Lifting machine load balance identification method and system based on sensor integrated monitoring

Abstract

The application provides a lifting machine load balance identification method and system based on sensor integrated monitoring, relates to the technical field of lifting machine load balance identification, and captures a structure response wave signal to generate a lifting machine response wave interaction data set through a sensor arranged at a lifting machine support point, analyzes the lifting machine response wave interaction data set to extract a conduction coupling characteristic to generate load conduction coupling correlation data, generates a load balance dynamic calibration benchmark based on the load conduction coupling correlation data, locates a response wave interaction abnormal position and a conduction track by tracking the deviation of the lifting machine response wave interaction data set and the load balance dynamic calibration benchmark, generates lifting machine load conduction abnormal data, and finally generates a load calibration waveform signal to adjust the response wave conduction state of the support point, so that the accurate identification and dynamic adjustment of the lifting machine load balance are realized.

Description

Technical Field

[0001] This invention relates to the field of lift load balance identification technology, and more specifically, to a lift load balance identification method and system based on sensor integrated monitoring. Background Technology

[0002] In numerous industrial production and specialized operational scenarios, lifts play an irreplaceable role as key specialized equipment. In the automotive manufacturing and repair industry, lifts are used to elevate vehicles to appropriate heights, enabling workers to inspect and replace parts from the underside of the vehicles. In the aerospace industry, lifts are used for the installation, commissioning, and maintenance of aircraft and spacecraft components, ensuring the safe and accurate assembly and testing of these high-precision devices. In large-scale warehousing and logistics scenarios, lifts are used for the vertical handling and storage of goods, improving the utilization rate of warehouse space and the efficiency of loading and unloading goods.

[0003] However, in the application scenarios of these special equipment, the accurate identification and control of the load balance of the lifting machine is particularly critical, as it directly affects the operational safety and stability of the lifting machine. Once an unbalanced load occurs during the operation of the lifting machine, it may lead to damage to the equipment structure, accelerated wear of parts, or even serious safety accidents, causing huge losses to life and property.

[0004] Traditional methods for identifying load balance in lifting platforms mainly rely on simple mechanical structure design and human experience. For example, some lifting platforms use mechanical limit devices at support points to roughly limit the load distribution range, but this method cannot accurately perceive the specific distribution of the load between each support point in real time, making it difficult to adapt to the balancing needs under different load levels and complex working conditions.

[0005] In addition, some lifts use a single sensor for load monitoring, such as obtaining pressure information at the support points only through a pressure sensor. However, the information obtained by a single sensor is limited and cannot fully reflect the dynamic response of the lift structure under load. It cannot effectively capture the interaction relationship of response waves between support points, making it difficult to accurately identify the load balance state. This can easily lead to safety hazards caused by load imbalance during the operation of the lift. Summary of the Invention

[0006] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for identifying the load balance of a lift based on integrated sensor monitoring, the method comprising: The structural response wave signals under load are captured by sensors deployed at the support points of the lift, and a lift response wave interaction dataset is generated. The structural response wave signals include pressure transmission wave signals and deformation feedback wave signals. The lift response wave interaction dataset is associated with the transmission interaction relationship of response waves between each support point. The response wave interaction dataset of the lift is analyzed, and the transmission coupling characteristics of the response waves at different support points are extracted to generate load transmission coupling correlation data of the lift. The load transmission coupling correlation data of the lift reflects the correspondence between the response wave interaction between support points and the load distribution. A load balance dynamic calibration benchmark is generated based on the load conduction coupling correlation data of the lift. The response wave interaction standard parameters of the load balance dynamic calibration benchmark are adjusted as the load level of the lift changes. Track the deviation between the lift response wave interaction dataset and the load balance dynamic calibration benchmark, locate the abnormal location and transmission trajectory of the response wave interaction, and generate abnormal load transmission data of the lift. Based on the abnormal load transmission data of the lift, a load calibration waveform signal is generated. The response wave transmission state of each support point is adjusted by the load calibration waveform signal so that the response wave interaction parameters match the load balance dynamic calibration benchmark.

[0007] In another aspect, embodiments of the present invention also provide a lift load balance identification system based on sensor integrated monitoring, including a processor and a machine-readable storage medium connected to the processor. The machine-readable storage medium is used to store programs, instructions or code, and the processor is used to run the programs, instructions or code in the machine-readable storage medium to implement the above-described method.

[0008] Based on the above, this embodiment of the invention acquires dynamic information about the lift under load by deploying sensors at the lift support points to capture structural response wave signals and generate a lift response wave interaction dataset. The response wave interaction dataset is analyzed to extract response wave conduction coupling characteristics and generate load conduction coupling correlation data, revealing the correspondence between response wave interaction between support points and load distribution. A dynamic load balance calibration benchmark generated based on the load conduction coupling correlation data can flexibly adjust the response wave interaction standard parameters according to changes in load magnitude, enhancing the adaptability and accuracy of the calibration benchmark. Tracking the deviation between the response wave interaction dataset and the calibration benchmark locates abnormal positions and trajectories, generating abnormal data that can promptly detect load imbalance problems. Finally, based on the abnormal data, a load calibration waveform signal is generated to adjust the response wave conduction state at the support points, ensuring that the response wave interaction parameters match the calibration benchmark. This achieves accurate identification and dynamic adjustment of the lift load balance, effectively improving the safety and stability of the lift operation. Attached Figure Description

[0009] Figure 1This is a schematic diagram of the execution flow of the lift load balance identification method based on sensor integrated monitoring provided in an embodiment of the present invention.

[0010] Figure 2 This is a schematic diagram of the hardware architecture of the lift load balance identification system based on sensor integrated monitoring provided in an embodiment of the present invention. Detailed Implementation

[0011] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a method for identifying the load balance of a lift based on integrated sensor monitoring, provided in one embodiment of the present invention. The following is a detailed description of this method for identifying the load balance of a lift based on integrated sensor monitoring.

[0012] Step S110: Capture structural response wave signals under load using sensors deployed at the lift support points, and generate a lift response wave interaction dataset. The structural response wave signals include pressure transmission wave signals and deformation feedback wave signals. The lift response wave interaction dataset is associated with the transmission interaction relationship of response waves between each support point.

[0013] This embodiment uses a ship repair lift as an application scenario. This lift is used for lifting large structural components during the construction or repair of ship sections. Its support system includes multiple evenly distributed support points, each with an independent structural transmission node. These nodes are rigidly connected to the main frame of the lift via high-strength alloy materials to transmit mechanical response wave signals under load. To accurately identify the load balance state of the lift, sensors are first used to capture the structural response wave signals of each support point under load, generating a lift response wave interaction dataset that reflects the interaction between the support points. Before data acquisition, all sensors undergo factory precision calibration to ensure their measurement range covers the amplitude range of the response wave signals corresponding to the lift's rated load. Simultaneously, the connection points between the sensors and the structural transmission nodes are sealed with waterproofing material to withstand complex conditions such as humidity and salt spray that may exist in the ship repair environment.

[0014] Step S111: Confirm the installation orientation of the pressure wave sensor and deformation wave sensor deployed at each support point according to the distribution of the support points of the lift. Connect each sensor to the corresponding structural transmission node of the support point one by one, so that the signal acquisition end of the sensor is in contact with the surface of the structural transmission node.

[0015] For the multiple support points of the ship repair lift, the distribution coordinates of each support point in three-dimensional space and the specific geometric positions of the structural transmission nodes were first confirmed according to the structural design drawings of the lift. The structural transmission node of each support point is a metal protrusion of a specific shape, whose surface is precision-machined to ensure signal transmission efficiency. For each support point, a pressure wave sensor and a deformation wave sensor are deployed. The pressure wave sensor is a piezoelectric sensor capable of acquiring high-frequency dynamic pressure signals, while the deformation wave sensor is a fiber optic grating sensor used to acquire wave signals generated by minute structural deformations. Both sensors use waterproof aviation connectors that meet marine industry standards for their signal output interfaces. During installation, a special mechanical clamp is used to vertically press the signal acquisition end of the pressure wave sensor onto the upper surface of the structural transmission node, ensuring complete contact between the sensor acquisition end and the node surface with a minimal gap. Simultaneously, bolts are used to secure the clamp with a specific preload to prevent excessive tightness from causing plastic deformation of the node or excessive looseness from affecting signal acquisition sensitivity. The signal acquisition end of the deformation wave sensor is attached to the side of the structural transmission node using special epoxy resin adhesive. Before attachment, the node surface needs to be sandblasted to remove rust, cleaned with alcohol, and treated with silane coupling agent to improve adhesion strength and long-term stability. After installation, a multimeter is used to test the signal circuit of each sensor to confirm that there are no short circuits or open circuits in the signal transmission line. At the same time, the baseline signal output by the sensor is observed with an oscilloscope to ensure that there is no abnormal noise interference.

[0016] Step S112: During the entire process of applying load, lifting and stabilizing the lift, pressure transmission wave signals of each support point structural transmission node are continuously collected by pressure wave sensors, and the amplitude, frequency and transmission speed of the pressure transmission wave signals are recorded at fixed time intervals as the load changes.

[0017] When the lifting machine begins lifting the ship's structural components, the pressure wave sensor continuously acquires data from the load application phase. The load application phase refers to the process of slowly placing the ship's structural components onto the lifting machine platform using hoisting equipment, during which the load gradually increases from zero. The lifting phase refers to the process of the lifting machine using a hydraulic drive system to raise the load from its initial low position to the target maintenance height; during this phase, the load may fluctuate dynamically with changes in lifting height. The stabilization phase refers to the process of the load remaining stationary after reaching the target height; during this phase, the load is relatively stable but may still experience minor vibrations. Throughout the operation, the pressure wave sensor continuously acquires pressure wave signals at fixed time intervals. These time intervals are pre-set based on the lifting speed of the lifting machine and the mass characteristics of the ship's structural components, ensuring complete capture of the dynamic changes in the response wave signal. For each acquisition moment, three key parameters of the pressure wave signal are recorded: amplitude, frequency, and propagation velocity. Amplitude reflects the intensity of the pressure wave signal, frequency reflects the rate of change of the pressure wave signal over time, and propagation velocity reflects the speed at which the pressure wave propagates through the structural transmission node material. The above parameters are arranged sequentially in time to form a numerical sequence of pressure transmission wave signals for each support point. This numerical sequence can fully reflect the dynamic evolution of the pressure transmission wave signals during the entire load process.

[0018] Step S113: Synchronously acquire deformation feedback wave signals of structural transmission nodes at each support point using deformation wave sensors, capture the synchronous frequency interaction changes of deformation feedback wave signals and pressure transmission wave signals at the same time intervals, and record the numerical trajectory of the phase difference between the two over time.

[0019] While the pressure wave sensor acquires the pressure transmission wave signal, the deformation wave sensor simultaneously acquires the deformation feedback wave signal. The acquisition time interval is exactly the same as that of the pressure wave sensor to ensure a strict correspondence between the two signals in the time dimension. The deformation feedback wave signal reflects the elastic deformation response of the structural transmission node under pressure, and there is a definite physical correlation between it and the pressure transmission wave signal. The acquisition trigger synchronization of the pressure wave sensor and the deformation wave sensor is achieved through the synchronous clock module of the lift control system, with the synchronization error controlled within a very small range. During the acquisition process, the focus is on capturing the synchronous frequency interaction between the deformation feedback wave signal and the pressure transmission wave signal; that is, when a certain frequency component appears in the pressure transmission wave signal, the amplitude response of the deformation feedback wave signal at the same frequency is monitored. Simultaneously, the phase difference between the two signals at the same frequency component is accurately measured. The phase difference reflects the time lag or lead of the structural deformation response relative to the pressure excitation. The phase difference values ​​at different acquisition times are connected sequentially in chronological order to form a numerical trajectory of the phase difference over time. This numerical trajectory can intuitively reflect the dynamic changes in the mechanical properties of the structural transmission node during load application.

[0020] Step S114: Combine the pressure transmission wave signal and deformation feedback wave signal of the same support point structural transmission node one by one according to the acquisition time to generate support point response wave data pairs. Add support point identifier, structural transmission node name and load application stage labeling information to each support point response wave data pair.

[0021] For each support point's structural transmission node, the pressure transmission wave signal data and deformation feedback wave signal data acquired at the same time are combined to form a support point response wave data pair. Each support point response wave data pair includes the amplitude, frequency, and transmission velocity of the pressure transmission wave signal at that time, as well as the corresponding parameters of the deformation feedback wave signal and the phase difference between the two. During the combination process, the two signals are strictly matched using high-precision timestamps to ensure that all signal parameters in the same data pair come from the exact same acquisition time. After the data pair combination is completed, annotation information is added to each support point response wave data pair. The support point identifier is used to uniquely distinguish different support points, using an alphanumeric coding method; the structural transmission node name is determined according to the naming rules in the lift's structural design drawings; the load application stage is divided into "load application stage," "lifting stage," and "stabilization stage" according to the lift's working state, and the current stage is automatically determined by the status signal of the lift control system and added to the annotation information.

[0022] Step S115: Extract the signal interaction feature parameters from the support point response wave data pair. The extracted signal interaction feature parameters include amplitude ratio, frequency difference and average phase difference. Map and associate the extracted signal interaction feature parameters such as amplitude ratio, frequency difference and average phase difference with the load action density of the corresponding support point to generate branch point load response wave association data.

[0023] Parameters reflecting the interaction characteristics between pressure-transmitted wave signals and deformation feedback wave signals are extracted from each support point response wave data pair. These parameters are called signal interaction characteristic parameters, specifically including amplitude ratio, frequency difference, and mean phase difference. The amplitude ratio is the ratio of the amplitude of the deformation feedback wave signal to the amplitude of the pressure-transmitted wave signal at the same moment, reflecting the deformation response sensitivity of the structural transmission node under unit pressure. The frequency difference is the difference between the dominant frequency of the deformation feedback wave signal and the dominant frequency of the pressure-transmitted wave signal at the same moment, reflecting the degree of difference in the frequency characteristics of the two signals. The mean phase difference is the arithmetic mean of the phase differences within a certain time window, reflecting the average level of the phase relationship between the two signals. After extracting these parameters, they are mapped and correlated with the load density of the corresponding support point. The load density refers to the load weight borne per unit area of ​​the support point, which can be calculated based on the total weight of the ship's structural components and the distribution area of ​​the support points. By establishing the correspondence between signal interaction characteristic parameters and load density, load response wave correlation data of branch points is generated. This load response wave correlation data of branch points can reflect the response wave characteristics of support points under different load densities.

[0024] Step S116: Integrate the load response wave correlation data of all support points, and add the overall response wave signal of the main structure of the lift and the response wave transmission path information between each support point to form the lift response wave interactive dataset.

[0025] After generating the load response wave correlation data for all support points, the data is integrated. The overall response wave signal of the lift's main structure is collected by global response sensors installed at multiple key locations on the lift's main frame. These sensors are of the same type as the support point sensors, and the acquisition parameters and time intervals are consistent. The overall response wave signal reflects the mechanical response characteristics of the lift's overall structure under load. The response wave propagation path information is determined based on the lift's structural design drawings, including the path connecting each support point through the main frame, key connection nodes along the path, and the structural material properties along the path. The load response wave correlation data for each support point, the overall response wave signal of the lift's main structure, and the response wave propagation path information between support points are combined to form a comprehensive dataset. During the integration process, a unified data format is used to standardize different types of data, and all signal parameters are converted to dimensionless form to facilitate data comparison and analysis between different support points. Simultaneously, the propagation path information is encoded to form structured path description data.

[0026] Step S117: Classify and collect the preliminary lift response wave interaction dataset according to the response wave transmission path between each support point, extract the interaction time sequence change characteristics of the response wave signal on each response wave transmission path, and mark the differences in response wave characteristics and the distinguishing markers of interaction modes for different response wave transmission paths in the lift response wave interaction dataset.

[0027] The lift's support points are connected by multiple response wave transmission paths via the main frame, each path corresponding to a specific mechanical connection between the support points. The initial lift response wave interaction dataset is categorized and grouped according to these transmission paths, grouping response wave data belonging to the same transmission path together. For each transmission path, the interaction time-series variation characteristics of the response wave signal are extracted, including the time delay of the response wave signal transmission from one support point to another, the degree of amplitude attenuation, frequency component changes, and phase changes. By comparing these characteristic parameters of different transmission paths, the differences in response wave characteristics between paths are identified; for example, some paths may have smaller amplitude attenuation, while others may have larger phase changes. Based on these characteristic differences, different interaction modes are defined, such as "strong transmission-weak attenuation mode" and "weak transmission-strong dispersion mode," and corresponding interaction mode distinguishing labels are added to each transmission path in the lift response wave interaction dataset to facilitate subsequent analysis of the response wave interaction patterns between support points.

[0028] Step S118: Map and associate the lift response wave interaction dataset with the load application area data, and mark the corresponding load application area, transmission direction and specific location of the interaction node in each response wave signal entry, and add spatial association attributes to the lift response wave interaction dataset.

[0029] Load application area data refers to the specific area information of the contact between the ship's structural components and the lifting platform. This data is acquired by dividing the lifting platform surface into grid areas and installing area identification sensors. The lifting platform response wave interaction dataset is mapped and associated with the load application area data. For each response wave signal entry, based on its corresponding support point location and load application area, the load application area, the direction of wave propagation between support points, and the specific location of the interaction node along the propagation path are labeled. For example, a response wave signal entry might be labeled as "Load application area: Platform grid area G5, Propagation direction: from support point A1 to support point A3, Interaction node: Main frame beam connection node J8". By adding these spatial association attributes, the lifting platform response wave interaction dataset includes not only temporal signal characteristics but also spatial location information, laying the foundation for subsequent analysis of the spatial relationship between load distribution and response wave interaction.

[0030] Step S119: Remove environmental interference wave signals and invalid sensor capture data from the lift response wave interaction dataset, retain valid response wave data, and sort and organize the valid response wave data according to the conduction path and load action stage to form a structured lift response wave interaction dataset.

[0031] Due to various environmental interference factors in the working environment of the lift, such as hydraulic system vibration, motor noise, and external impacts, these factors generate environmental interference wave signals. Simultaneously, sensors may also capture invalid data during the acquisition process, such as signal loss or abnormal jumps caused by brief sensor malfunctions. Digital filtering technology is employed to remove environmental interference wave signals. A bandpass filter is set based on the frequency characteristics of the lift's structural response wave signal, retaining valid signals within a specific frequency range and filtering out interference signals above and below this range. For invalid sensor data, signal amplitude thresholds, frequency thresholds, and rate of change thresholds are set for identification. When signal parameters exceed the set threshold range, they are determined as invalid data and discarded. After removing interference and invalid data, valid response wave data is retained and sorted according to the conduction path and load application stage. Data from the same conduction path is arranged consecutively, and data from the same load application stage is grouped together, forming a structured lift response wave interactive dataset for subsequent data analysis and feature extraction.

[0032] Step S120: Analyze the lift response wave interaction dataset, extract the transmission coupling characteristics of response waves at different support points, and generate lift load transmission coupling correlation data. The lift load transmission coupling correlation data reflects the correspondence between the response wave interaction between support points and the load distribution.

[0033] After obtaining the structured lift response wave interaction dataset, it needs to be analyzed in depth to extract the conduction coupling characteristics of the response waves at different support points. Conduction coupling characteristics refer to the interaction relationship between response wave signals between support points, including energy transfer, phase synchronization, and frequency modulation. These characteristics directly reflect the load conduction relationship between support points. By analyzing the signal parameters, temporal relationships, spatial correlations, and interaction modes in the response wave interaction dataset, the conduction coupling law of the response waves between support points can be identified, and the correspondence between response wave interaction characteristics and load distribution status can be established, thereby generating lift load conduction coupling correlation data.

[0034] Step S121: Call the preset lifting machine structural mechanics model, and load the structural parameters of each part of the lifting machine, the characteristics of the transmission nodes, and the transmission path data between the support points into the lifting machine structural mechanics model.

[0035] The pre-defined structural mechanics model of the lift is a digital model established based on the finite element analysis method, capable of simulating the mechanical response of the lift under different loads. This model includes the geometric models and material property parameters of the lift's main frame, support points, structural transmission nodes, and connecting components. During the analysis process, this structural mechanics model is first invoked, and then the structural parameters of each part of the lift are loaded into the model, including the dimensions, shape, mass distribution, and connection methods of each component; the characteristic parameters of each structural transmission node are loaded, including the node's stiffness, damping, natural frequency, and the material's elastic modulus and Poisson's ratio; and the transmission path data between support points is loaded, including the path's geometric length, cross-sectional shape, material properties, and constraints along the path. By loading this data, the structural mechanics model accurately reflects the actual structural characteristics of the lift.

[0036] Step S122: Input the lift response wave interactive dataset into the lift structural mechanics model, perform layered analysis processing on the response wave signal according to the transmission path between support points, and separate the response wave signal that is independently transmitted on each response wave transmission path and the response wave signal that is coupled between different paths.

[0037] The lift response wave interaction dataset was imported into the lift structural mechanics model as input data, and the model's dynamic analysis module was used to analyze the response wave signals. The response wave signals were processed hierarchically according to the transmission paths between support points, decomposing the complex response wave signals into signal components along different transmission paths. For each transmission path, the propagation characteristics of the response wave signal in the path were calculated using the model, separating the response wave signals that propagate independently only along that path. These signals are unaffected by or have negligible influence from other paths. Simultaneously, the response wave signals coupled between different paths were identified and separated. These signals are generated due to structural coupling between different transmission paths, reflecting the interaction between paths. Through hierarchical analysis and separation processing, the independent responses of each transmission path and the coupled responses between paths can be clearly identified, laying the foundation for subsequent extraction of transmission coupling characteristics.

[0038] Step S123: Extract the response wave signal features of each independent conduction path. The extracted response wave signal features include amplitude attenuation rate, frequency stability, and conduction delay time. Associate and label the extracted response wave signal features such as amplitude attenuation rate, frequency stability, and conduction delay time with the corresponding conduction path and support point to generate single-path response wave feature data.

[0039] For each independent propagation path, characteristic parameters of its response wave signal are extracted, including amplitude attenuation rate, frequency stability, and propagation delay time. Amplitude attenuation rate refers to the degree of amplitude attenuation of the response wave signal per unit distance propagated along the propagation path, obtained by calculating the amplitude ratio between the start and end points of the path and dividing by the path length. Frequency stability refers to the degree of frequency change of the response wave signal during propagation, measured by calculating the ratio of the standard deviation to the mean of the frequency. Propagation delay time refers to the time it takes for the response wave signal to propagate from the starting support point to the ending support point, obtained by comparing the timestamps of the starting and ending signals. These extracted response wave signal features are associated and labeled with the corresponding propagation path identifiers and the support point identifiers at both ends of the path, for example, "Propagation path P3 (support point A2 - support point A5): amplitude attenuation rate X1, frequency stability Y1, propagation delay time Z1". This associated data is then organized into a structured form to generate single-path response wave feature data.

[0040] Step S124: Analyze the response wave signals of coupling and propagation between different paths, extract the phase superposition characteristics, frequency modulation characteristics and energy transfer efficiency of the coupled waves, associate and label the extracted coupling characteristics such as phase superposition characteristics, frequency modulation characteristics and energy transfer efficiency with the corresponding participating paths, and generate coupling response data between paths.

[0041] The coupled response wave signals from different paths are separated from the analyzed response wave signals, and these coupled wave signals are analyzed in depth. The phase superposition characteristics of the coupled waves are extracted, i.e., the phase synthesis of the response waves from different paths at the coupling point, described by calculating the phase difference and the synthesized phase of the response waves from each participating path. The frequency modulation characteristics are extracted, i.e., the law of frequency variation of the coupled wave with the frequency of the response waves from the participating paths, obtained by analyzing the relationship curve between the frequencies of the coupled wave and the response waves from the participating paths. The energy transfer efficiency is extracted, i.e., the proportion of energy transferred from one path to another relative to the incident energy, obtained by calculating the ratio of the energy values ​​of the response waves from each path before and after coupling. These extracted coupling characteristics are associated and labeled with the corresponding participating conduction paths, for example, "Coupled path group (P2, P4): Phase superposition characteristic A, Frequency modulation characteristic B, Energy transfer efficiency C," and the above associated data is organized into a structured form to generate inter-path coupling response data.

[0042] Step S1241: Separate the response wave signals coupled and propagated between different paths from the analyzed response wave signals, mark the corresponding participating propagation paths and interaction nodes in the coupled wave signals, and lock the propagation range of the coupled waves.

[0043] During the analysis of the response wave signals, structural mechanics model analysis and signal correlation analysis were used to identify the response wave signals coupled and propagated along different paths. These coupled wave signals were then marked, clearly indicating the participating propagation paths involved in each signal—that is, which propagation paths generated the coupled wave signal through structural coupling. The locations of the interaction nodes within the structure were also marked, indicating where the response waves from different paths coupled. By marking the participating propagation paths and interaction nodes, the propagation range of the coupled waves was determined, identifying the propagation path and affected area of ​​the coupled waves within the lift structure.

[0044] Step S1242: Extract the phase superposition characteristics of the coupled waves, analyze the phase change characteristics after superposition of response waves from different paths, calculate the phase offset and phase stability after superposition, and record the influence of phase superposition on the propagation of coupled waves.

[0045] For coupled wave signals, their phase superposition characteristics are analyzed. Response waves from different paths meet and superimpose at the interaction node. Each path's response wave has a different initial phase, and the phase of the superimposed coupled wave is determined by the phase and amplitude of each participating path's response wave. The phase components of each participating path's response wave are separated using signal processing methods, and the phase offset after superposition, i.e., the difference between the coupled wave phase and the phases of each participating path's response wave, is calculated. The phase stability after superposition, i.e., the degree of fluctuation of the coupled wave phase over time, is also calculated. These phase parameters are analyzed, and the impact of phase superposition on coupled wave propagation is recorded. For example, phase superposition may cause the coupled wave to be enhanced in some directions and weakened in others, or it may affect the propagation speed and attenuation characteristics of the coupled wave.

[0046] Step S1243: Analyze the frequency modulation characteristics of the coupled wave, label the frequency interaction modes of the response waves of different paths, extract the frequency variation range and modulation efficiency after frequency modulation, and map and associate the extracted frequency variation range, modulation efficiency and other data with the load conduction strength.

[0047] This study analyzes the frequency modulation characteristics of the coupled wave, specifically the characteristic that the frequency interaction of response waves from different paths causes a change in the coupled wave frequency. It labels the frequency interaction modes of response waves from different paths, such as sum-frequency modulation, difference-frequency modulation, and harmonic modulation; these modes reflect the specific ways in which the frequencies interact. The study extracts the frequency variation range after modulation, i.e., the difference between the maximum and minimum values ​​of the coupled wave frequency; and extracts the modulation efficiency, i.e., the ratio of the power of the coupled wave after modulation to the sum of the powers of the response waves from each participating path before modulation. The extracted frequency variation range, modulation efficiency, and other data are mapped and correlated with the load conduction strength. The study analyzes the variation patterns of these frequency modulation parameters under different load conduction strengths, establishing a model relating the frequency modulation characteristics to the load conduction strength.

[0048] Step S1244: Calculate the energy transfer efficiency of the coupled wave, statistically analyze the energy attenuation and transfer rate during the coupling process, and map and associate the statistical data such as energy attenuation and transfer rate with the structural connection characteristics between paths to generate energy transfer data.

[0049] The energy transfer efficiency of the coupled wave is calculated. For two coupled propagation paths, the energy of the response wave from the input path is partially transferred to the output path to form coupled wave energy. The energy transfer efficiency is defined as the ratio of the coupled wave energy at the output path to the response wave energy at the input path. The energy attenuation during coupling is statistically analyzed, which is the difference between the energy at the input path and the coupled wave energy at the output path. The energy transfer rate is also statistically analyzed, which is the energy transferred from the input path to the output path per unit time. These energy parameters are mapped and correlated with the structural connection characteristics between the paths, including the stiffness, damping, material properties, and geometry of the connection nodes. The influence of structural connection characteristics on energy transfer efficiency, attenuation, and transfer rate is analyzed, generating energy transfer data that reflects the relationship between the structural connection characteristics and the energy transfer characteristics between the paths.

[0050] Step S1245: Integrate phase superposition characteristics, frequency modulation characteristics and energy transfer data to form inter-path coupling response data, and mark the coupling path and load conduction state corresponding to each data item in the inter-path coupling response data.

[0051] The phase superposition feature data, frequency modulation characteristic data, and energy transfer data extracted in the previous steps are integrated to form inter-path coupling response data. During integration, data belonging to the same coupling path group (i.e., multiple mutually coupled conduction paths) are grouped together, and each data item is labeled with its corresponding coupling path identifier and the current load conduction state, such as the load conduction intensity range and load change stage. For example, for a coupling path group composed of conduction paths P2 and P4, under a medium load conduction intensity state, its inter-path coupling response data includes the phase superposition characteristics, frequency modulation characteristics, and energy transfer data under that state. Through integration and labeling, the inter-path coupling response data has a clear structure and explicit physical meaning, facilitating subsequent analysis of the impact of coupling on the characteristics of single-path response waves.

[0052] Step S1246: Retrieve the characteristic data of the single-path response waves participating in the coupling, compare the amplitude, frequency, propagation velocity and phase characteristics of the single-path response waves before and after coupling, extract the characteristic changes, map and associate the extracted characteristic changes with the inter-path coupling response data, mark the characteristics of the change of single-path response wave features by the coupling effect, and determine the correlation mode between coupling strength and characteristic changes.

[0053] The process involves retrieving single-path response wave characteristic data for each propagation path involved in the coupling interaction. This data includes characteristic parameters before and after coupling. The single-path response wave characteristic parameters before and after coupling are compared. For characteristic parameters such as amplitude, frequency, propagation velocity, and phase, the difference or ratio between the parameter values ​​after coupling and those before coupling is calculated to obtain the characteristic changes. These characteristic changes are then mapped and correlated with the corresponding inter-path coupling response data. For example, the energy transfer efficiency of a certain coupled path group is correlated with the amplitude change of the participating paths. Through correlation analysis, the characteristics of the changes in single-path response wave characteristics caused by coupling are labeled, such as "coupling increases the amplitude attenuation rate of path P2 by X% and reduces frequency stability by Y%". Further analysis of the patterns of characteristic changes under different coupling strengths is conducted to determine the correlation mode between coupling strength and characteristic changes, such as linear correlation mode, nonlinear saturation correlation mode, etc.

[0054] Step S1247: For coupling effects under different load intensities, extract the characteristics of changes in single-path response wave features caused by coupling, generate load-dependent coupling effect data, and supplement the analysis results.

[0055] Considering the impact of load intensity on coupling, the changes in single-path response wave characteristics caused by coupling are analyzed for different load intensity ranges, such as low, medium, and high load intensity ranges. For each load intensity range, the comparison and extraction process in step S1246 is repeated to obtain data on the changes in single-path response wave characteristics caused by coupling under that load intensity. This data is then categorized and organized according to load intensity ranges to generate load-dependent coupling influence data. This load-dependent coupling influence data reflects the differences in the impact of coupling on single-path characteristics under different load intensities. The load-dependent coupling influence data is added to the path coupling influence analysis results to more comprehensively reflect the impact of load intensity changes on coupling.

[0056] Step S125: Map and associate the single-path response wave feature data with the inter-path coupling response data, extract the characteristics of the change of the single-path response wave features by the coupling effect, and supplement the extracted characteristics of the change of the single-path response wave features by the coupling effect into the feature analysis base data.

[0057] By mapping and associating single-path response wave characteristic data with inter-path coupling response data, the coupling relationships between propagation paths and their corresponding single-path response wave characteristics are identified. By comparing the changes in single-path response wave characteristics before and after coupling, the alteration characteristics of single-path response wave characteristics by coupling are extracted. For example, coupling may lead to an increase in the amplitude attenuation rate, a decrease in frequency stability, or a change in propagation delay time. Specifically, for a given propagation path, there is one set of characteristic parameters without coupling and another set with coupling. The difference or ratio between the two sets of parameters is calculated to obtain the amount or rate of change of each characteristic parameter by coupling. This alteration characteristic data is then added to the feature analysis base data, ensuring that the base data includes not only the original characteristics of single-path and coupled paths but also information on the impact of coupling on single-path characteristics.

[0058] Step S126: Extract dynamic change parameters of response wave features from the time evolution trajectory of the lift response wave interaction dataset. The extracted dynamic change parameters include the rate of change and amplitude of change of feature parameters. Correlate the extracted dynamic change parameters such as the rate of change and amplitude of change with the load conduction strength to generate load strength coupling feature data.

[0059] The lift response wave interactive dataset contains the complete trajectory of the response wave signal changing over time. Dynamic parameters of the response wave characteristics are extracted from this trajectory. For each response wave characteristic parameter (such as amplitude, frequency, phase difference, etc.), its rate of change and amplitude are calculated at different time intervals. The rate of change refers to the amount of change of the characteristic parameter per unit time, and the amplitude refers to the difference between the maximum and minimum values ​​of the characteristic parameter within a certain time interval. These dynamic parameters are then correlated with load transmission intensity, which refers to the load energy transmitted through the support point per unit time, and can be calculated based on the lift's load weight and lifting speed. By establishing a functional relationship between the dynamic parameters and the load transmission intensity, load intensity coupled characteristic data is generated. This load intensity coupled characteristic data reflects the law of change of the response wave characteristics with the load transmission intensity.

[0060] Step S127: Integrate single-path response wave characteristic data, inter-path coupling response data, path coupling influence data, and load strength coupling characteristic data to form preliminary lift load conduction coupling correlation data.

[0061] The single-path response wave characteristic data, inter-path coupling response data, path coupling effect data (i.e., data on the changes in single-path response wave characteristics caused by coupling), and load strength coupling characteristic data generated in the previous steps are integrated. During integration, the transmission path and support points are used as the core correlation elements, and different types of data are associated with the corresponding transmission paths and support points. For example, for the transmission path P1 between support points A1 and A2, the single-path response wave characteristic data of this path, the coupling response data with other paths, the coupling effect data of other paths on this path, and the coupling characteristic data of the path's response wave characteristics changing with load strength are integrated. Through integration, preliminary load transmission coupling correlation data for the lift is formed, which comprehensively reflects the transmission coupling characteristics of the response waves between each support point of the lift and their relationship with the load transmission strength.

[0062] Step S128: Classify and organize the preliminary load transmission coupling association data of the lift according to the load transmission intensity. Mark the core coupling features of each transmission path and the interaction points between paths under different load intensities in the load transmission coupling association data of the lift. Filter the core feature data in the load transmission coupling association data of the lift and group the processed core feature data according to the transmission path to form structured load transmission coupling association data of the lift.

[0063] The initial load transfer coupling correlation data of the lift was divided into multiple intervals based on the load transfer intensity, such as low load transfer intensity interval, medium load transfer intensity interval, and high load transfer intensity interval. The data was then categorized and organized according to these intervals, with data belonging to the same load transfer intensity interval grouped together. For each load transfer intensity interval, the coupling characteristics of each transfer path were analyzed to identify the core coupling characteristics that play a crucial role in load transfer and the key interaction points between paths, which were then labeled in the dataset. For example, in the high load transfer intensity interval, "transfer path P5 is the core transfer path, its core coupling characteristic is high energy transfer efficiency, and the interaction point with path P7 is node J12." Then, based on the core coupling characteristics and interaction points, core feature data was selected, and secondary or redundant data was removed. Finally, the processed core feature data was grouped and aggregated according to the transfer path, with core feature data belonging to the same transfer path arranged consecutively to form structured lift load transfer coupling correlation data, facilitating the subsequent generation of load balance dynamic calibration benchmarks.

[0064] Step S130: Generate a load balance dynamic calibration benchmark based on the load conduction coupling correlation data of the lift, and adjust the response wave interaction standard parameters of the load balance dynamic calibration benchmark as the load level of the lift changes.

[0065] The lift load balance dynamic calibration benchmark is a standard reference data used to determine whether the lift load is balanced. It can dynamically adjust the response wave interaction standard parameters according to changes in the lift load level. This calibration benchmark is generated based on the lift load conduction coupling correlation data produced in the previous steps. By analyzing the conduction coupling characteristics under different load levels, the response wave interaction standard parameters corresponding to each load level are determined, enabling the calibration benchmark to adapt to load changes and improving the accuracy and dynamism of load balance identification.

[0066] Step S131: Obtain the rated load parameters of the lift and the load-bearing standards of each transmission path. The load-bearing standards include the rated transmission energy of each response wave transmission path, the allowable fluctuation range of the response wave, and the coupling strength standard between paths. Use the rated load parameters of the lift, the rated transmission energy of each response wave transmission path, the allowable fluctuation range of the response wave, and the coupling strength standard between paths as the basic reference data for calibration.

[0067] Obtain the lift's rated load parameters from its design manual and technical specifications, including the maximum rated load, rated load distribution range, and maximum permissible deformation under rated load. Simultaneously, obtain the load-bearing standards for each transmission path. The load-bearing standards for each transmission path include: rated transmission energy (the maximum energy value that the path can safely transmit under rated load); permissible fluctuation range of the response wave (the permissible fluctuation range of the path's response wave characteristic parameters (such as amplitude and frequency) under normal operating conditions); and inter-path coupling strength standards (the maximum permissible coupling strength between this path and other paths), to avoid structural resonance or excessive local stress due to excessive coupling. Use the above rated load parameters and load-bearing standard data as the basis for generating the load balance dynamic calibration benchmark, ensuring that the calibration benchmark meets the lift's design safety requirements.

[0068] Step S132: Divide the load range into multiple load ranges according to the load level of the lift, map and associate each load range with the corresponding features in the load conduction coupling association data of the lift, extract the stable range of response wave interaction from the load conduction coupling association data of each load range, and determine the response wave amplitude standard, frequency standard and inter-path coupling strength standard of each conduction path in the load range. Each load range corresponds to a continuous load range.

[0069] Based on the lift's rated load parameters and actual working load range, the lift's load level is divided into multiple continuous load intervals, such as no-load interval, low-load interval, medium-load interval, high-load interval, and full-load interval. Each load interval corresponds to a continuous range of load values. Each load interval is mapped and associated with corresponding features in the lift's load conduction coupling correlation data to find the characteristic data corresponding to the stable operating state of the response wave interaction within that load interval. From these characteristic data, the stable interval of the response wave interaction is extracted, i.e., the interval where the response wave characteristic parameters fluctuate little and meet structural safety requirements. Based on the stable interval, the response wave amplitude standards (such as upper and lower limits of amplitude), frequency standards (such as the center value and allowable deviation range of frequency), and inter-path coupling strength standards (such as the allowable range of coupling energy transfer efficiency) for each conduction path within that load interval are determined. These standard parameters constitute the core content of the calibration benchmark for that load interval.

[0070] Step S133: Map and associate the standard parameters of the response wave in the stable range with the rated load parameters of the lift, verify the compatibility of the standard parameters of the response wave with the structural safety and load-bearing requirements of the lift, and adjust the numerical range of the standard parameters of the response wave.

[0071] The standard parameters of the response wave, determined for each load range, are mapped and correlated with the rated load parameters of the lift. This checks whether the standard parameters meet the structural safety and load-bearing requirements of the lift under the rated load parameters. For example, in the full load range, it is verified whether the standard response wave amplitude is below the amplitude threshold that causes plastic deformation of the structure, whether the standard frequency is far from the natural frequency of the lift structure to avoid resonance, and whether the coupling strength standard is within the allowable range of structural connection strength. The suitability of the standard response wave parameters is verified through structural mechanics calculations and safety margin analysis. If some standard parameters are found to be mismatched with safety requirements, such as an excessively high amplitude standard that may lead to structural overload, the numerical range of the standard response wave parameters is adjusted, lowering the upper limit of the amplitude to ensure that the adjusted standard parameters can guarantee the safe operation of the lift within that load range.

[0072] Step S134: Extract the optimal interaction mode of the response wave between support points in each load interval, map and associate the optimal interaction mode with the corresponding load distribution state, and determine the optimal load distribution ratio of each load interval.

[0073] Within each load range, the interaction patterns of response waves between support points are extracted from the load conduction coupling correlation data of the lift. Performance indicators such as load distribution uniformity, structural stress distribution, and energy transfer efficiency under different interaction patterns are analyzed. Based on these performance indicators, the optimal interaction pattern within the load range is selected—that is, the response wave interaction pattern that achieves the most uniform load distribution, the lowest structural stress, and the highest energy transfer efficiency. This optimal interaction pattern is mapped and correlated with the corresponding load distribution state to determine the load proportion borne by each support point under this interaction pattern, i.e., the optimal load distribution proportion. For example, in the medium load range, the optimal load distribution proportion might be that support points A1 to A8 each bear a specific percentage of the total load, with small differences between each percentage to ensure uniform load distribution.

[0074] Step S135: Integrate the response wave standard parameters, optimal load distribution ratio and path coupling standard of each load range to form a preliminary load balance dynamic calibration benchmark. Add the load range label corresponding to each benchmark parameter to the load balance dynamic calibration benchmark.

[0075] The response wave standard parameters (amplitude standard, frequency standard, etc.), optimal load distribution ratio, and inter-path coupling strength standard determined in the previous steps are integrated to form a preliminary load balancing dynamic calibration benchmark. During the integration process, the benchmark data for each load interval are arranged in ascending order of load interval size, with each load interval corresponding to a complete set of benchmark parameters. In the load balancing dynamic calibration benchmark, a clear load interval label is added to each benchmark parameter, indicating which load interval the parameter applies to, for example, "Amplitude standard: [L1, U1] (applicable to low load interval)". Adding labels gives the calibration benchmark a clear structure, facilitating the rapid retrieval and application of the corresponding benchmark parameters based on the actual load level.

[0076] Step S136: Simulate the interactive change process of the response wave when switching between different load ranges, capture the dynamic transition characteristics of the response wave features, and generate a smooth transition curve of the reference parameters based on the captured dynamic transition characteristics of the response wave features.

[0077] When the load on a lift changes from one zone to an adjacent zone, the response wave interaction characteristics undergo a dynamic transition. The interaction process of the response wave during the switch between different load zones is simulated using a lift structural mechanics model, such as switching from a low-load zone to a medium-load zone, simulating the change trajectory of the response wave characteristic parameters as the load increases at different rates. The dynamic transition characteristics of the response wave features are captured, including the rate of change of characteristic parameters, overshoot, settling time, and other transition process parameters. Based on these dynamic transition characteristics, a smooth transition curve for the reference parameters is generated, ensuring a smooth transition between the reference parameters of adjacent load zones and avoiding abrupt changes in reference parameters at zone switching points. The shape of the transition curve is determined according to the dynamic transition characteristics of the response wave features, such as using an exponential curve, a ramp curve, or an S-shaped curve, to ensure the smoothness of the transition process.

[0078] Step S137: Retrieve historical valid data from the lift response wave interaction dataset, compare the historical valid data with the parameters of the preliminary load balance dynamic calibration benchmark, extract parameter deviation data, and correct the standard parameters in the load balance dynamic calibration benchmark based on the extracted parameter deviation data.

[0079] Historical valid data from the lift response wave interaction dataset is retrieved. This dataset contains records of the lift's response wave interactions under different load conditions. Representative historical data samples are selected, such as typical operating condition data for different load ranges and dynamic data during load range switching. These historical valid data are compared with the corresponding parameters in the preliminary load balance dynamic calibration benchmark. The deviation between the response wave characteristic parameters in the historical data and the benchmark parameters is calculated, and parameter deviation data is extracted. The causes of parameter deviation are analyzed, which may include model simplification errors, sensor drift, and changes in environmental factors. Based on the extracted parameter deviation data, the standard parameters in the load balance dynamic calibration benchmark are corrected. For example, if the actual wave amplitude in a certain load range in the historical data is generally lower than the benchmark wave amplitude, the upper limit of the benchmark wave amplitude for that range is reduced, making the corrected benchmark parameters closer to the actual operating conditions.

[0080] Step S138: Sort and organize the load balance dynamic calibration benchmarks in ascending order of load magnitude, mark the adjustment characteristics of the benchmark parameters of each load range in the load balance dynamic calibration benchmarks, connect the benchmark parameters of each load range in series, and integrate the corrected benchmark parameters with the transition curve to form the load balance dynamic calibration benchmarks.

[0081] The load balancing dynamic calibration benchmarks are sorted and organized in ascending order of load level, with the benchmark data for each load range arranged sequentially. The adjustment characteristics of the benchmark parameters for each load range are marked in the calibration benchmarks, indicating how the benchmark parameters change with the load within that range, such as linear adjustment, step adjustment, or curve adjustment. The benchmark parameters for each load range are concatenated using the smooth transition curves generated in the previous steps, forming a continuous and smooth family of curves across the entire load range. The corrected benchmark parameters are then integrated with the corresponding transition curves to ensure that the transition curves accurately reflect the changes in the corrected benchmark parameters during range switching. Through sorting, marking, and integration, a complete load balancing dynamic calibration benchmark is finally formed, which can dynamically adjust the response wave interaction standard parameters according to changes in the lift's load level.

[0082] Step S140: Track the deviation between the lift response wave interaction dataset and the load balance dynamic calibration benchmark, locate the abnormal position and transmission trajectory of the response wave interaction, and generate abnormal load transmission data of the lift.

[0083] During the operation of the lift, the deviation between the lift's response wave interaction dataset and the load balance dynamic calibration benchmark is tracked in real time. By comparing the differences between the actual response wave characteristic parameters and the benchmark parameters, anomalies in the response wave interaction are identified. When the deviation exceeds a set threshold, an anomaly in the response wave interaction is determined, and the specific location of the anomaly and the abnormal transmission trajectory of the response wave on the transmission path are located. This generates lift load transmission anomaly data containing the anomaly location, anomaly characteristics, and transmission trajectory.

[0084] Step S141: According to the load range and transmission path, compare the corresponding standard parameters in the load balance dynamic calibration benchmark point by point with the interactive dataset of the lift response wave. Extract the deviation data between the response wave characteristics and the benchmark parameters at each moment, and add the corresponding time, transmission path and support point labels to the deviation data between the response wave characteristics and the benchmark parameters.

[0085] Based on the current load level of the lift, its load range is determined. Then, the real-time response wave characteristic parameters in the lift's response wave interactive dataset are compared point-by-point with the standard parameters of the corresponding load range and transmission path in the load balance dynamic calibration benchmark, following the transmission path. The comparison process is performed at each data acquisition moment, calculating the deviation values ​​between the real-time characteristic parameters and the benchmark parameters, such as amplitude deviation, frequency deviation, phase difference deviation, and coupling strength deviation. These deviation data are arranged in chronological order to form a deviation time series. For each deviation data point, a corresponding time identifier (accurate to the acquisition timestamp), transmission path identifier, and relevant support point identifier are added, for example, "Time t123, Transmission Path P3, Support Points A2-A4, Amplitude Deviation D1, Frequency Deviation D2," giving the deviation data clear spatiotemporal attributes.

[0086] Step S142: Analyze the evolution trend of the deviation data between the response wave characteristics and the reference parameters, distinguish between instantaneous deviation and continuous deviation, extract the deviation amplification rate, deviation stability range and deviation fluctuation frequency for continuous deviation, map and associate the extracted deviation amplification rate, deviation stability range and deviation fluctuation frequency with the load conduction state to generate deviation dynamic evolution data.

[0087] Trend analysis is performed on the time series of deviation data to distinguish between instantaneous and persistent deviations by setting time windows and deviation thresholds. Instantaneous deviations refer to deviations that occur within a short period and quickly recover to the allowable range, usually caused by accidental factors. Persistent deviations refer to deviations that consistently exceed the allowable range and do not recover for a long time, indicating a possible load transmission anomaly. For persistent deviations, the deviation amplification rate (the rate at which the deviation value increases over time), the deviation stability range (the range between the maximum and minimum values ​​of the deviation during the persistent process), and the deviation fluctuation frequency (the frequency at which the deviation value fluctuates within the stable range) are extracted. These deviation characteristic data are mapped and correlated with the load transmission status of the lift, including load size, load change rate, and load distribution uniformity. Through correlation analysis, dynamic evolution data of the deviation is generated, reflecting the development process of persistent deviations and their relationship with the load transmission status. For example, uneven load distribution may lead to an increased deviation amplification rate.

[0088] Step S143: Based on the dynamic evolution data of the deviation, locate the transmission path that generates the continuous deviation, trace the response wave signal of each support point structural transmission node on the transmission path, and lock the support point position and structural transmission node corresponding to the source of the deviation.

[0089] By analyzing the dynamic evolution data of deviations, transmission paths with persistent deviations are identified, whose deviation characteristics significantly exceed the normal range. For each transmission path with persistent deviations, the response wave signals of the structural transmission nodes at each support point along the path are traced. Starting from one support point at the end of the path, the characteristic parameters of the response wave signals are examined node by node, and the signal differences between adjacent nodes are compared. By analyzing the signal transmission process along the transmission path, the node where the signal characteristics begin to change abnormally is identified. This node is the structural transmission node corresponding to the source of the deviation, and its corresponding support point location is the location of the source of the deviation. For example, on transmission path P5, starting from support point A3, nodes J10, J11, and J12 are examined sequentially. It is found that the signal amplitude begins to attenuate abnormally from node J11. Therefore, node J11 is the structural transmission node of the source of the deviation, corresponding to support point A3.

[0090] Step S1431: Extract the transmission path identifier corresponding to the continuous deviation from the deviation dynamic evolution data, call the lift response wave interaction data and lift load transmission coupling correlation data corresponding to the transmission path, and use the lift response wave interaction data and lift load transmission coupling correlation data corresponding to the transmission path as the basic data for positioning analysis.

[0091] In the dynamic evolution data of deviations, each persistent deviation record contains a corresponding transmission path identifier. These transmission path identifiers are extracted to determine the transmission paths requiring anomaly localization analysis. For each identified transmission path, the response wave interaction data for that path is retrieved from the lift response wave interaction dataset, including the pressure transmission wave signals, deformation feedback wave signals, and their interaction characteristic parameters at each support point along the path; the single-path response wave characteristic data, inter-path coupling response data, and load strength coupling characteristic data for that path are retrieved from the lift load transmission coupling correlation data.

[0092] Step S1432: Trace the response wave signals of each support point structural transmission node along the transmission path according to the direction of response wave transmission. Compare the response wave characteristics with the load balance dynamic calibration benchmark node by node from the starting support point to the ending support point of the transmission path, and screen out the first structural transmission node that shows a continuous deviation.

[0093] The direction of the response wave propagation along the path is determined based on the design drawings, typically from the support point in the load application area to other support points. Starting from the initial support point of the propagation path, the response wave signals of each structural transmission node along the path are traced sequentially until the final support point. For each structural transmission node, its response wave characteristic parameters (amplitude, frequency, propagation velocity, phase difference, etc.) are compared with the standard parameters of the corresponding load range and node in the load balance dynamic calibration benchmark, and the deviation value is calculated. A deviation threshold is set. When the deviation value of the response wave characteristic parameters of a node first exceeds the threshold and persists for multiple acquisition cycles, that node is determined to be the first structural transmission node with a sustained deviation, i.e., the deviation source node.

[0094] Step S1433: Confirm the location of the support point corresponding to the first structural transmission node with continuous deviation, extract the response wave signal capture data of the support point, analyze the integrity and authenticity of the signal capture, and eliminate the deviation data caused by sensor capture error.

[0095] The location of the support point to which the first structural transmission node exhibiting persistent deviation is determined by locating the support point through the correspondence between node numbers and support point numbers. The raw data of the response wave signal capture at this support point is extracted, including the raw output signals from the pressure wave sensor and deformation wave sensor. The integrity of the signal capture is analyzed, checking for data loss, discontinuity, or anomalous jumps. The authenticity of the signal capture is analyzed by comparing it with signals from other sensors at the same time or historical signals to determine if the signal is subject to severe interference or sensor malfunction. Deviation data caused by sensor capture errors, such as signal noise from temporary sensor malfunctions or signal attenuation due to poor connection lines, are discarded to ensure that subsequent analyses are based on authentic and valid abnormal deviation data.

[0096] Step S1434: For the structural transmission node of the deviation source support point, analyze the abnormal characteristics of the response wave signal. The extracted abnormal characteristics include abnormal amplitude attenuation, frequency shift, abnormal transmission delay, and abnormal phase difference with the response waves of other support points. The specific manifestations of abnormal transmission are marked in the analysis results.

[0097] A detailed analysis was conducted on the structural transmission node response wave signals of the support points at the source of the deviation, extracting their abnormal characteristics. Abnormal amplitude attenuation manifested as a signal amplitude attenuation rate with transmission distance that was significantly greater than the reference attenuation rate; frequency shift manifested as the main frequency component deviating from the reference frequency and exceeding the allowable fluctuation range; abnormal transmission delay manifested as the signal transmission time from the upstream node to this node being significantly longer than the normal transmission delay time; and phase difference manifested as the phase difference between the response wave signals and other related support points exceeding the normal range. The analysis results detailed the specific manifestations of these abnormal transmissions, such as "abnormal amplitude attenuation: the actual attenuation rate is X times the reference attenuation rate" and "frequency shift: the main frequency deviates from the reference frequency by Y Hz," etc.

[0098] Step S1435: Compare the single-path response wave characteristic data of the conduction path with the overall response wave interaction characteristics of the conduction path before and after abnormal conduction, extract the characteristic change, and analyze the impact of abnormal conduction at the source of deviation on the overall conduction efficiency and response wave stability of the conduction path.

[0099] Single-path response wave characteristic data for the conduction path are retrieved, including normal characteristic data before and after the abnormal conduction. These two sets of data are compared, and the changes in various characteristic parameters, such as the change in amplitude attenuation rate, frequency stability, and conduction delay time, are calculated. The impact of these characteristic changes on the overall conduction efficiency and response wave stability of the conduction path is analyzed. Conduction efficiency can be measured by changes in energy transfer efficiency, while response wave stability can be measured by changes in the frequency fluctuation range and phase difference fluctuation range. For example, an increased amplitude attenuation rate leads to a decrease in conduction efficiency, and decreased frequency stability leads to a decrease in response wave stability. Through analysis, the degree of impact of the abnormal conduction at the source of the deviation on the overall performance of the path is assessed.

[0100] Step S1436: Extract the response wave signal changes of other support point structure transmission nodes on the transmission path, map and associate the response wave signal change data of other support point structure transmission nodes on the transmission path with the abnormal transmission of the deviation source, mark the degree of influence and transmission characteristics of the deviation source on the subsequent nodes on the transmission path, and generate inter-node transmission influence data.

[0101] Extract the response wave signal variation data of the structural transmission nodes along the transmission path, excluding the deviation source node; that is, the changes in the signal characteristic parameters of these nodes before and after the abnormal transmission from the deviation source. Map and correlate these variation data with the abnormal transmission from the deviation source to analyze how the abnormal transmission from the deviation source affects subsequent nodes through the transmission path. Label the degree of influence of the deviation source on subsequent nodes, such as strong, medium, or weak influence, which can be classified by the magnitude of the changes in the signal characteristics of subsequent nodes; label the transmission characteristics, such as whether the deviation is transmitted linearly, exponentially, or in a stepwise manner. Based on these analyses, generate inter-node transmission influence data, which describes the transmission pattern and degree of influence of the deviation among the nodes along the transmission path.

[0102] Step S1437: Combining the structural mechanics model of the lift, simulate the potential impact of abnormal transmission from the source of deviation on the structural strength and load-bearing capacity of the transmission path, generate path structure impact data from the simulation results, and supplement them to the abnormal transmission analysis results.

[0103] The structural mechanics model of the lift is invoked, using the abnormal transmission characteristic parameters of the deviation source (such as abnormal stress wave amplitude and frequency) as input conditions to simulate the potential impact of abnormal transmission on the structural strength and load-bearing capacity of the transmission path. The simulation analysis includes calculating the stress distribution, strain distribution, displacement changes, and fatigue life prediction of the path structural components. It assesses whether structural components will yield, fracture, or undergo excessive deformation under abnormal transmission, and whether the load-bearing capacity will decrease. The simulation results are then compiled to generate path structural impact data, including parameters such as maximum stress value, safety margin, and remaining load-bearing capacity, and this data is added to the abnormal transmission analysis results.

[0104] Step S1438: Integrate the location of the deviation source support point, structural transmission node information, abnormal transmission manifestations, overall path impact data, and inter-node transmission impact data to form path abnormal transmission analysis results; organize the path abnormal transmission analysis results in a structured format; after determining the correlation between each data item in the path abnormal transmission analysis results, associate the path abnormal transmission analysis results with the deviation dynamic evolution data and supplement them to the lift load transmission abnormal data.

[0105] The coordinates of the deviation source support points, the numbers and names of structural transmission nodes, the abnormal transmission manifestations (amplitude anomalies, frequency shifts, etc.), the overall path impact data (changes in transmission efficiency, stability changes, etc.), and the inter-node transmission impact data obtained in the previous steps are integrated to form the path anomaly transmission analysis results. The analysis results are organized in a structured format, defining the field names, data types, and units of data items, and clarifying the relationships between each data item, such as the correspondence between deviation source nodes and abnormal manifestations, and the causal relationship between abnormal manifestations and the overall path impact. The organized path anomaly transmission analysis results are then correlated with the deviation dynamic evolution data, establishing a connection through transmission path identifiers and timestamps. Detailed anomaly information from the analysis results is added to the lift load transmission anomaly data, enriching the content of the anomaly data and improving its usability.

[0106] Step S144: Extract the abnormal features of the response wave signal of the support point of the deviation source. The extracted abnormal features include abnormal amplitude attenuation, frequency shift, abnormal conduction delay, and phase difference with the response wave of other support points. Map and associate the extracted abnormal features such as abnormal amplitude attenuation, frequency shift, abnormal conduction delay, and phase difference with the abnormal performance of load conduction to generate abnormal path conduction data.

[0107] For the structural transmission nodes at the source of the deviation, abnormal characteristic parameters of their response wave signals are extracted, including abnormal amplitude attenuation (the amplitude attenuation rate is much greater than the normal range); frequency offset (the main frequency of the signal deviates from the reference frequency beyond the allowable deviation); abnormal transmission delay (the signal transmission time is much greater than the normal transmission delay time); and abnormal phase difference (the phase difference between the response waves of other support points is beyond the normal range). These abnormal characteristics are mapped and correlated with abnormal load transmission behaviors, including excessive or insufficient load on the support points, severely uneven load distribution, and abnormal deformation of structural components. For example, abnormal amplitude attenuation may correspond to insufficient load on the support points, frequency offset may correspond to resonance of structural components, and abnormal transmission delay may correspond to loose structural connections. Through correlation analysis, abnormal path transmission data is generated, which details the abnormal characteristics on the transmission path of the deviation source and their corresponding abnormal load transmission behaviors.

[0108] Step S145: Track the diffusion trajectory of the deviation from the source support point to other propagation paths, analyze the changing characteristics of the deviation during the diffusion process and its impact on the interaction of response waves in other paths, generate deviation diffusion correlation data, and mark the abnormal influence range in the deviation diffusion correlation data.

[0109] The abnormal response wave signal generated at the source of the deviation will spread to other conduction paths through structural coupling, affecting the response wave interaction of other support points. The diffusion trajectory of the deviation from the source support point to other conduction paths is tracked, recording the conduction paths, interaction nodes, and diffusion time. The changing characteristics of the deviation during diffusion are analyzed, such as the attenuation or amplification of the deviation and changes in deviation characteristic parameters. The impact of deviation diffusion on the response wave interaction of other paths is assessed, including the degree of interference with the characteristic parameters of other path response waves and whether it leads to persistent deviations in other paths. Based on the diffusion trajectory and impact assessment results, deviation diffusion correlation data is generated. This data includes information such as diffusion path, diffusion time, deviation changes, and impact degree. The abnormal impact range, i.e., the set of all conduction paths and support points affected by the deviation diffusion, is marked in the deviation diffusion correlation data.

[0110] Step S146: Integrate deviation dynamic evolution data, path anomaly transmission data, deviation diffusion correlation data and corresponding support point location information to form preliminary lift load transmission anomaly data, and mark the time, location and diffusion characteristics of the anomaly in the lift load transmission anomaly data.

[0111] The dynamic evolution data of deviations, path anomaly transmission data, deviation diffusion correlation data, and corresponding support point location information generated in the previous steps are integrated to form preliminary lift load transmission anomaly data. During the integration process, anomaly events are used as the core, and data related to the same anomaly event are grouped together. In the lift load transmission anomaly data, each anomaly event is labeled with its start time, end time, duration, and other time information; the location of the anomaly source, the involved transmission path, and the diffusion impact range are labeled; and the diffusion characteristics such as the trajectory, speed, and degree of impact of the deviation diffusion are labeled. Through integration and labeling, the lift load transmission anomaly data can comprehensively and clearly describe the occurrence, development, and impact process of load transmission anomalies.

[0112] Step S147: Map and associate the load conduction coupling correlation data of the lift with the load conduction anomaly data of the lift, extract the feature anomaly items corresponding to the load conduction anomaly data of the lift, and establish the causal relationship between the feature anomaly and the load conduction anomaly.

[0113] The load conduction coupling correlation data of the lifting machine is mapped and correlated with the load conduction anomaly data to identify the corresponding characteristic manifestations of the anomalies described in the load conduction anomaly data in the load conduction coupling correlation data. Feature anomalies corresponding to load conduction anomalies are extracted, namely, the anomalies in the response wave characteristic parameters that cause load conduction anomalies, such as an abnormally increased amplitude attenuation rate or an abnormally decreased frequency stability in a specific conduction path. Causal analysis is used to establish causal relationships between these feature anomalies and the load conduction anomalies, determining which feature anomalies are direct causes and which are indirect causes or consequences of the load conduction anomalies. For example, the feature anomaly "abnormally increased amplitude attenuation rate of path P5" may be a direct cause of the load conduction anomaly "insufficient load at support point A5". This causal relationship is established through this process.

[0114] Step S148: Organize the abnormal load transmission data of the lift in chronological order to generate an abnormal evolution timeline. The abnormal evolution timeline presents the complete process of the abnormality from its generation, development to its spread, and marks the core deviation parameters of each stage.

[0115] The information in the load transfer anomaly data of the lifting machine is sorted out chronologically, and key time points such as the occurrence time of the anomaly, the appearance time of the anomaly characteristics, and the deviation propagation time are arranged in chronological order to generate an anomaly evolution timeline. The timeline clearly presents the complete process of the anomaly from its generation (the appearance of the deviation source), development (the deviation amplifies along the source path), to its spread (the deviation affects other paths and support points). Key deviation parameters for each stage of the timeline are marked, such as the initial deviation value in the anomaly generation stage, the deviation amplification rate in the development stage, and the scope of influence in the spread stage. Through the anomaly evolution timeline, the dynamic development process and key characteristics of the load transfer anomaly can be intuitively understood.

[0116] Step S149: Map and associate the abnormal load transmission data of the lift with the corresponding load condition information, analyze the relationship between the changes in the operating conditions and the evolution of the abnormality, mark the influence of the operating conditions on the abnormal load transmission in the abnormal load transmission data, classify and organize the abnormal load transmission data of the lift according to the type of abnormality, and form a structured abnormal load transmission data of the lift.

[0117] Load condition information includes load size, load type, load application method, lifting speed, and environmental conditions. The load transmission anomaly data of the lift is mapped and correlated with the corresponding load condition information to analyze the relationship between changes in conditions and the evolution of anomalies. For example, does rapid loading easily lead to amplified deviations? Are specific types of loads prone to causing frequency offset anomalies? The impact of different operating conditions on load transmission anomalies is assessed, and the main influencing factors and their impact mechanisms are marked in the lift load transmission anomaly data. Based on the manifestation and cause of the anomalies, the lift load transmission anomaly data is classified and organized according to anomaly type, such as amplitude anomalies, frequency anomalies, phase difference anomalies, and coupling strength anomalies. Each type of anomaly data is structured to form structured lift load transmission anomaly data containing information such as anomaly type, characteristic parameters, impact range, operating conditions, and causal relationships, facilitating the subsequent generation of load calibration waveform signals.

[0118] Step S150: Generate a load calibration waveform signal based on the abnormal load transmission data of the lift, and adjust the response wave transmission state of each support point through the load calibration waveform signal so that the response wave interaction parameters match the load balance dynamic calibration benchmark.

[0119] Based on the generated abnormal load transmission data of the lift, a targeted load calibration strategy is formulated, generating load calibration waveform signals to adjust the response wave transmission state of each support point. The calibration waveform signals are then applied to the corresponding support point structural transmission nodes via the lift's actuators, adjusting the node's mechanical state and thus altering the transmission characteristics of the response waves. This allows the interaction parameters of the response waves at each support point to gradually approach and match the load balance dynamic calibration benchmark, achieving load balance calibration.

[0120] Step S151: Analyze the abnormal load transmission data of the lift, extract the location of the support point of the abnormal source, the abnormal transmission path, the range of deviation spread, the abnormal transmission performance and the abnormal evolution trend, and lock in the core calibration target and adjustment range.

[0121] A detailed analysis of the load transmission anomaly data from the lift was conducted to extract key information: the coordinates and numbers of the support points at the source of the anomaly; the identification and direction of the anomaly transmission path; the transmission paths, support points, and interaction nodes involved in the deviation propagation range; the specific type of anomaly transmission manifestation, such as amplitude anomaly, frequency shift, phase difference anomaly, etc.; and the anomaly evolution trend, such as whether the deviation gradually increases, remains stable, or slowly decays. Based on this information, the core calibration targets were identified, namely the support points at the source of the deviation and the anomaly transmission path that needed to be adjusted first; the adjustment range of the calibration was determined, including the number of support points to be adjusted, the range of the transmission path, and the type of response wave characteristic parameters.

[0122] Step S152: Based on the abnormal conduction performance of the support point at the source of the abnormality, and in conjunction with the standard parameters of the corresponding load range in the load balance dynamic calibration benchmark, determine the adjustment direction and target parameters of the response wave conduction state so that the response wave characteristics of the support point after adjustment conform to the benchmark standard.

[0123] For each abnormal conduction behavior of the support point at the source of the anomaly, such as abnormal amplitude attenuation, consult the standard amplitude parameters for the corresponding load range in the load balancing dynamic calibration benchmark to determine the target value range to which the amplitude needs to be adjusted, thus clarifying the adjustment direction (e.g., increasing amplitude). For frequency deviation, determine the adjustment direction (e.g., increasing or decreasing frequency) and target frequency range based on the benchmark frequency standard. For abnormal conduction delay, determine the adjustment direction (e.g., decreasing delay) and target delay time based on the benchmark conduction delay time. For abnormal phase difference, determine the adjustment direction (e.g., increasing or decreasing phase difference) and target phase difference based on the benchmark phase difference range. Compile the above adjustment directions and corresponding adjustment target parameters into a table to ensure that the response wave characteristics of the support point after adjustment conform to the standard of the load balancing dynamic calibration benchmark.

[0124] Step S1521: In response to the abnormal conduction behavior of abnormal amplitude attenuation, and in combination with the amplitude standard parameters in the load balance dynamic calibration benchmark, determine the direction of amplitude increase of the response wave, set the target value that the adjusted amplitude needs to reach, and make the target value fit the benchmark parameters.

[0125] When abnormal amplitude decay is observed in the load transfer anomaly data of the lift, indicating that the actual response wave amplitude is significantly lower than the lower limit of the amplitude standard in the load balance dynamic calibration benchmark, the amplitude standard parameters for the corresponding load range are retrieved from the load balance dynamic calibration benchmark. These parameters include the lower limit, center value, and upper limit of the amplitude standard. Based on the difference between the current actual amplitude and the standard parameters, the adjustment direction for the response wave amplitude is determined to be the lifting direction. A target value for the adjusted amplitude is set. This target value is typically chosen to be the center value of the amplitude standard or slightly higher than the lower limit, ensuring that the target value closely matches the benchmark parameters and leaving a certain safety margin to avoid over-adjustment.

[0126] Step S1522: Based on the abnormal conduction behavior of frequency offset, and in conjunction with the frequency standard parameters in the load balance dynamic calibration reference, determine the correction direction of the response wave frequency, set the target value and frequency stability range of the corrected frequency, and make the target value and range conform to the reference parameters.

[0127] When an abnormal frequency offset occurs, meaning the dominant frequency of the response wave deviates from the standard frequency range in the load balancing dynamic calibration reference, the standard frequency parameters in the reference are retrieved, including the standard center frequency and the allowable frequency deviation range. The actual dominant frequency is compared with the standard center frequency. If the actual frequency is lower than the standard center frequency, the correction direction is determined to be increasing the frequency; if the actual frequency is higher than the standard center frequency, the correction direction is determined to be decreasing the frequency. The target value of the corrected frequency is set to the standard center frequency, and the frequency stability range is set to the standard center frequency plus or minus the allowable frequency deviation, so that the target value and stability range perfectly match the reference parameters, ensuring that the corrected frequency can be stabilized within the reference range.

[0128] Step S1523: For abnormal conduction behavior of abnormal conduction delay, determine the adjustment direction of response wave conduction velocity by combining the conduction velocity standard parameters in the load balance dynamic calibration benchmark, and set the target value that the conduction delay needs to reach after adjustment so that the target value fits the benchmark parameters.

[0129] An abnormal conduction delay manifests as a propagation time for the response wave along the conduction path that is significantly longer than the normal conduction delay time, corresponding to a conduction velocity lower than the baseline conduction velocity. The standard parameters for the conduction velocity along the corresponding conduction path, including the standard conduction velocity range, are obtained from the load-balanced dynamic calibration benchmark. Based on the inverse relationship between conduction delay time and conduction velocity, the adjustment direction is determined to be increasing the conduction velocity of the response wave. The normal conduction delay time range is calculated based on the length of the conduction path and the standard conduction velocity range. The target value for the adjusted conduction delay is set to be the midpoint of this range, ensuring that the target value closely matches the conduction delay requirements corresponding to the benchmark parameters.

[0130] Step S1524: For abnormal conduction behavior with abnormal phase difference, combine the phase difference standard parameters in the load balance dynamic calibration reference to determine the calibration direction of the response wave phase, set the target value of the phase difference after calibration, and make the target value fit the reference parameter.

[0131] Phase difference typically refers to the phase difference between the response wave and the response waves of other support points exceeding the standard range in the load balancing dynamic calibration reference. The standard phase difference parameters, including the standard phase difference range, are retrieved from the reference. The actual phase difference is compared to the standard range. If the actual phase difference is too large, the calibration direction is determined to be reducing the phase difference; if the actual phase difference is too small, the calibration direction is determined to be increasing the phase difference. The target value for the calibrated phase difference is set to the center value of the standard phase difference range, ensuring that the target value conforms to the reference parameters and that the phase difference is restored to the normal interaction range.

[0132] Step S1525: Map and associate the load conduction coupling data of the abnormal conduction path with the adjustment direction, extract the interaction characteristics of the response waves of each support point on the conduction path, and analyze the influence of the determined adjustment direction on the overall response wave interaction characteristics of the conduction path.

[0133] Retrieve the load conduction coupling correlation data of the lift along the abnormal conduction path, and map and correlate the single-path response wave characteristic data and inter-path coupling response data with the determined adjustment direction. Analyze how the interaction characteristics of the response waves at each support point along the conduction path (such as amplitude ratio, frequency difference, phase difference, coupling strength, etc.) change when the response wave characteristics of the support point at the source of the deviation are adjusted according to the adjustment direction. For example, increasing the response wave amplitude at the support point at the source of the deviation may also increase the amplitude at the downstream support points, thereby changing the coupling strength between paths. Through analysis, assess the degree and trend of the impact of the adjustment direction on the overall response wave interaction characteristics of the conduction path.

[0134] Step S1526: Simulate the interaction state of the overall response wave of the conduction path under the adjusted direction and target parameters, compare the simulation results with the load balance dynamic calibration benchmark, and adjust the parameters to make the overall response wave of the conduction path fit the benchmark.

[0135] The interaction state of the overall response wave of an abnormal transmission path is simulated using a lifting machine structural mechanics model under a defined adjustment direction and target parameters. During the simulation, the response wave characteristic parameters of the calibrated deviation source support point are input, and the response wave characteristic parameters and interaction characteristics of each support point along the transmission path are calculated. The simulated overall response wave interaction characteristics are compared with the standard parameters of the overall response wave in the corresponding load range in the load balance dynamic calibration benchmark to check if any other support point's response wave characteristic parameters exceed the benchmark range. If so, the target parameters or adjustment direction of the deviation source support point are adjusted, and the simulation is repeated until the overall response wave interaction characteristics of the transmission path conform to the load balance dynamic calibration benchmark.

[0136] Step S1527: For other support point response wave anomalies that may be caused by adjusting the direction, preset compensation adjustment parameters are used to map and associate the preset compensation adjustment parameters with the corresponding support point response wave characteristics.

[0137] During the adjustment of the response wave characteristics of the support point at the source of the deviation, new response wave anomalies may appear at other support points due to the coupling effect between paths. Based on the inter-path coupling response data in the load conduction coupling correlation data of the lift, the support points that may be affected and the types of anomalies are predicted. For the above potential anomalies, preset compensation adjustment parameters are set. For example, if adjusting the frequency of the support point at the source of the deviation may cause the frequency of a coupled support point to shift, a preset frequency compensation adjustment amount is set for that coupled support point. The preset compensation adjustment parameters are mapped and correlated with the corresponding support point response wave characteristics to clarify which support point and which response wave characteristic requires compensation adjustment and the adjustment amount, so as to be applied in real time during the calibration process.

[0138] Step S1528: Optimize and adjust the target parameters based on the simulation results, refine the grading criteria for the adjustment range, and set multi-stage adjustment targets so that the response wave propagation state gradually approaches the reference parameters.

[0139] Based on the simulation results, the initially determined adjustment target parameters are optimized. If the simulation shows overshoot or instability during the adjustment process, the adjustment amplitude or rate is appropriately reduced; if the adjustment effect is not significant, the adjustment amplitude is appropriately increased. The grading criteria for the adjustment amplitude are refined, dividing the total adjustment amplitude into multiple levels, such as slight adjustment, moderate adjustment, and significant adjustment, with each level corresponding to a different adjustment range. Multi-stage adjustment targets are set: the first stage target is to adjust the abnormal characteristic parameters to near the edge of the reference range; the second stage target is to adjust to the middle value of the reference range; and the third stage target is to stabilize within the reference range. Through graded adjustment and multi-stage target settings, the response wave propagation state can smoothly and gradually approach and ultimately reach the reference parameters.

[0140] Step S1529: Map and associate the structural mechanical characteristics of the lift with the adjustment direction and target parameters to verify the compatibility of the adjustment direction and target parameters with the structural safety requirements.

[0141] The structural mechanical properties of a lift include safety indicators such as the ultimate strength, ultimate stiffness, and fatigue life of each component. The determined adjustment direction and target parameters are mapped and correlated with these structural mechanical properties to analyze whether the stress and strain experienced by the lift's structural components during adjustment will exceed safety limits. For example, excessive adjustment may cause structural transmission nodes to experience excessive impact forces, leading to plastic deformation or damage. Structural mechanical calculations verify the compatibility of the adjustment direction and target parameters with structural safety requirements, ensuring that the calibration process will not damage the lift structure. If necessary, the target parameters are adjusted to meet safety requirements.

[0142] Step S15210: Integrate the adjustment direction, adjustment target parameters, grading standards and compensation adjustment parameters to form an adjustment plan.

[0143] The adjustment direction (increasing / decreasing amplitude, increasing / decreasing frequency, etc.) determined in the previous steps, the adjustment target parameters (target value, stability range, etc.) for each anomalous feature, the grading criteria for the adjustment magnitude (slight / moderate / significant adjustment), and the preset compensation adjustment parameters are integrated to form an adjustment plan. This adjustment plan clarifies the specific adjustment strategy, implementation steps, and parameter settings for each anomalous feature.

[0144] Step S153: Map and associate the load conduction coupling correlation data of the abnormal conduction path with the adjustment direction, analyze the influence of the adjustment direction on the overall response wave interaction characteristics of the conduction path, calculate the adjustment amplitude and adjustment rate of the response wave signal, and match the adjustment amplitude with the response wave characteristic change threshold.

[0145] Retrieve load conduction coupling correlation data for the abnormal conduction path of the lift, including single-path response wave characteristic data, inter-path coupling response data, and load strength coupling characteristic data. Map and correlate this data with the determined adjustment direction to analyze how the interaction characteristics of the response waves at other support points along the conduction path change when the response wave characteristics of the support point at the source of the deviation are adjusted, such as the changing trends and extent of parameters like amplitude, frequency, and coupling strength. Based on the analysis results, calculate the adjustment amplitude of the response wave signal, i.e., the amount of change required to adjust the abnormal characteristic parameters from the current value to the target value; calculate the adjustment rate, i.e., the amount of adjustment per unit time, to ensure a smooth adjustment process without overshoot. Simultaneously, match the adjustment amplitude with a response wave characteristic change threshold, which is the minimum change in the response wave characteristic parameters that can produce an effective adjustment effect, ensuring that the adjustment amplitude is not less than this threshold to achieve effective calibration.

[0146] Step S154: Based on the adjustment amplitude and adjustment rate, and combined with the structural transmission node characteristics and response wave transmission characteristics of the lift, a targeted load calibration waveform signal is generated so that the signal parameters are adapted to the structural response characteristics of the support point at the source of the deviation.

[0147] Based on the calculated adjustment amplitude and rate, and considering the mechanical characteristics (such as stiffness, damping, and natural frequency) and response wave propagation characteristics (such as wave velocity, wavelength, and attenuation characteristics) of the lifting structure's transmission nodes, the parameters of the load calibration waveform signal are designed. The load calibration waveform signal is typically a periodic or transient signal with a specific frequency and amplitude. Its frequency should be close to the natural frequency of the structural transmission node to improve adjustment efficiency, the amplitude should be determined based on the adjustment amplitude, and the duration should be determined based on the adjustment rate. For example, for adjusting abnormal amplitude attenuation, a sinusoidal calibration signal with gradually increasing amplitude can be generated, with its frequency being the node's natural frequency and its duration calculated based on the adjustment rate. It is crucial to ensure that the generated load calibration waveform signal parameters can adapt to the structural response characteristics of the deviation source support point, enabling the calibration signal to effectively excite the node's mechanical response and achieve precise adjustment of the response wave characteristics.

[0148] Step S155: For other conduction paths and support points involved in the deviation diffusion range, generate a collaborative calibration waveform signal, and combine the response wave interaction data of each support point to match the adjustment amplitude and adjustment timing of each support point.

[0149] For other transmission paths and support points involved in the deviation propagation range, their response wave interactions are also affected by anomalies, requiring the generation of coordinated calibration waveform signals for synchronous adjustment. Based on the lift load transmission anomaly data for these transmission paths and support points, the adjustment direction, adjustment amplitude, and adjustment rate are determined for each. Combining the response wave interaction data of each support point, the response wave coupling relationship between each support point is analyzed, and a coordinated calibration strategy is designed to ensure that the adjustment actions of each support point are coordinated and to avoid generating new interference or conflicts. The adjustment amplitude of each support point is matched so that the adjustment amplitude is proportional to the degree of anomaly; the adjustment sequence of each support point is matched, and the start time of adjustment is set according to the order of deviation propagation. Typically, support points closer to the source of the deviation are adjusted first, followed by support points farther away, to achieve orderly coordinated calibration.

[0150] Step S156: Map and associate the real-time data of the lift response wave interaction dataset with the output timing of the load calibration waveform signal to determine the output timing of the load calibration waveform signal, so that the calibration action is dynamically matched with the load conduction state.

[0151] The real-time data from the lift response waveform interactive dataset reflects the current load conduction state. This real-time data is mapped and correlated with the output timing of the load calibration waveform signal. By monitoring changes in the response waveform characteristic parameters in real time, it is determined when to start outputting the calibration signal, when to adjust the parameters of the calibration signal, and when to stop the calibration signal. For example, when real-time data shows that the deviation begins to decrease and approaches the target value, the amplitude of the calibration signal is gradually reduced or the frequency is adjusted; when real-time data shows a new deviation, the output timing of the calibration signal is adjusted promptly. This allows the calibration action to adaptively adjust according to the dynamic changes in the load conduction state. Specific technical means are used to desensitize potentially privacy-sensitive information in the real-time data (such as detailed load distribution data for specific operations), for example, by obfuscating the data or removing identification information, to protect privacy and prevent data leakage, ensuring optimal calibration results.

[0152] Step S157: Adjust the output amplitude and frequency of the load calibration waveform signal so that the load calibration waveform signal and the response wave signal of the corresponding support point are accurately superimposed, gradually correct the abnormal response wave characteristics, and guide the response wave to move closer to the reference parameters.

[0153] During the output load calibration waveform signal, the response wave signal of the corresponding support point is continuously monitored. Using the signal superposition principle, the output amplitude and frequency of the calibration waveform signal are adjusted to create a constructive or destructive superposition between the calibration signal and the abnormal response wave signal of the support point in the time domain, thereby correcting abnormal characteristics. For example, for frequency offset anomalies, if the response wave frequency is too low, a calibration signal with a frequency slightly higher than the reference frequency is output, gradually guiding the response wave frequency towards the reference frequency. For abnormal amplitude attenuation, a calibration signal with the same phase is output, increasing the response wave amplitude through amplitude superposition. The parameters of the calibration signal are gradually adjusted to slowly correct abnormal response wave characteristics, avoiding system instability caused by excessively rapid adjustments, and guiding the response wave interaction parameters to gradually approach the load balancing dynamic calibration reference parameters.

[0154] Step S158: In response to the abnormal evolution trend, optimize the dynamic adjustment characteristics of the load calibration waveform signal, set phased calibration targets, and gradually reduce the deviation between the response wave interaction and the reference parameters.

[0155] Based on the abnormal evolution trend in the load transmission anomaly data of the lift, such as a continuous increase in deviation, a slow decrease, or a stable state, the dynamic adjustment characteristics of the load calibration waveform signal are optimized. For a continuous increase in deviation, the adjustment amplitude and rate of the calibration signal are increased; for a slow decrease in deviation, a smaller adjustment amplitude and rate are maintained; for a stable deviation, a pulsed calibration signal is used for intermittent adjustment. Staged calibration targets are set, dividing the total adjustment amount into multiple smaller stage targets, each corresponding to a certain deviation reduction. After completing one stage target, the abnormal evolution trend and response waveform interaction state are reassessed, and the calibration parameters for the next stage are adjusted to gradually reduce the deviation between the response waveform interaction and the reference parameters, ultimately achieving perfect matching.

[0156] Step S159: Integrate the targeted load calibration waveform signal, the collaborative calibration waveform signal, the output timing and dynamic adjustment characteristics to form a load calibration waveform signal set. Mark the support point, transmission path and calibration stage corresponding to each signal in the load calibration waveform signal set. Send the load calibration waveform signal set to the lift control system. The lift control system outputs the load calibration waveform signal to adjust the response wave transmission state of each support point.

[0157] The generated targeted load calibration waveform signals (for the source of deviation), collaborative calibration waveform signals (for the range of deviation spread), output timing parameters, and dynamic adjustment characteristics of each signal are integrated to form a load calibration waveform signal set. Each calibration waveform signal in the signal set is labeled with its corresponding support point identifier, transmission path identifier, and calibration stage (e.g., initial calibration stage, fine calibration stage, stabilization stage). The load calibration waveform signal set is sent to the lift control system via a data bus. Based on the labeled information in the signal set, the control system distributes each calibration waveform signal to the corresponding actuator (e.g., hydraulic actuator, electromagnetic vibrator, etc.). The actuator outputs the load calibration waveform signal according to the output timing and dynamic adjustment characteristics, acting on the structural transmission nodes of each support point to adjust the response wave transmission state of the nodes, thereby achieving dynamic calibration of the lift load balance.

[0158] Figure 2 The following is a schematic diagram of the hardware structure of a lift load balance identification system 100 based on sensor integrated monitoring, provided by an embodiment of the present invention, for implementing the above-described lift load balance identification method based on sensor integrated monitoring. Figure 2 As shown, the lift load balance identification system 100 based on sensor integrated monitoring may include a processor 110, a machine-readable storage medium 120, a bus 130, and a communication unit 140.

[0159] In specific implementation, one or more processors 110 execute computer-executable instructions stored in the machine-readable storage medium 120, enabling the processor 110 to execute the lift load balance identification method based on sensor integrated monitoring as described in the above method embodiment. The processor 110, the machine-readable storage medium 120, and the communication unit 140 are connected via a bus 130, and the processor 110 can be used to control the transmission and reception actions of the communication unit 140. The specific implementation process of the processor 110 can be found in the various method embodiments executed by the lift load balance identification system 100 based on sensor integrated monitoring described above; their implementation principles and technical effects are similar, and will not be repeated here.

[0160] Furthermore, this embodiment of the invention also provides a readable storage medium containing computer-executable instructions. When the processor runs the computer-executable instructions, the above-mentioned lift load balance identification method based on sensor integrated monitoring is implemented.

[0161] It should be noted that, in order to simplify the description of this invention and thus aid in the understanding of one or more embodiments, the foregoing description of the embodiments of this invention sometimes combines multiple features into a single embodiment, drawing, or description thereof. Similarly, it should be noted that, in order to simplify the description of this invention and thus aid in the understanding of one or more embodiments, the foregoing description of the embodiments of this invention sometimes combines multiple features into a single embodiment, drawing, or description thereof.

Claims

1. A method for identifying the load balance of a lift based on sensor integrated monitoring, characterized in that, The method includes: The structural response wave signals under load are captured by sensors deployed at the support points of the lift, and a lift response wave interaction dataset is generated. The structural response wave signals include pressure transmission wave signals and deformation feedback wave signals. The lift response wave interaction dataset is associated with the transmission interaction relationship of response waves between each support point. The response wave interaction dataset of the lift is analyzed, and the transmission coupling characteristics of the response waves at different support points are extracted to generate load transmission coupling correlation data of the lift. The load transmission coupling correlation data of the lift reflects the correspondence between the response wave interaction between support points and the load distribution. A load balance dynamic calibration benchmark is generated based on the load conduction coupling correlation data of the lift. The response wave interaction standard parameters of the load balance dynamic calibration benchmark are adjusted as the load level of the lift changes. Track the deviation between the lift response wave interaction dataset and the load balance dynamic calibration benchmark, locate the abnormal location and transmission trajectory of the response wave interaction, and generate abnormal load transmission data of the lift. Based on the abnormal load transmission data of the lift, a load calibration waveform signal is generated. The response wave transmission state of each support point is adjusted by the load calibration waveform signal so that the response wave interaction parameters match the load balance dynamic calibration benchmark.

2. The lift load balance identification method based on sensor integrated monitoring according to claim 1, characterized in that, The sensors deployed at the lift support points capture structural response wave signals under load, generating a lift response wave interactive dataset, including: Based on the distribution of the lift support points, confirm the installation orientation of the pressure wave sensor and deformation wave sensor deployed at each support point, and connect each sensor to the corresponding structural transmission node of the support point one by one, so that the signal acquisition end of the sensor is in contact with the surface of the structural transmission node. Throughout the entire process of load application, lifting, and stabilization of the lift, pressure wave sensors continuously collect pressure transmission wave signals from each support point structural transmission node, and record the numerical sequence of the amplitude, frequency, and transmission speed of the pressure transmission wave signals as a function of the load at fixed time intervals. The deformation feedback wave signal of each support point structural transmission node is synchronously acquired by the deformation wave sensor. The synchronous interaction change of the deformation feedback wave signal and the pressure transmission wave signal is captured at the same time interval, and the numerical trajectory of the phase difference between the two changes with time is recorded. The pressure transmission wave signal and deformation feedback wave signal of the same support point structural transmission node are combined one by one according to the acquisition time to generate support point response wave data pairs. Support point identifier, structural transmission node name and load action stage labeling information are added to each support point response wave data pair. The signal interaction feature parameters in the support point response wave data pairs are extracted. The extracted signal interaction feature parameters include amplitude ratio, frequency difference and average phase difference. The extracted signal interaction feature parameters such as amplitude ratio, frequency difference and average phase difference are mapped and associated with the load action density of the corresponding support point to generate branch point load response wave association data. Integrate the load response wave correlation data of all support points, and add the overall response wave signal of the main structure of the lift and the response wave transmission path information between each support point to form a lift response wave interactive dataset. The initial response wave interaction dataset of the lift is classified and collected according to the response wave transmission path between each support point. The interaction time sequence change characteristics of the response wave signal on each response wave transmission path are extracted. The differences in response wave characteristics and the distinguishing markers of interaction modes of different response wave transmission paths are marked in the response wave interaction dataset of the lift. The lift response wave interaction dataset is mapped and associated with the load application area data. The corresponding load application area, transmission direction and specific location of the interaction node are marked in each response wave signal entry, and spatial association attributes are added to the lift response wave interaction dataset. Environmental interference wave signals and invalid sensor capture data are removed from the lift response wave interaction dataset, and valid response wave data are retained. The valid response wave data are then sorted and organized according to the conduction path and load action stage to form a structured lift response wave interaction dataset.

3. The lift load balance identification method based on sensor integrated monitoring according to claim 1, characterized in that, The analytical lift response wave interaction dataset is used to extract the transmission coupling characteristics of the response waves at different support points, generating lift load transmission coupling correlation data, including: Call the preset lift structural mechanics model, and load the structural parameters of each part of the lift, the characteristics of the transmission nodes, and the transmission path data between the support points into the lift structural mechanics model; The interactive dataset of the lift response wave is input into the structural mechanics model of the lift. The response wave signal is processed in layers according to the transmission path between support points to separate the response wave signal that is independently transmitted on each response wave transmission path and the response wave signal that is coupled between different paths. The response wave signal features of each independent conduction path are extracted. The extracted response wave signal features include amplitude attenuation rate, frequency stability and conduction delay time. The extracted response wave signal features such as amplitude attenuation rate, frequency stability and conduction delay time are associated and labeled with the corresponding conduction path and support point to generate single-path response wave feature data. The response wave signals of coupling and propagation between different paths are analyzed, and the phase superposition characteristics, frequency modulation characteristics and energy transfer efficiency of the coupled waves are extracted. The extracted coupling characteristics such as phase superposition characteristics, frequency modulation characteristics and energy transfer efficiency are associated and labeled with the corresponding participating paths to generate inter-path coupling response data. The single-path response wave characteristic data and the inter-path coupling response data are mapped and associated to extract the characteristics of the change of the single-path response wave characteristics by the coupling effect. The extracted characteristics of the change of the single-path response wave characteristics by the coupling effect are added to the basic data of feature analysis. Dynamic change parameters of response wave features are extracted from the time evolution trajectory of the lift response wave interaction dataset. The extracted dynamic change parameters include the rate of change and the amplitude of change of feature parameters. The extracted dynamic change parameters such as the rate of change and amplitude of change are correlated with the load conduction strength to generate load strength coupling feature data. By integrating single-path response wave characteristic data, inter-path coupling response data, path coupling influence data, and load intensity coupling characteristic data, preliminary load conduction coupling correlation data of the lift is formed. The initial load transmission coupling correlation data of the lift is classified and organized according to the load transmission intensity. The core coupling features of each transmission path and the interaction points between paths under different load intensities are marked in the load transmission coupling correlation data. The core feature data in the load transmission coupling correlation data of the lift is screened, and the processed core feature data is grouped and collected according to the transmission path to form structured load transmission coupling correlation data of the lift.

4. The lift load balance identification method based on sensor integrated monitoring according to claim 1, characterized in that, The generation of a load balance dynamic calibration benchmark based on lift load conduction coupling correlation data includes: Obtain the rated load parameters of the lift and the load-bearing standards of each transmission path. The load-bearing standards include the rated transmission energy of each response wave transmission path, the allowable fluctuation range of the response wave, and the coupling strength standard between paths. Use the rated load parameters of the lift, the rated transmission energy of each response wave transmission path, the allowable fluctuation range of the response wave, and the coupling strength standard between paths as the basic reference data for calibration. The load range is divided into multiple load ranges according to the load level of the lift. Each load range is mapped and associated with the corresponding features in the load conduction coupling correlation data of the lift. For each load range, the stable range of response wave interaction is extracted from the load conduction coupling correlation data of the lift. The response wave amplitude standard, frequency standard and inter-path coupling strength standard of each conduction path in the load range are determined. Each load range corresponds to a continuous load range. The standard parameters of the response wave in the stable range are mapped and correlated with the rated load parameters of the lift to verify the compatibility of the standard parameters of the response wave with the structural safety and load-bearing requirements of the lift, and the numerical range of the standard parameters of the response wave is adjusted. Extract the optimal interaction mode of the response wave between support points within each load interval, map and associate the optimal interaction mode with the corresponding load distribution state, and determine the optimal load distribution ratio for each load interval. By integrating the response wave standard parameters, optimal load distribution ratio, and path coupling standards of each load range, a preliminary load balance dynamic calibration benchmark is formed. Load range labels corresponding to each benchmark parameter are added to the load balance dynamic calibration benchmark. Simulate the interactive change process of response waves when switching between different load ranges, capture the dynamic transition characteristics of response wave features, and generate a smooth transition curve of reference parameters based on the captured dynamic transition characteristics of response wave features. Retrieve historical valid data from the lift response wave interaction dataset, compare the historical valid data with the parameters of the preliminary load balance dynamic calibration benchmark, extract parameter deviation data, and correct the standard parameters in the load balance dynamic calibration benchmark based on the extracted parameter deviation data. The load balance dynamic calibration benchmarks are sorted and organized in ascending order of load magnitude. The adjustment characteristics of the benchmark parameters for each load range are marked in the load balance dynamic calibration benchmarks. The benchmark parameters of each load range are connected in series. The corrected benchmark parameters are integrated with the transition curve to form the load balance dynamic calibration benchmarks.

5. The lift load balance identification method based on sensor integrated monitoring according to claim 1, characterized in that, The deviation between the tracking lift response wave interaction dataset and the load balance dynamic calibration benchmark is used to locate the abnormal location and transmission trajectory of the response wave interaction, and generate abnormal load transmission data for the lift, including: According to the load range and transmission path, the lift response wave interactive dataset is compared point by point with the corresponding standard parameters in the load balance dynamic calibration benchmark. The deviation data between the response wave characteristics and the benchmark parameters at each moment is extracted, and the corresponding time, transmission path and support point labels are added to the deviation data between the response wave characteristics and the benchmark parameters. The evolution trend of the deviation data between the response wave characteristics and the reference parameters is analyzed to distinguish between instantaneous deviation and continuous deviation. For continuous deviation, the deviation amplification rate, deviation stability range and deviation fluctuation frequency are extracted. The extracted deviation characteristic data such as deviation amplification rate, deviation stability range and deviation fluctuation frequency are mapped and correlated with the load conduction state to generate dynamic deviation evolution data. Based on the dynamic evolution data of the deviation, the transmission path that generates the continuous deviation is located, and the response wave signals of the structural transmission nodes of each support point on the transmission path are traced to lock the position of the support point and the structural transmission node corresponding to the source of the deviation. The abnormal features of the response wave signal at the support point of the deviation source are extracted. The extracted abnormal features include abnormal amplitude attenuation, frequency shift, abnormal conduction delay, and phase difference with the response wave of other support points. The extracted abnormal features such as abnormal amplitude attenuation, frequency shift, abnormal conduction delay, and phase difference are mapped and correlated with the abnormal performance of load conduction to generate abnormal path conduction data. Track the diffusion trajectory of the deviation from the source support point to other propagation paths, analyze the changing characteristics of the deviation during the diffusion process and its impact on the interaction of response waves in other paths, generate deviation diffusion correlation data, and mark the range of abnormal influence in the deviation diffusion correlation data. By integrating deviation dynamic evolution data, path anomaly transmission data, deviation diffusion correlation data, and corresponding support point location information, preliminary lift load transmission anomaly data is formed. The time, location, and diffusion characteristics of the anomaly are marked in the lift load transmission anomaly data. Map and associate the load conduction coupling correlation data of the lift with the load conduction anomaly data of the lift, extract the feature anomalies corresponding to the load conduction anomalies data, and establish the causal relationship between the feature anomalies and the load conduction anomalies. The abnormal load transmission data of the lift is sorted out in chronological order to generate an abnormal evolution timeline. The abnormal evolution timeline presents the complete process of the abnormality from its generation, development to its spread, and marks the core deviation parameters of each stage. The abnormal load transmission data of the lift is mapped and associated with the corresponding load condition information. The relationship between the changes in the operating conditions and the evolution of the abnormality is analyzed. The influence of the operating conditions on the abnormal load transmission data is marked in the abnormal load transmission data. The abnormal load transmission data of the lift is classified and organized according to the type of abnormality to form a structured abnormal load transmission data of the lift.

6. The lift load balance identification method based on sensor integrated monitoring according to claim 5, characterized in that, The method involves locating the transmission path that generates continuous deviation based on deviation dynamic evolution data, tracing the response wave signals of each support point structural transmission node along the transmission path, determining the support point location and structural transmission node corresponding to the deviation source, and analyzing the impact of abnormal transmission on the overall response wave interaction of the path, including: Extract the transmission path identifier corresponding to the continuous deviation from the deviation dynamic evolution data, call the lift response wave interaction data and lift load transmission coupling correlation data corresponding to the transmission path, and use the lift response wave interaction data and lift load transmission coupling correlation data corresponding to the transmission path as the basic data for positioning analysis. The response wave signals of each support point structural transmission node along the transmission path are traced according to the direction of the response wave transmission. The response wave characteristics are compared with the load balance dynamic calibration benchmark node by node from the support point at the beginning of the transmission path to the support point at the end of the transmission path, and the first structural transmission node with a continuous deviation is selected. Identify the support point location corresponding to the first structural transmission node with continuous deviation, extract the response wave signal capture data of the support point, analyze the integrity and authenticity of the signal capture, and eliminate the deviation data caused by sensor capture error; For the structural transmission nodes of the support points at the source of deviation, the abnormal characteristics of the response wave signals are analyzed. The extracted abnormal characteristics include abnormal amplitude attenuation, frequency shift, abnormal transmission delay, and abnormal phase difference with the response waves of other support points. The specific manifestations of abnormal transmission are marked in the analysis results. The single-path response wave characteristic data of the conduction path is compared with the overall response wave interaction characteristics of the conduction path before and after abnormal conduction. The change in characteristics is extracted, and the impact of abnormal conduction at the source of deviation on the overall conduction efficiency and response wave stability of the conduction path is analyzed. Extract the response wave signal changes of other support point structural transmission nodes on the transmission path, map and associate the response wave signal change data of other support point structural transmission nodes on the transmission path with the abnormal transmission of the deviation source, mark the degree of influence and transmission characteristics of the deviation source on the transmission of subsequent nodes on the transmission path, and generate inter-node transmission influence data. By combining the structural mechanics model of the lift, the potential impact of abnormal transmission from the source of deviation on the structural strength and load-bearing capacity of the transmission path is simulated. The simulation results are used to generate path structure impact data, which are then added to the abnormal transmission analysis results. By integrating the location of the support point of the deviation source, information on structural transmission nodes, abnormal transmission manifestations, overall path impact data, and inter-node transmission impact data, a path abnormal transmission analysis result is formed. The path anomaly transmission analysis results are organized in a structured format. After determining the correlation between each data item in the path anomaly transmission analysis results, the path anomaly transmission analysis results are correlated with the deviation dynamic evolution data and supplemented into the lift load transmission anomaly data.

7. The lift load balance identification method based on sensor integrated monitoring according to claim 1, characterized in that, The process of generating a load calibration waveform signal based on abnormal load conduction data of the lift, and adjusting the response wave conduction state of each support point through the load calibration waveform signal to match the response wave interaction parameters with the load balance dynamic calibration benchmark, includes: Analyze the abnormal load transmission data of the lift, extract the location of the support point of the abnormal source, the abnormal transmission path, the range of deviation spread, the abnormal transmission performance and the abnormal evolution trend, and identify the core calibration target and adjustment range; In response to the abnormal conduction behavior of the support point at the source of the abnormality, the adjustment direction and target parameters of the response wave conduction state are determined by combining the standard parameters of the corresponding load range in the load balance dynamic calibration benchmark, so that the response wave characteristics of the support point after adjustment conform to the benchmark standard. The load conduction coupling correlation data of the abnormal conduction path of the lift is mapped and correlated with the adjustment direction. The influence of the adjustment direction on the overall response wave interaction characteristics of the conduction path is analyzed, the adjustment amplitude and adjustment rate of the response wave signal are calculated, and the adjustment amplitude and response wave characteristic change threshold are matched. Based on the adjustment amplitude and adjustment rate, combined with the structural transmission node characteristics and response wave transmission characteristics of the lift, a targeted load calibration waveform signal is generated so that the signal parameters are adapted to the structural response characteristics of the support point at the source of the deviation. For other transmission paths and support points involved in the deviation diffusion range, generate collaborative calibration waveform signals, and combine the response wave interaction data of each support point to match the adjustment amplitude and adjustment timing of each support point; The real-time data of the lift response wave interactive dataset is mapped and associated with the output timing of the load calibration waveform signal to determine the output timing of the load calibration waveform signal, so that the calibration action is dynamically matched with the load conduction state. Adjust the output amplitude and frequency of the load calibration waveform signal to make the load calibration waveform signal and the response wave signal of the corresponding support point accurately superimposed, gradually correct the abnormal response wave characteristics, and guide the response wave to move closer to the reference parameters. In response to the abnormal evolution trend, the dynamic adjustment characteristics of the load calibration waveform signal are optimized, and phased calibration targets are set to gradually reduce the deviation between the response waveform interaction and the reference parameters. The targeted load calibration waveform signal, the collaborative calibration waveform signal, the output timing and dynamic adjustment characteristics are integrated to form a load calibration waveform signal set. The support point, transmission path and calibration stage corresponding to each signal are marked in the load calibration waveform signal set. The load calibration waveform signal set is sent to the lift control system. The lift control system outputs the load calibration waveform signal to adjust the response wave transmission state of each support point.

8. The lift load balance identification method based on sensor integrated monitoring according to claim 7, characterized in that, The abnormal transmission behavior at the support point of the abnormal source is analyzed by combining the standard parameters of the corresponding load range in the load balance dynamic calibration benchmark to determine the adjustment direction and target parameters of the response wave transmission state. The impact of the adjustment direction on the overall response wave interaction characteristics of the transmission path is analyzed by combining the lifting load transmission coupling correlation data of the abnormal transmission path, including: In response to the abnormal conduction behavior of abnormal amplitude attenuation, the amplitude standard parameters in the load balance dynamic calibration benchmark are combined to determine the direction of the increase of the response wave amplitude, and set the target value that the adjusted amplitude needs to reach so that the target value fits the benchmark parameters. In response to the abnormal conduction behavior of frequency offset, the correction direction of the response wave frequency is determined by combining the frequency standard parameters in the load balance dynamic calibration reference, and the target value and frequency stability range of the corrected frequency are set so that the target value and range conform to the reference parameters. In response to the abnormal conduction behavior of abnormal conduction delay, the direction of adjustment of the response wave conduction velocity is determined by combining the standard parameters of conduction velocity in the load balance dynamic calibration benchmark, and the target value of the conduction delay after adjustment is set so that the target value fits the benchmark parameters. To address the abnormal conduction behavior of the phase difference, the calibration direction of the response wave phase is determined by combining the phase difference standard parameters in the load balance dynamic calibration reference, and the target value of the phase difference after calibration is set so that the target value fits the reference parameters. The load conduction coupling correlation data of the lift in the abnormal conduction path is mapped and correlated with the adjustment direction. The interaction characteristics of the response waves of each support point on the conduction path are extracted, and the influence of the determined adjustment direction on the overall response wave interaction characteristics of the conduction path is analyzed. The interaction state of the overall response wave of the conduction path under the adjustment of direction and target parameters is simulated. The simulation results are compared with the load balance dynamic calibration benchmark. The parameters are adjusted to make the overall response wave of the conduction path fit the benchmark. To address potential anomalies in the response waves of other support points that may be caused by adjusting the direction, preset compensation adjustment parameters are used to map and associate the preset compensation adjustment parameters with the corresponding support point response wave characteristics. Based on the simulation results, the target parameters are optimized and adjusted, the grading criteria for the adjustment range are refined, and multi-stage adjustment targets are set so that the response wave propagation state gradually approaches the reference parameters. The structural mechanical characteristics of the lift are mapped and correlated with the adjustment direction and target parameters to verify the compatibility of the adjustment direction and target parameters with the structural safety requirements. The adjustment plan is formed by integrating the adjustment direction, adjustment target parameters, classification standards and compensation adjustment parameters.

9. The lift load balance identification method based on sensor integrated monitoring according to claim 3, characterized in that, The analysis of response wave signals coupled and conducted between different paths involves extracting the phase superposition characteristics, frequency modulation characteristics, and energy transfer efficiency of the coupled waves to generate inter-path coupling response data. By combining single-path response wave characteristic data with inter-path coupling response data, the characteristics of how coupling alters the single-path response wave characteristics are analyzed, including: The response wave signals coupled and propagated between different paths are separated from the analyzed response wave signals. The corresponding participating propagation paths and interaction nodes are marked in the coupled wave signals to lock the propagation range of the coupled waves. Extract the phase superposition characteristics of the coupled waves, analyze the phase change characteristics of the response waves after superposition of different paths, calculate the phase shift and phase stability after superposition, and record the influence of phase superposition on the propagation of the coupled waves. The frequency modulation characteristics of the coupled wave are analyzed, the frequency interaction modes of the response waves of different paths are labeled, the frequency variation range and modulation efficiency of the frequency-modulated wave are extracted, and the extracted frequency variation range, modulation efficiency and other data are mapped and correlated with the load conduction strength. Calculate the energy transfer efficiency of the coupled wave, statistically analyze the energy attenuation and transfer rate during the coupling process, and map and correlate the statistical data such as energy attenuation and transfer rate with the structural connection characteristics between paths to generate energy transfer data. By integrating phase superposition characteristics, frequency modulation characteristics, and energy transfer data, inter-path coupling response data is formed. The coupling path and load conduction state corresponding to each data item are marked in the inter-path coupling response data. The characteristic data of the single-path response waves involved in the coupling are retrieved, and the amplitude, frequency, propagation velocity and phase characteristics of the single-path response waves before and after coupling are compared. The characteristic changes are extracted, and the extracted characteristic changes are mapped and correlated with the inter-path coupling response data. The characteristics of the change of single-path response wave features by the coupling effect are marked, and the correlation mode between coupling strength and characteristic changes is determined. For the coupling effect under different load intensities, the characteristics of the change of single-path response wave features by coupling are extracted respectively, and load-dependent coupling effect data are generated to supplement the analysis results. The path coupling response data, single-path characteristic changes, and load-dependent coupling impact data are integrated to form the path coupling impact analysis results. The path coupling impact analysis results are then classified according to load intensity and supplemented to the lift load transmission coupling correlation data.

10. A lift load balance identification system based on sensor integrated monitoring, characterized in that, The lift load balance identification system based on sensor integrated monitoring includes a processor and a memory, the memory and the processor are connected, the memory is used to store programs, instructions or code, and the processor is used to run the programs, instructions or code in the memory to implement the lift load balance identification method based on sensor integrated monitoring as described in any one of claims 1-9.

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