of Things according to claim 3 is characterized in that: The posture deviation verification module includes: Stress data acquisition submodule: obtains the stress output data of the stress sensors arranged at the four corner points of the hanging basket platform within a specified time period, classifies the stress value corresponding to each corner point into four labels: left rear, right front, right rear, and left front, and generates a set of stress measurement values ​​at the corner points; Load difference calculation submodule: Based on the set of stress measurement values ​​at the corner points, construct load combinations for the left rear-right front and right rear-left front diagonal regions, calculate the stress difference sequence corresponding to the time period within each diagonal combination, obtain the relative trend of the difference change between the two combinations, and generate the diagonal load change trend; Directional consistency verification submodule: Based on the diagonal load change trend and the torsional posture recognition result, compare whether there is an interlaced offset phenomenon in the trend direction change direction within the same period, and determine whether the posture deviates from the center of gravity dominant path, and output the posture offset determination result.

5. The special-shaped hanging basket safety monitoring system based on the Internet of Things according to claim 4 is characterized in that: The load center of gravity calculation module includes: Operation load data acquisition submodule: Based on the posture deviation determination result, the coordinate information and equivalent load value of the operator and equipment in the corresponding time period recorded in the UWB positioning tag and RFID identification tag configured on the hanging basket platform are collected to obtain the operation load coordinate value pair set; Load distribution construction submodule: establishes a spatial distribution map of the operating load in a two-dimensional plane according to the operating load coordinate and load value pair set, maps the load value to the plane distribution map according to the coordinate position, and obtains a load position map; Center of gravity coordinate extraction submodule: calls the load position mapping diagram, uses the coordinate weight and load value as operation factors, calculates the load composite center of gravity coordinates of the hanging basket in the two-dimensional plane in the current time period, and outputs the center of gravity positions in continuous time periods in sequence as sequence data to generate a load center of gravity sequence.

6. The special-shaped hanging basket safety monitoring system based on the Internet of Things according to claim 5 is characterized in that: The trend path identification module includes: State partition construction submodule: calling the load center of gravity sequence, dividing the discrete state space according to the plane coordinate range of the two-dimensional platform, placing all the center of gravity points into the corresponding blocks according to their positions, and generating the load center of gravity state distribution column; Transition probability calculation submodule: Based on the load center of gravity state distribution column, the Markov chain model is applied to calculate the transition frequency between adjacent states, identify the jump probability changes between each path node, and obtain the state transition probability set; Deviation path screening submodule: Based on the state transition probability set, the jump probability of each state path is screened, and the transition path segments that are continuously higher than the state transition probability threshold are identified and extracted as deviation trend data to generate a deviation trend sequence.

7. The special-shaped hanging basket safety monitoring system based on the Internet of Things according to claim 6 is characterized in that: The posture abnormality monitoring module includes: Trend persistence statistics submodule: calls the offset trend sequence, analyzes the start and end ranges of the continuous offset segments in the time dimension in sequence, calculates the corresponding duration data according to the time span of each segment, and establishes statistical indexes for all segments to obtain a continuous offset duration group; Abnormal state judgment submodule: Based on the continuous offset duration group, each duration is compared item by item to identify whether there is a continuous segment that is greater than the offset trend duration threshold, and the state identifier of the corresponding time period is marked to generate an abnormal posture segment mark set; Monitoring result uploading submodule: Based on the abnormal posture segment mark set, a time-series monitoring result data structure of the hanging basket posture state is constructed, and encapsulated into a structured transmission format content, which is uploaded to the cloud monitoring platform through the Internet of Things link to generate posture stability monitoring results.

Images