A correction system and correction method based on elevator dynamics model

Abstract

The present invention relates to the technical field of elevator dynamics, and in particular to a correction system and correction method based on an elevator dynamics model. The method comprises the following steps: obtaining elevator design data and performing structural analysis, thereby obtaining elevator structural data and power source data, and constructing an elevator power simulation model; obtaining elevator power data through simulation, and analyzing the elevator overload condition based on the data, and estimating the abnormal state of the transmission system; performing aging trend analysis and performance degradation detection in combination with the abnormal state data of the transmission system, and estimating the required power increase of the elevator; predicting the accumulation effect of power errors based on performance degradation data and power demand data, analyzing abnormal fluctuations, and adjusting the elevator power simulation model; the present invention optimizes the elevator power model to achieve more stable elevator power.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator dynamics models, and in particular to a correction system and correction method based on an elevator dynamics model. Background Art

[0002] As one of the core components of elevator operation, the working state of the elevator transmission system is easily affected by many factors during long-term operation, such as mechanical wear, uneven load distribution, and changes in ambient temperature. If these problems are not discovered and handled in time, the elevator's operating performance will decline. The existing elevator dynamic simulation model is usually established in the initial design stage. With the changes in the operating environment and the aging of the equipment, its accuracy will gradually decrease, and it is difficult to effectively reflect the actual operating state of the elevator. By introducing dynamic simulation and real-time data correction technology, all-round monitoring and prediction of the elevator's operating state can be achieved, overcoming the shortcomings of traditional maintenance methods. Based on the elevator design data, this method combines multi-dimensional parameters such as dynamic analysis, load distribution detection, and friction heat effect to gradually build and optimize the elevator dynamic simulation model. However, the traditional correction system and correction method based on the elevator dynamic model have the problem of inaccurate analysis of the cumulative effect of the elevator dynamic error and inaccurate analysis of the elevator aging trend. Summary of the invention

[0003] Based on this, it is necessary to provide a correction system and correction method based on an elevator dynamics model to solve at least one of the above technical problems.

[0004] To achieve the above object, a correction method based on an elevator dynamics model comprises the following steps:

[0005] Step S1: acquiring elevator design data; performing elevator structure analysis according to the elevator design data to obtain elevator structure data; performing elevator power source analysis based on the elevator structure data to obtain elevator power source data; constructing an elevator power simulation model according to the elevator power source data and the elevator structure data to obtain elevator power simulation model data;

[0006] Step S2: performing elevator power simulation based on the elevator power simulation model data to obtain elevator power simulation data; performing elevator transmission system abnormal state estimation based on the elevator power simulation data to obtain elevator transmission system abnormal state data;

[0007] Step S3: performing an aging trend analysis of the elevator operation transmission system according to the abnormal state data of the elevator transmission system to obtain the aging trend data of the elevator operation transmission system; performing an elevator operation performance degradation detection according to the aging trend data of the elevator operation transmission system to obtain the elevator operation performance degradation data; performing an elevator demand power increase estimation based on the elevator operation performance degradation data to obtain the elevator demand power increase data;

[0008] Step S4: Estimate the power error accumulation effect based on the elevator operation performance degradation data and the elevator demand power increase data to obtain the elevator power error accumulation effect data; perform power abnormal fluctuation analysis based on the elevator power error accumulation effect data to obtain the elevator operation power abnormal fluctuation data; calibrate the elevator power simulation model data based on the elevator operation power abnormal fluctuation data and the elevator power error accumulation effect data to obtain the elevator power simulation correction model.

[0009] The present invention can significantly improve the monitoring accuracy of the elevator operation state and the intelligence level of the system through a correction method based on the elevator dynamics model. Acquiring elevator design data and combining it with structural analysis can help to fully understand the physical characteristics of the elevator, provide a reliable basis for the construction of subsequent dynamic models, and ensure that the model can accurately reflect the actual structural characteristics of the elevator. By analyzing the power source of the elevator, the various power factors involved in the operation of the elevator are deeply understood, and the adaptability of the model to different power inputs during the simulation process is enhanced, thereby improving the accuracy of the dynamic simulation model. Simulation is carried out based on the dynamic model to simulate the actual operation state of the elevator, which helps to quickly capture the load anomalies or transmission system problems that occur in the elevator under various operating conditions, and provide a reliable basis for the identification and prediction of subsequent abnormal states. On the basis of abnormal state data, by analyzing the aging trend of the transmission system, the wear and performance degradation of key components are timely evaluated, and accurate aging trend data is provided for maintenance personnel to help formulate a scientific and reasonable maintenance plan. The decline detection of operating performance can quantify the degree of overall performance decline of the elevator, providing key support for the subsequent prediction of power demand growth. Through the estimation of power demand increase, the power shortage problem faced by the elevator can be identified in advance, providing data basis for further optimizing the transmission system and improving operation efficiency. In the estimation of the cumulative effect of dynamic errors, it is possible to identify the source of the error and quantify its long-term impact, which is crucial to ensuring the stability and safety of elevator operation. By analyzing the abnormal fluctuation phenomenon, the irregular dynamic changes that occur during the operation of the elevator can be further refined, providing an accurate reference for timely adjustment of the dynamic model. The dynamic matching of the simulation model and the actual operating state is achieved through model correction, which not only improves the accuracy and reliability of the model, but also provides important guarantees for the intelligent operation and maintenance and fault prevention of the elevator. Therefore, the present invention is an optimization of a traditional correction method based on an elevator dynamic model, which solves the problem of inaccurate analysis of the cumulative effect of elevator dynamic errors and inaccurate analysis of elevator aging trends in a traditional correction method based on an elevator dynamic model, and improves the accuracy of the analysis of the cumulative effect of elevator dynamic errors and the accuracy of the analysis of elevator aging trends.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Obtain elevator design data;

[0012] Step S12: performing elevator structure analysis according to the elevator design data, thereby obtaining elevator structure data;

[0013] Step S13: Analyze the elevator power source according to the elevator structure data and the elevator design data to obtain the elevator power source data;

[0014] Step S14: constructing an elevator power simulation model according to the elevator power source data and the elevator structure data to obtain elevator power simulation model data.

[0015] The present invention obtains and analyzes the elevator design data to ensure that all subsequent steps are based on accurate and detailed initial information, thereby laying a solid foundation for the entire dynamic correction process. The elevator design data provides the core parameters of the system design, including structure, material, and power configuration, which can fully reflect the design concept and engineering goals of the elevator. Structural analysis on this basis can more intuitively grasp the internal structure and operation logic of the elevator system, and provide a specific reference for subsequent power source analysis and model construction. Analyzing the power source of the elevator in combination with structural data and design data helps to clarify the actual contribution and action mechanism of various power drives in the system, thereby improving the model's ability to describe complex dynamic behaviors. By constructing an elevator power simulation model based on the above data, the operating state of the elevator is theoretically restored, providing an accurate calculation tool for subsequent simulation and operation optimization. The entire process aims to open up the entire chain from design to model, ensuring that the construction of the dynamic model has both theoretical basis and practical application, thereby providing support for improving elevator operating performance, reducing maintenance costs, and extending equipment life.

[0016] Preferably, step S14 comprises the following steps:

[0017] Step S141: Calculate the elevator structure size according to the elevator structure data to obtain the elevator structure size data;

[0018] Step S142: analyzing the position of the elevator guide rail according to the elevator structure data to obtain the position data of the elevator guide rail;

[0019] Step S143: using the elevator guide rail position data and the elevator structure size data to collect the elevator's three-dimensional structure to obtain the elevator's three-dimensional structure data;

[0020] Step S144: analyzing the elevator running direction according to the elevator three-dimensional structure data and the elevator guide rail position data to obtain the elevator running direction data;

[0021] Step S145: performing elevator power size detection on the elevator power source data to obtain elevator power size data;

[0022] Step S146: constructing an elevator power simulation model based on the elevator three-dimensional structure data, the elevator running direction data and the elevator power size data to obtain the elevator power simulation model data.

[0023] The present invention calculates the structural dimensions of the elevator structural data to accurately obtain the dimensional parameters of each component of the elevator, which is crucial for subsequent analysis and modeling. The dimensional data provides a physical basis for the elevator dynamics model and ensures the accuracy of the simulation model at the structural level. Further analysis of the elevator guide rail position can provide a detailed understanding of the layout and positioning of the guide rails, which helps to evaluate the friction changes and guide rail-related problems encountered during the operation of the elevator, thereby providing data support for improving the stability of elevator operation. The three-dimensional structure is collected using the guide rail position and dimensional data to accurately reconstruct the three-dimensional spatial layout of the elevator, which helps to more intuitively understand the relationship between the elevator structure and the power system, and provides a more realistic geometric basis for subsequent dynamic simulation. The elevator running direction is analyzed by analyzing the three-dimensional structure data and the guide rail position data, and the movement path of the elevator is accurately determined, providing key parameters for the movement direction and speed control in the simulation. In addition, detecting the power size of the elevator's power source helps to accurately quantify the power level required by the elevator during operation, avoiding equipment damage caused by insufficient power or overload. Combining three-dimensional structural data, operating direction and power size data to build a power simulation model can more comprehensively reflect the actual operating characteristics of the elevator, ensure accuracy and reliability in dynamic simulation, and provide strong support for subsequent performance optimization and fault prevention.

[0024] Preferably, step S2 comprises the following steps:

[0025] Step S21: performing elevator power simulation based on the elevator power simulation model data to obtain elevator power simulation data;

[0026] Step S22: performing elevator overload detection based on elevator dynamics simulation data to obtain elevator operation overload data;

[0027] Step S23: Based on the elevator power simulation data, the elevator operation overload data is used to predict the abnormal state of the elevator transmission system to obtain the abnormal state data of the elevator transmission system.

[0028] The present invention effectively restores the dynamic characteristics of the elevator in actual operation by performing power simulation based on the elevator power simulation model data, captures the power changes of the elevator in each operation stage, and provides an accurate numerical basis for subsequent analysis. The simulation data can not only reflect the dynamic behavior of the elevator system, but also help identify potential performance problems, and provide a basis for optimizing design and fault warning. Next, based on the simulation data, overload detection is performed to effectively identify whether the elevator is overloaded during operation, timely discover potential safety hazards, and prevent system damage or failure caused by excessive load. This detection can accurately determine the specific situation of uneven load or overload through data analysis, thereby providing a guarantee for the safe operation of the elevator, and using the overload data to predict the abnormal state of the elevator transmission system, which helps to discover the abnormal state of the elevator transmission system under high load or long-term operation in advance, including wear, eccentricity or potential failures of transmission components. This prediction can not only help with regular maintenance and overhaul, but also extend the service life of elevator equipment, avoid sudden mechanical failures, and ensure the stable operation of the elevator system.

[0029] Preferably, step S23 includes the following steps:

[0030] Step S231: Classify the elevator types based on the elevator power simulation data to obtain elevator car power simulation data and escalator power simulation data;

[0031] Step S232: Predicting the local wear of the elevator traction sheave based on the elevator car power simulation data and the elevator operation overload data, and obtaining the local wear data of the elevator traction sheave;

[0032] Step S233: performing escalator power simulation data analysis on the elevator operation overload data according to the escalator power simulation data to obtain escalator transmission system wear data;

[0033] Step S234: based on the wear data of the escalator transmission system and the local wear data of the elevator traction wheel, the abnormal state of the elevator transmission system is estimated to obtain the abnormal state data of the elevator transmission system.

[0034] The present invention divides the elevator power simulation data into types and performs more accurate analysis for different types of elevators, so that the dynamic characteristics of the car and escalator can be processed and optimized separately. This division helps to identify the unique needs of different types of elevators in actual operation and improve the adaptability and simulation accuracy of the model. Load overload analysis is performed on the elevator car power simulation data to effectively evaluate the wear degree of the traction wheel under high-load operation, identify component fatigue and wear phenomena in advance, thereby providing a scientific basis for maintenance and replacement and reducing the occurrence of sudden failures. Similarly, analysis of escalator power simulation data helps to identify potential wear problems of the escalator transmission system, especially system stress accumulation and wear patterns under uneven load conditions. This analysis can optimize the design and operation and maintenance plan of the escalator and extend its service life. Combined with the local wear data of the traction wheel and the wear data of the escalator transmission system, the health status of the entire elevator transmission system is more comprehensively evaluated, and abnormal conditions are warned in time. Through accurate prediction of abnormal conditions of the transmission system, data support is provided for the safe operation of the elevator, helping to discover and repair potential faults in advance, avoiding operation interruptions or larger-scale damage, and improving the stability and reliability of the elevator.

[0035] Preferably, step S232 includes the following steps:

[0036] Performing uneven load distribution detection on the elevator according to the elevator car dynamic simulation data and the elevator operation overload data to obtain uneven load distribution data on the elevator;

[0037] According to the uneven distribution data of elevator running load and the excessive load data of elevator running, the growth trend of elevator traction rope tension is analyzed to obtain the growth trend data of elevator traction rope tension;

[0038] Based on the elevator traction rope tension growth trend data, the traction wheel load direction is estimated to obtain the traction wheel positive load direction data;

[0039] Based on the elevator operation overload data and the elevator traction rope tension growth trend data, the traction wheel positive pressure growth is estimated to obtain the traction wheel positive pressure growth data;

[0040] According to the traction wheel positive pressure growth data, the traction wheel friction force growth analysis is carried out to obtain the elevator traction wheel friction force growth data;

[0041] According to the traction wheel positive eccentric load direction data and the elevator running friction force growth data, the friction heat effect is processed to obtain the elevator running friction heat effect data;

[0042] The local wear of the elevator traction sheave is estimated based on the frictional heat effect data of the elevator operation and the positive eccentric load direction data of the traction sheave, and the local wear data of the elevator traction sheave is obtained.

[0043] The present invention effectively identifies the uneven load phenomenon existing in the operation of the elevator by performing uneven load distribution detection on the dynamic simulation data of the elevator car and the excessive load data of the operation. This detection helps to discover the potential risks caused by uneven load and ensure that the various components of the elevator operate under a reasonable load, thereby avoiding failures or excessive wear caused by uneven load. Combined with the uneven load distribution data and the excessive load data, the traction rope tension growth trend analysis is performed to accurately predict the tension change trend of the elevator traction rope in long-term operation, identify the problem of excessive tension or imbalance in advance, and provide data basis for optimizing elevator operation and avoiding failures such as rope breakage. By analyzing the tension growth trend, the eccentric load direction of the traction wheel is further estimated, and the load change of the traction wheel during operation is effectively predicted, avoiding abnormal wear and performance degradation of the traction wheel caused by uneven load. Based on the tension growth trend, the positive pressure growth of the traction wheel is estimated, and the force condition of the traction wheel is evaluated, so as to predict its pressure increase in advance and take corresponding preventive measures. With the increase of the positive pressure of the traction wheel, the friction growth analysis is performed to reveal the friction between the traction wheel and the track, providing guidance for timely adjustment of the operation strategy or maintenance. Furthermore, the processing of frictional thermal effects helps to simulate and analyze the heat accumulation during elevator operation, reveal the impact of the thermal effects caused by friction on the equipment, avoid component performance degradation or failure due to overheating, and estimate the local wear of the traction sheave by combining the frictional thermal effect and the eccentric load direction data. The local wear of the traction sheave can be effectively identified, and corresponding measures can be taken in advance for maintenance or replacement to ensure the long-term safe and stable operation of the elevator.

[0044] Preferably, step S233 includes the following steps:

[0045] The escalator operation angle is calculated according to the escalator power simulation data, thereby obtaining the escalator operation angle data;

[0046] Perform load force analysis on the escalator operation angle data and elevator operation overload data to obtain the escalator operation load force data;

[0047] The escalator chain plastic deformation accumulation analysis is performed on the escalator operation load force data to obtain the escalator chain plastic deformation accumulation data;

[0048] The stress growth of the elevator running chain is calculated based on the accumulated data of the escalator chain plastic deformation, thereby obtaining the stress growth data of the escalator running chain;

[0049] According to the escalator chain stress growth data and the escalator chain plastic deformation accumulation data, the escalator chain heterogeneity tension test is carried out to obtain the escalator chain heterogeneity tension data;

[0050] Based on the escalator chain heterogeneity tension data and the escalator chain stress growth data, the chain metal fatigue cumulative statistics are performed to obtain the escalator chain metal fatigue cumulative data;

[0051] Based on the accumulated data of escalator chain metal fatigue and the heterogeneous tension data of escalator chain, the escalator chain damage analysis is carried out to obtain the escalator chain damage data;

[0052] According to the escalator chain damage data and the escalator chain heterogeneity tension data, the sprocket meshing mismatch detection is performed to obtain the escalator sprocket meshing mismatch data;

[0053] According to the escalator sprocket meshing mismatch data and escalator chain damage data, the escalator power simulation data analysis is carried out to obtain the escalator transmission system wear data.

[0054] The present invention calculates the escalator running angle through escalator power simulation data, accurately obtains the angle change of the escalator during operation, helps to identify the stress conditions of the escalator under different working conditions, and further provides necessary data for subsequent structural analysis. Combining the escalator running angle data and the elevator running overload data to perform load stress analysis helps to understand the stress distribution of the escalator under overload conditions, and provides a scientific basis for adjusting the load configuration and ensuring the smooth operation of the equipment. Based on the load stress data, the escalator chain plastic deformation accumulation analysis is performed to reveal the plastic deformation trend of the escalator chain due to long-term stress, predict the fatigue state of the chain, and identify safety hazards in advance. By calculating the chain stress growth of the chain plastic deformation accumulation data, the stress change trend of the escalator chain in long-term use is quantified, providing data support for preventing chain breakage and other faults. Chain heterogeneity tension detection further analyzes the tension distribution problem caused by uneven stress of the escalator chain, and timely discovers potential structural defects. Based on the cumulative statistics of metal fatigue, the metal fatigue state of the escalator chain is quantified, providing a quantitative basis for maintenance and reducing sudden failures. Combining chain damage analysis with sprocket meshing mismatch detection can accurately reveal the matching problems between the chain and sprocket in the escalator transmission system, avoid the reduction of transmission efficiency or wear caused by improper meshing. Through these analyses, the overall wear of the escalator transmission system can be accurately evaluated, providing strong support for optimized design and early maintenance, and ensuring the long-term stable operation of the escalator.

[0055] Preferably, step S3 comprises the following steps:

[0056] Step S31: performing an aging trend analysis of the elevator operation transmission system according to the abnormal state data of the elevator transmission system to obtain aging trend data of the elevator operation transmission system;

[0057] Step S32: Elevator component fracture risk assessment is performed based on the elevator operation transmission system aging trend data to obtain elevator component fracture risk data;

[0058] Step S33: performing elevator operation performance degradation detection according to the elevator operation transmission system aging trend data and the elevator component fracture risk data to obtain elevator operation performance degradation data;

[0059] Step S34: Estimating the increase in elevator power demand based on the elevator operation performance degradation data to obtain elevator power demand increase data.

[0060] The present invention can reveal the wear or fatigue of the elevator transmission system over time by analyzing the aging trend of the abnormal state data of the elevator transmission system, thereby providing valuable data support for taking maintenance measures in advance. Based on the aging trend data of the elevator operation transmission system, the risk assessment of component fracture is carried out to effectively predict which key components will have the risk of fracture, help formulate targeted prevention strategies, and avoid shutdowns and safety accidents caused by sudden component fractures. The aging trend data is combined with the component fracture risk data to perform operating performance degradation detection, more comprehensively evaluate the comprehensive performance changes of the elevator during use, discover signs of performance degradation in advance, and provide a scientific basis for the maintenance and maintenance of the elevator. Based on the operating performance degradation data, the increase in the elevator's required power is estimated, and the change in the elevator's demand for the power system during the aging process can be predicted, ensuring timely adjustment when the power is insufficient to avoid unstable operation of the elevator or serious performance degradation. The effective connection of this series of steps helps to achieve accurate maintenance and risk management throughout the life cycle of the elevator, extend the service life of the elevator, and improve its operating safety and stability.

[0061] Preferably, step S4 comprises the following steps:

[0062] Step S41: performing elevator operation efficiency attenuation analysis according to the elevator operation performance decay data and the elevator demand power increase data to obtain elevator operation efficiency attenuation data;

[0063] Step S42: Predicting the dynamic error accumulation effect according to the elevator operation efficiency attenuation data to obtain the elevator dynamic error accumulation effect data;

[0064] Step S43: performing power abnormal fluctuation analysis according to the elevator power error cumulative effect data to obtain elevator operation power abnormal fluctuation data;

[0065] Step S44: performing elevator power simulation model correction on the elevator power simulation model data based on the elevator running power abnormal fluctuation data and the elevator power error cumulative effect data to obtain an elevator power simulation correction model.

[0066] The present invention can accurately evaluate the efficiency loss of the elevator due to performance degradation during long-term operation through the analysis of elevator operation efficiency attenuation, and provide a quantitative basis for subsequent maintenance and scheduling optimization. Estimating the power error accumulation effect based on the elevator operation efficiency attenuation data helps to identify the power error accumulation generated during the use of the elevator, and avoid the equipment from failing to operate normally due to the gradual increase in errors. Next, the power error is analyzed for abnormal fluctuations, and abnormal fluctuations in the elevator system are discovered in time, providing early warning for detecting and eliminating potential faults. By correcting the elevator power simulation model, it can be accurately adjusted according to the abnormal fluctuation data in actual operation, thereby improving the accuracy and reliability of the power model, so that the elevator can be more stable and efficient in future operations. The comprehensive application of these steps not only improves the safety of elevator operation, but also reduces maintenance costs, extends the service life of equipment, and ensures that the elevator always maintains the best operating state under different working environments.

[0067] The present invention also provides a correction system based on an elevator dynamics model, which is used to execute the correction method based on an elevator dynamics model as described above. The correction system based on the elevator dynamics model includes:

[0068] Elevator power simulation model construction module: obtain elevator design data; perform elevator structure analysis based on the elevator design data to obtain elevator structure data; perform elevator power source analysis based on the elevator structure data to obtain elevator power source data; construct an elevator power simulation model based on the elevator power source data and elevator structure data to obtain elevator power simulation model data;

[0069] Transmission system abnormal state prediction module: based on the elevator power simulation model data, the elevator power simulation is performed to obtain the elevator power simulation data; based on the elevator power simulation data, the elevator transmission system abnormal state is predicted to obtain the elevator transmission system abnormal state data;

[0070] Elevator power demand increase estimation module: perform an elevator operation transmission system aging trend analysis based on the elevator transmission system abnormal state data to obtain elevator operation transmission system aging trend data; perform elevator operation performance degradation detection based on the elevator operation transmission system aging trend data to obtain elevator operation performance degradation data; perform an elevator power demand increase estimation based on the elevator operation performance degradation data to obtain elevator power demand increase data;

[0071] Elevator power simulation model correction module: the power error accumulation effect is estimated according to the elevator operation performance degradation data and the elevator demand power increase data to obtain the elevator power error accumulation effect data; the power abnormal fluctuation analysis is performed according to the elevator power error accumulation effect data to obtain the elevator operation power abnormal fluctuation data; the elevator power simulation model data is corrected based on the elevator operation power abnormal fluctuation data and the elevator power error accumulation effect data to obtain the elevator power simulation correction model.

[0072] The present invention is that, through a correction method based on an elevator dynamics model, the monitoring accuracy of the elevator operation state and the intelligence level of the system can be significantly improved. Acquiring elevator design data and combining it with structural analysis can help to fully understand the physical characteristics of the elevator, provide a reliable basis for the construction of subsequent dynamic models, and ensure that the model can accurately reflect the actual structural characteristics of the elevator. By analyzing the power source of the elevator, the various power factors involved in the operation of the elevator are deeply understood, and the adaptability of the model to different power inputs during the simulation process is enhanced, thereby improving the accuracy of the dynamic simulation model. Simulation is carried out based on the dynamic model to simulate the actual operation state of the elevator, which helps to quickly capture the load anomalies or transmission system problems that occur in the elevator under various operating conditions, and provide a reliable basis for the identification and prediction of subsequent abnormal states. On the basis of abnormal state data, by analyzing the aging trend of the transmission system, the wear and performance degradation of key components are timely evaluated, and accurate aging trend data is provided for maintenance personnel to help formulate a scientific and reasonable maintenance plan. The decline detection of operating performance can quantify the degree of decline in the overall performance of the elevator, providing key support for the subsequent prediction of the growth of power demand. Through the estimation of power demand increase, the power shortage problem faced by the elevator can be identified in advance, providing data basis for further optimizing the transmission system and improving operating efficiency. In the estimation of the cumulative effect of dynamic errors, it is possible to identify the source of the error and quantify its long-term impact, which is crucial to ensuring the stability and safety of elevator operation. By analyzing the abnormal fluctuation phenomenon, the irregular dynamic changes that occur during the operation of the elevator can be further refined, providing an accurate reference for timely adjustment of the dynamic model. The dynamic matching of the simulation model and the actual operating state is achieved through model correction, which not only improves the accuracy and reliability of the model, but also provides important guarantees for the intelligent operation and maintenance and fault prevention of the elevator. Therefore, the present invention is an optimization of a traditional correction method based on an elevator dynamic model, which solves the problem of inaccurate analysis of the cumulative effect of elevator dynamic errors and inaccurate analysis of elevator aging trends in a traditional correction method based on an elevator dynamic model, and improves the accuracy of the analysis of the cumulative effect of elevator dynamic errors and the accuracy of the analysis of elevator aging trends. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1A schematic diagram of a correction method based on an elevator dynamics model;

[0074] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0075] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0076] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0077] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0078] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0079] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0080] To achieve this, please refer to Figures 1 to 3 , a correction method based on an elevator dynamics model, comprising the following steps:

[0081] Step S1: acquiring elevator design data; performing elevator structure analysis according to the elevator design data to obtain elevator structure data; performing elevator power source analysis based on the elevator structure data to obtain elevator power source data; constructing an elevator power simulation model according to the elevator power source data and the elevator structure data to obtain elevator power simulation model data;

[0082] Step S2: performing elevator power simulation based on the elevator power simulation model data to obtain elevator power simulation data; performing elevator transmission system abnormal state estimation based on the elevator power simulation data to obtain elevator transmission system abnormal state data;

[0083] Step S3: performing an aging trend analysis of the elevator operation transmission system according to the abnormal state data of the elevator transmission system to obtain the aging trend data of the elevator operation transmission system; performing an elevator operation performance degradation detection according to the aging trend data of the elevator operation transmission system to obtain the elevator operation performance degradation data; performing an elevator demand power increase estimation based on the elevator operation performance degradation data to obtain the elevator demand power increase data;

[0084] Step S4: Estimate the power error accumulation effect based on the elevator operation performance degradation data and the elevator demand power increase data to obtain the elevator power error accumulation effect data; perform power abnormal fluctuation analysis based on the elevator power error accumulation effect data to obtain the elevator operation power abnormal fluctuation data; calibrate the elevator power simulation model data based on the elevator operation power abnormal fluctuation data and the elevator power error accumulation effect data to obtain the elevator power simulation correction model.

[0085] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic flow chart of a correction method based on an elevator dynamics model of the present invention. In this example, the correction method based on an elevator dynamics model includes the following steps:

[0086] Step S1: acquiring elevator design data; performing elevator structure analysis according to the elevator design data to obtain elevator structure data; performing elevator power source analysis based on the elevator structure data to obtain elevator power source data; constructing an elevator power simulation model according to the elevator power source data and the elevator structure data to obtain elevator power simulation model data;

[0087] In the embodiment of the present invention, the elevator design data is obtained, including the type of elevator, car structure, traction machine power, guide rail layout and other information. These design data are usually derived from the design drawings or system engineering files of the elevator manufacturer, including the detailed size, material, configuration and other parameters of each component of the elevator. Next, by performing structural analysis on these design data, the various structural components of the elevator are analyzed using engineering calculation software (such as finite element analysis tools such as ANSYS and ABAQUS) to obtain the structural data of the elevator, including the stress, displacement and other information of each component. Through structural analysis, key data such as the geometric shape, load distribution, and movement direction of the elevator are obtained to ensure that the design data is consistent with the load requirements in actual use. Based on the structural data of the elevator, the power source analysis is performed, mainly including the power, rated load and inertia parameters of the elevator drive motor. Through the comprehensive analysis of these data, the power demand and source of the elevator are clarified. These power source data include the driving mode and energy consumption of the elevator under different operating modes. Finally, according to these power source data and structural data, the elevator power simulation model is constructed. The construction of this model relies on mechanical dynamics analysis software (such as Simulink, MATLAB and other tools) to simulate the interaction and movement process of the various components of the elevator, and consider factors such as friction, gravity, driving force, etc. to obtain the elevator dynamic simulation model data.

[0088] Step S2: performing elevator power simulation based on the elevator power simulation model data to obtain elevator power simulation data; performing elevator transmission system abnormal state estimation based on the elevator power simulation data to obtain elevator transmission system abnormal state data;

[0089] In the embodiment of the present invention, the elevator power simulation is performed based on the elevator power simulation model data constructed in step S1. Dynamic simulation tools such as MATLAB / Simulink, Adams and other software are used to simulate the movement process of the elevator by inputting the actual operating parameters of the elevator (such as motor speed, car load, etc.). During the simulation process, considering the different working conditions of the elevator such as starting, stopping, accelerating, and decelerating, important data such as power demand, traction and speed change in each stage are recorded. Through dynamic simulation, the power simulation data of the elevator under normal working and abnormal working conditions are obtained, and these data will reflect the different load and speed conditions encountered during the operation of the elevator. After the simulation is completed, the overload condition of the elevator is detected based on the simulation data. By analyzing the load change, insufficient driving force, increased friction and other phenomena of the elevator, the overload phenomenon occurring during the operation of the elevator is identified, and the overload data of the elevator operation is obtained. Through further analysis of the overload data, combined with the characteristics of the elevator transmission system (such as the load bearing capacity of the traction machine and the traction rope), the abnormal state of the elevator transmission system under overload is estimated. Abnormal conditions include excessive wear of the traction wheel, overload of the transmission components, energy loss, etc., and the abnormal state data of the elevator transmission system is obtained.

[0090] Step S3: performing an aging trend analysis of the elevator operation transmission system according to the abnormal state data of the elevator transmission system to obtain the aging trend data of the elevator operation transmission system; performing an elevator operation performance degradation detection according to the aging trend data of the elevator operation transmission system to obtain the elevator operation performance degradation data; performing an elevator demand power increase estimation based on the elevator operation performance degradation data to obtain the elevator demand power increase data;

[0091] In an embodiment of the present invention, according to the abnormal state data of the elevator transmission system obtained in step S2, an aging trend analysis of the elevator transmission system is performed. This analysis is based on the estimation of the service life and wear of each component of the elevator transmission system, and the aging progress of each component is evaluated in combination with historical data and actual operating conditions. By using data analysis tools (such as MATLAB, Excel, etc.), regression analysis is performed on the historical operating data to obtain the aging trend data of the elevator transmission system. The aging trend includes factors such as the wear rate of each component, the decline rate of load bearing capacity, and the decline of lubrication effect. Elevator operation performance decline detection is performed according to the aging trend data. By monitoring the working efficiency, operating time, load capacity and other indicators of the elevator, combined with the aging trend data, the operation performance decline of the elevator is evaluated in real time. Using the equipment monitoring system (such as the SCADA system) or sensor data (such as temperature, vibration sensors, etc.), the real-time operation status of each component of the elevator is detected, the early signs of performance decline are identified, and the elevator operation performance decline data is obtained. The increase in elevator demand power is estimated based on the operation performance decline data. By calculating the change in power demand of the elevator under different loads and operating conditions, the additional power required by the elevator as the performance declines is estimated. Use numerical calculation tools (such as the numerical simulation module in MATLAB) to combine the decay data with the dynamic model to obtain the elevator demand power increase data.

[0092] Step S4: Estimate the power error accumulation effect based on the elevator operation performance degradation data and the elevator demand power increase data to obtain the elevator power error accumulation effect data; perform power abnormal fluctuation analysis based on the elevator power error accumulation effect data to obtain the elevator operation power abnormal fluctuation data; calibrate the elevator power simulation model data based on the elevator operation power abnormal fluctuation data and the elevator power error accumulation effect data to obtain the elevator power simulation correction model.

[0093] In an embodiment of the present invention, the power error accumulation effect is estimated according to the elevator operation performance decline data and the elevator demand power increase data obtained in step S3. This process is based on the accumulation of various errors (such as control system errors, transmission system errors, etc.) generated during the operation of the elevator. By using simulation software (such as MATLAB, Simulink), the errors accumulated by the elevator in long-term operation are modeled, and the growth trend of the errors in each stage is calculated. In this way, the power error accumulation effect data generated by the elevator system in long-term use can be estimated. Abnormal power fluctuation analysis is performed based on the power error accumulation effect data. Using data processing tools (such as MATLAB's signal processing toolbox), the power fluctuation of the elevator is analyzed in frequency domain and time domain to identify the pattern of abnormal power fluctuation. Analyze whether these fluctuations exceed the normal operating range and whether they are caused by system aging or component failure. Through in-depth analysis of the fluctuation data, abnormal power fluctuation data of the elevator operation is obtained, and early warning information of system failure is provided. Based on the abnormal power fluctuation data of the elevator operation and the cumulative effect data of the elevator power error, the elevator power simulation model is corrected. By adjusting the parameters in the simulation model (such as friction coefficient, driving force, etc.), the dynamic error in the simulation model is corrected to make it more consistent with the actual operation. During the model correction process, the prediction accuracy of the model is gradually optimized by combining the actual operation data and simulation data. Using simulation correction tools (such as MATLAB's optimization toolbox), parameters are optimized and errors are minimized to obtain the elevator dynamic simulation correction model.

[0094] Preferably, step S1 comprises the following steps:

[0095] Step S11: Obtain elevator design data;

[0096] Step S12: performing elevator structure analysis according to the elevator design data, thereby obtaining elevator structure data;

[0097] Step S13: Analyze the elevator power source according to the elevator structure data and the elevator design data to obtain the elevator power source data;

[0098] Step S14: constructing an elevator power simulation model according to the elevator power source data and the elevator structure data to obtain elevator power simulation model data.

[0099] In an embodiment of the present invention, detailed design data of the elevator is obtained from the elevator manufacturer or designer. These design data should include but are not limited to the basic configuration of the elevator, such as the elevator type (elevator or escalator), car size, maximum load, rated load, running speed, motor power, braking system, traction machine configuration, traction rope configuration, etc. These data are usually provided in the form of design drawings, technical specifications, calculation books, etc., and contain detailed dimensions, materials, performance parameters, etc. of each component of the elevator. In addition, it is also necessary to obtain the installation environment data of the elevator, including the size of the elevator shaft, track layout, door configuration, and expected working conditions of the elevator operation (such as frequency of use, load type, etc.). The elevator design data is used to perform elevator structural analysis. This step requires mechanical calculation of the main structure of the elevator to determine the stress conditions of each component of the elevator under different operating conditions. By inputting the dimensions and material properties of components such as the elevator car, counterweight, guide rail, traction machine, etc., the finite element analysis (FEA) method is used to analyze the stress, strain and displacement of the elevator during load, acceleration and deceleration, and braking. During the structural analysis, special attention should be paid to the key components of the elevator, such as the forces on the traction wheel and traction rope, the docking of the guide rails, and the strength of each connecting component. The analysis results can provide structural data such as the overall stability of the elevator structure, load distribution, and stress state of each component. Based on the elevator structural data obtained in step S12, combined with the elevator design data, the elevator power source analysis is performed. The goal of this step is to determine the power required for the operation of the elevator, including the driving force of the traction machine, the power requirements during acceleration and deceleration, and the contribution of the system (such as the braking system, electric drive device, etc.) to the total power. By analyzing the operating conditions and load conditions of the elevator, the power requirements of the elevator under different working conditions are considered, including starting, stopping, accelerating, and running at a constant speed. The power source analysis process should include the calculation of the power, current, and voltage of the elevator motor, especially considering the power requirements of the elevator under maximum load conditions, and analyzing the output power of the traction machine under different speeds and loads. In addition, the influence of factors such as friction and air resistance on the power source of the elevator should also be considered. This analysis determines the drive requirements of the system by using traditional dynamic calculation methods (such as dynamic balance method, energy equation method, etc.), obtains the elevator power source data, and uses the elevator power source data and elevator structure data to build an elevator power simulation model. This step combines the elevator power source data obtained in step S13 with the elevator structure data in step S12 to establish a dynamics-based simulation model to simulate the dynamic behavior of the elevator under various operating conditions. The construction of the simulation model is based on the classical mechanical dynamics principle, taking into account factors such as the elevator's motion equation, friction, inertia, and elastic force. By simplifying the modeling of the elevator system, its main parts (such as the car, counterweight, traction machine, traction rope, etc.) are regarded as rigid bodies, and mechanical modeling is performed on them.Next, through numerical simulation tools such as MATLAB / Simulink, SimPack, etc., the system is modeled and solved using dynamic equations to simulate the dynamic response of the elevator under various working conditions such as acceleration, deceleration, and uniform speed operation. This simulation model can accurately reflect the overall dynamic behavior of the elevator system and the interaction between various components, providing necessary data support for the subsequent prediction of abnormal states of the transmission system. During the construction process, the mass, stiffness, damping and other characteristics of each component of the elevator should also be considered, and abnormal working conditions should be simulated to ensure the comprehensiveness and accuracy of the model, and obtain the elevator dynamic simulation model data.

[0100] Preferably, step S14 comprises the following steps:

[0101] Step S141: Calculate the elevator structure size according to the elevator structure data to obtain the elevator structure size data;

[0102] Step S142: analyzing the position of the elevator guide rail according to the elevator structure data to obtain the position data of the elevator guide rail;

[0103] Step S143: using the elevator guide rail position data and the elevator structure size data to collect the elevator three-dimensional structure to obtain the elevator three-dimensional structure data;

[0104] Step S144: analyzing the elevator running direction according to the elevator three-dimensional structure data and the elevator guide rail position data to obtain the elevator running direction data;

[0105] Step S145: performing elevator power size detection on the elevator power source data to obtain elevator power size data;

[0106] Step S146: constructing an elevator power simulation model based on the elevator three-dimensional structure data, the elevator running direction data and the elevator power size data to obtain the elevator power simulation model data.

[0107] In the embodiment of the present invention, the detailed calculation of the elevator structure size is performed based on the elevator structure data obtained from the elevator design stage, including the car size, counterweight size, hoistway size, and relevant data of the guide rail and machine room. This process mainly determines the overall size of the structure through the geometric dimensions and configuration of the main components of the elevator, including the length, width, height of the car and the size of the counterweight. The standard engineering calculation method is used to adjust and optimize the requirements of the elevator installation space in combination with the design specifications to ensure that the structural components can run freely in the designed hoistway. In particular, key factors such as the gap between the elevator car and the hoistway, the balance requirements of the counterweight, and the laying position of the guide rail need to be considered to ensure the safety and stability of the elevator during operation. The output data of this step is the specific size data of the elevator structure, including the specific sizes of the car, counterweight, and guide rail, forming a detailed list of elevator structure sizes. According to the elevator structure data obtained from step S141, the position of the elevator guide rail is analyzed and calculated. It is necessary to accurately measure the size and shape of the elevator hoistway and compare it with the elevator design requirements to ensure that there is enough space in the hoistway to install the guide rail. By analyzing the actual size and geometry of the elevator shaft, the optimal installation position of the guide rail is determined. The position of the elevator guide rail not only needs to consider the motion trajectory of the car and the counterweight, but also needs to be reasonably arranged according to the operation mode of the elevator (such as speed, acceleration, etc.) and the changes under different load conditions. Especially in long or complex shafts, it is also necessary to ensure that the installation position of the guide rail meets the requirements of verticality and parallelism to avoid unstable operation caused by installation errors. By using precision measuring tools such as laser rangefinders and total stations, the specific position data of the guide rail is obtained in real time to obtain the position data of the elevator guide rail. Combined with the elevator structure size data and guide rail position data obtained in steps S141 and S142, the three-dimensional structure of the elevator is collected, and the three-dimensional model of the elevator structure is obtained by accurately measuring the dimensions in the elevator shaft and using three-dimensional laser scanning technology or three-dimensional modeling software (such as AutoCAD, Revit, etc.). The complete three-dimensional point cloud data of the elevator shaft is obtained by laser scanning equipment, and further model generation is performed in combination with the guide rail position and structure size data. In the 3D modeling process, it is necessary to consider the position, size and relationship of each component of the elevator (such as the car, counterweight, guide rails, machine room equipment, etc.) to form a complete three-dimensional structural model. In addition, the spatial range of the elevator operation must be considered to ensure that the elevator's motion trajectory matches the shaft structure and guide rail position to avoid any potential interference or conflict. The output of this process is the three-dimensional structural data of the elevator, including the overall geometry of the elevator, the relative position relationship of each component and the range of motion. The three-dimensional structural data of the elevator and the elevator guide rail position data obtained from step S143 are used to analyze the running direction of the elevator. It is necessary to determine the motion trajectory and direction of the elevator in the shaft according to the geometric structure of the elevator shaft, the position of the guide rails and the size of the elevator car.By analyzing the trajectory of the elevator during acceleration, deceleration and uniform speed operation, combined with the operation mode in the elevator design (such as bidirectional operation or unidirectional operation), the direction of the elevator's operation is further determined. In a complex elevator shaft, there are multiple intersections of guide rails and tracks. Special attention should be paid to the direction of the elevator's operation to avoid any direction conflicts during operation. Use 3D modeling software (such as ANSYS, MATLAB, etc.) to simulate the movement process of the elevator and calculate the running direction data of the elevator under different loads and speeds. According to the power source data obtained from the elevator design stage, the elevator power size is detected and analyzed. This step mainly calculates the power requirements of each component of the elevator under different working conditions, determines the maximum power requirements of the elevator during normal operation, starting, acceleration and deceleration, calculates the power requirements of the elevator under different loads according to the rated load and operating speed of the elevator, and combines the motor specifications of the elevator and the characteristics of the traction system to detect whether the power provided by the actual system meets the design standards. Through dynamic load testing, the actual power requirements of the elevator under different operating conditions are measured and compared with the theoretical calculated value. It is also necessary to pay special attention to the influence of factors such as friction, air resistance, and load changes in elevator operation on the elevator power. On-site measurements are performed using power meters, torque sensors and other equipment to obtain elevator power data. Based on the three-dimensional structure data of the elevator obtained in step S143, the elevator running direction data obtained in step S144, and the elevator power data obtained in step S145, the elevator power simulation model is constructed, and the three-dimensional structure data of the elevator is used to establish the dynamic model of the elevator through simulation software (such as Simulink, MATLAB, ADAMS, etc.). During the simulation process, factors such as the acceleration, deceleration, friction, and inertial force of the elevator are considered to simulate the dynamic response of the elevator under different operating conditions. According to the running direction data of the elevator, combined with the motor power and traction system characteristics under different load conditions, the power simulation model of the elevator is constructed to simulate the acceleration, speed and required power of the elevator. Through multiple simulations, the parameters of the model are optimized so that the simulation model can more accurately reflect the dynamic performance of the elevator under actual working conditions.

[0108] Preferably, step S2 comprises the following steps:

[0109] Step S21: performing elevator power simulation based on the elevator power simulation model data to obtain elevator power simulation data;

[0110] Step S22: performing elevator overload detection based on elevator dynamics simulation data to obtain elevator operation overload data;

[0111] Step S23: Based on the elevator power simulation data, the elevator operation overload data is used to predict the abnormal state of the elevator transmission system to obtain the abnormal state data of the elevator transmission system.

[0112] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0113] Step S21: performing elevator power simulation based on the elevator power simulation model data to obtain elevator power simulation data;

[0114] In the embodiment of the present invention, the overall system of the elevator is modeled by using advanced dynamic simulation tools (such as Simulink, MATLAB, etc.). The simulation model needs to take into account the dynamic characteristics of each component during the operation of the elevator, including the power output of the motor, the effect of the braking system, the traction of the traction system, and the interaction between the elevator car and the counterweight. The simulation process includes different operating conditions such as starting, acceleration, deceleration, and braking. By dynamically simulating the acceleration, speed, and torque changes of the elevator under different operating conditions, the elevator dynamic simulation data is obtained. These data include the motion state of the elevator at different time points, the required traction, friction, and related dynamic data.

[0115] Step S22: performing elevator overload detection based on elevator dynamics simulation data to obtain elevator operation overload data;

[0116] In an embodiment of the present invention, based on the elevator power simulation data obtained in step S21, an elevator overload detection is performed, and various loads (including the elevator's own weight, passenger weight, counterweight weight, and external impact loads that occur during elevator operation, etc.) during the elevator power simulation process need to be analyzed in detail. By screening and calculating the mechanical data during the elevator operation in the simulation results, it is identified whether the elevator is overloaded under different loads. For example, it is detected whether the load of the traction motor exceeds the rated power during acceleration, braking or speed change of the elevator, or whether the transmission system of the elevator is subjected to an overload. Use load sensors, torque sensors and other equipment to perform real-time detection of the load data of each component during the simulation process, and compare them with the maximum load-bearing capacity of the elevator design. If it is detected that the load exceeds the rated value, the elevator operation overload data is formed.

[0117] Step S23: Based on the elevator power simulation data, the elevator operation overload data is used to predict the abnormal state of the elevator transmission system to obtain the abnormal state data of the elevator transmission system.

[0118] In the embodiment of the present invention, based on the elevator operation overload data obtained in step S22, the abnormal state of the elevator transmission system is estimated, the elevator operation overload data is analyzed, and whether there is an overload condition during the elevator operation, as well as the time period and frequency of overload occurrence are identified. The main components of the elevator transmission system include motors, traction wheels, wire ropes, guide rails and reducers, etc. These components will be subjected to different degrees of mechanical impact during operation. By combining the overload data with the elevator dynamics simulation data, especially the operating state of the traction system and the motor, the potential damage risk of the elevator transmission system under overload conditions is evaluated. This process includes load analysis and mechanical analysis of each component in the elevator transmission system, and estimating the abnormal state (such as motor overheating, traction wheel wear too fast or wire rope tension is too large). Through detection equipment such as vibration sensors, temperature sensors and stress sensors, the operating state of the elevator transmission system is monitored in real time, and the elevator transmission system is estimated to be abnormal based on the elevator dynamics simulation data and overload data, and the abnormal state data of the elevator transmission system is generated, including abnormal warning information of each component in the system.

[0119] Preferably, step S23 includes the following steps:

[0120] Step S231: Classify the elevator types based on the elevator power simulation data to obtain elevator car power simulation data and escalator power simulation data;

[0121] Step S232: Predicting the local wear of the elevator traction sheave based on the elevator car power simulation data and the elevator operation overload data, and obtaining the local wear data of the elevator traction sheave;

[0122] Step S233: performing escalator power simulation data analysis on the elevator operation overload data according to the escalator power simulation data to obtain escalator transmission system wear data;

[0123] Step S234: based on the wear data of the escalator transmission system and the local wear data of the elevator traction wheel, the abnormal state of the elevator transmission system is estimated to obtain the abnormal state data of the elevator transmission system.

[0124] In the embodiment of the present invention, the elevator type is divided according to the elevator power simulation data. The elevator type is mainly classified according to the elevator usage, structural configuration and operation characteristics. For the traditional elevator system, it is mainly divided into two types: passenger elevator and escalator. By analyzing the elevator power simulation data, it is determined whether the elevator is in the passenger elevator operation state or the escalator operation state. For the passenger elevator, the focus is on the motion state and traction of the elevator car, and the dynamic characteristics during its operation, such as the impact of acceleration, braking and load changes on the elevator power system, are analyzed. For the escalator elevator, the motion characteristics of its elevator steps are analyzed, mainly focusing on the driving power of the steps, the power transmission efficiency of the steps and the drive motor, and the load characteristics of the escalator. Through these analyses, the elevator car power simulation data and the escalator power simulation data are obtained respectively. Based on the elevator car power simulation data, the local wear of the elevator traction sheave is estimated for the elevator operation overload data, and the power simulation data of the elevator car is analyzed to identify the load changes of the elevator under different operating conditions. During the operation of the elevator, the friction between the traction sheave and the wire rope determines the traction force transmission of the elevator, and also causes the wear of the traction sheave. Through simulation, the load condition of the traction sheave is obtained, and the stress condition of the traction sheave during excessive load is analyzed. If the elevator operation overload occurs frequently, high load will appear in the local area of ​​the traction sheave, which will accelerate the wear. The tribological model and material fatigue analysis method are used to predict the wear trend of the traction sheave under a specific load, and then the data of local wear of the traction sheave is calculated. These data include the wear depth, wear area and wear rate of the traction sheave surface. The escalator power simulation data is analyzed based on the elevator operation overload data, and the escalator power simulation data is analyzed, especially in the case of excessive elevator load, the impact of the escalator operation load on the transmission system. The escalator transmission system includes components such as drive motors, chains, gears, etc., and its wear is mainly affected by excessive load and long-term high-load operation. Through simulation, the dynamic response of the escalator during excessive load can be obtained, focusing on the impact of load changes on various components of the escalator transmission system. Based on the simulation results, the wear condition of the escalator transmission system is further estimated through material properties, contact mechanics and vibration analysis models, and the wear data of the escalator transmission system is obtained. These data include the degree of wear, wear rate and fault area of ​​each component of the transmission system (such as drive gears, chains, etc.). Based on the wear data of the escalator transmission system and the local wear data of the elevator traction wheel, the abnormal state of the elevator transmission system is estimated. The wear data of the escalator transmission system and the local wear data of the elevator traction wheel are comprehensively analyzed to evaluate the impact of each wear component on the overall performance of the elevator transmission system. By modeling the wear characteristics of the traction wheel and escalator transmission system, taking into account the distribution characteristics and wear rate of wear, combined with the simulation results, the abnormal state of the elevator transmission system is predicted.For example, when the traction wheel is severely worn locally, it will cause fluctuations in the elevator's traction force or a reduction in transmission efficiency; and wear of the escalator's transmission system will cause unstable escalator steps or excessive load on the drive motor. Based on these data, the abnormal state data of the elevator transmission system is generated through the fault diagnosis and early warning model.

[0125] Preferably, step S232 includes the following steps:

[0126] Performing uneven load distribution detection on the elevator according to the elevator car dynamic simulation data and the elevator operation overload data to obtain uneven load distribution data on the elevator;

[0127] According to the uneven distribution data of elevator running load and the excessive load data of elevator running, the growth trend of elevator traction rope tension is analyzed to obtain the growth trend data of elevator traction rope tension;

[0128] Based on the elevator traction rope tension growth trend data, the traction wheel load direction is estimated to obtain the traction wheel positive load direction data;

[0129] Based on the elevator operation overload data and the elevator traction rope tension growth trend data, the traction wheel positive pressure growth is estimated to obtain the traction wheel positive pressure growth data;

[0130] According to the traction wheel positive pressure growth data, the traction wheel friction force growth analysis is carried out to obtain the elevator traction wheel friction force growth data;

[0131] According to the traction wheel positive eccentric load direction data and the elevator running friction force growth data, the friction heat effect is processed to obtain the elevator running friction heat effect data;

[0132] The local wear of the elevator traction sheave is estimated based on the frictional heat effect data of the elevator operation and the positive eccentric load direction data of the traction sheave, and the local wear data of the elevator traction sheave is obtained.

[0133] In an embodiment of the present invention, based on the dynamic simulation data of the elevator car, the load distribution of the elevator in different operation stages is analyzed. The elevator overload data determines whether the elevator has uneven load by real-time monitoring of the weight change, acceleration or deceleration, and load fluctuation data during the operation of the elevator. In the case of uneven load, some parts of the elevator (such as the front or rear car area) will bear more load, resulting in unstable operation of the elevator. The load data of different areas are collected by using a data acquisition system and a multi-dimensional dynamic load sensor, and the load distribution analysis is performed through a mathematical model. According to the analysis results, the specific data of the uneven load distribution of the elevator operation are calculated, which include the difference value of the load in each area, the load distribution deviation, and the load peak value. Through the comprehensive analysis of the uneven load distribution data of the elevator operation and the excessive load data of the elevator operation, the stress state of the elevator traction rope is evaluated. The traction rope is a key component in the elevator transmission system. The magnitude of its tension directly affects the operation stability and safety of the elevator. According to the uneven load data, the tension change of the traction rope of the elevator when the load is excessive is identified. If the load is uneven during the operation of the elevator, the load on the traction rope will also be unevenly distributed, resulting in excessive tension on some parts of the traction rope. The mechanical model and the elevator dynamics analysis method are used to calculate the change in the tension of the elevator traction rope in combination with the change trend of the elevator operation load. By analyzing the overload data, the growth trend of the traction rope tension over time is predicted, and the elevator traction rope tension growth trend data is obtained, including the change rate of the traction rope tension, the tension growth peak and the tension fluctuation area. According to the elevator traction rope tension growth trend data, the load of the traction wheel in the elevator traction system is further analyzed. During the operation of the elevator, the traction wheel bears the tension of the traction rope. Especially when the elevator is overloaded, the stress state of the traction wheel will change. By analyzing the growth trend of the traction rope tension, the load distribution of the traction wheel under different operating conditions is determined. If the elevator has uneven load, the tension of the traction rope will cause one side of the traction wheel to bear a larger load, resulting in an eccentric load phenomenon. By analyzing the growth trend of the traction rope tension and combining the mechanical characteristics of the traction wheel, the eccentric load direction of the traction wheel is estimated, and its positive eccentric load direction is determined. The data includes the change in the direction of the eccentric load, the eccentric load angle and the force range, etc. The growth of the positive pressure of the traction sheave is calculated based on the elevator operation overload data and the elevator traction rope tension growth trend data. The positive pressure of the traction sheave refers to the contact pressure between the traction sheave and the elevator traction rope. This pressure will increase with the overload of the elevator and the change of the traction rope tension. According to the time and intensity of the elevator overload and the growth trend of the traction rope tension, the pressure fluctuations of the traction sheave during the operation of the elevator are identified. When the load is excessive, the traction sheave needs to withstand greater tension, which will lead to an increase in the positive pressure of the traction sheave. Through mechanical analysis, combined with elevator dynamic simulation data, the changes in the positive pressure of the traction sheave under different operating conditions are evaluated, and its growth trend is predicted.The obtained traction wheel positive pressure growth data includes information such as the rate of change of positive pressure, pressure peak, and pressure fluctuation range. The friction growth analysis is performed based on the traction wheel positive pressure growth data. The friction between the traction wheel and the traction rope is an important factor affecting the elevator transmission efficiency and traction wheel wear. When the positive pressure of the traction wheel increases, the friction between the traction rope and the traction wheel will also increase, resulting in increased wear on the traction wheel surface. Based on the traction wheel positive pressure growth data, the friction on the contact surface between the traction rope and the traction wheel is calculated. Using the tribological model and material fatigue theory, the change of friction under different loads is analyzed, and the growth trend of friction is evaluated. According to different operating states (such as stationary, acceleration, deceleration, etc.), the friction change data between the traction wheel and the traction rope are calculated, including the rate of change of friction, the peak value of friction and the fluctuation amplitude, etc. According to the traction wheel positive load direction data and the elevator operation friction growth data, the friction heat effect is processed. The friction heat effect is the heat generated by the friction between the traction wheel and the traction rope. This heat will cause the temperature of the traction wheel surface to rise, thereby accelerating the wear of the material. During the operation of the elevator, if the traction wheel is in the positive eccentric load direction and the friction force is large, the friction heat effect will be more significant. Through the heat conduction model, combined with the positive eccentric load direction and friction force growth data of the traction wheel, the temperature distribution and its change trend on the traction wheel surface are calculated. Through the temperature-mechanical coupling model, the influence of friction heat on the material properties of the traction wheel surface is further analyzed, and the friction heat effect data generated during the operation of the elevator are obtained. These data include temperature change amplitude, thermal stress distribution and heat conduction rate. According to the friction heat effect data of the elevator operation and the positive eccentric load direction data of the traction wheel, the local wear of the traction wheel is estimated. Based on the friction heat effect data, the surface temperature distribution of the traction wheel and the material fatigue range caused by temperature are determined. Combined with the positive eccentric load direction data of the traction wheel, the wear of the traction wheel surface under different load and temperature conditions is analyzed. Using the wear model and the thermal-mechanical coupling analysis method, the wear rate and wear amount of the traction wheel under different loads are calculated. Especially under high temperature and high load conditions, the wear of the local area of ​​the traction wheel is more serious. Through simulation, the local wear data of the traction wheel are obtained, which include wear depth, wear area, wear rate and local characteristics of wear.

[0134] Preferably, step S233 includes the following steps:

[0135] The escalator operation angle is calculated according to the escalator power simulation data, thereby obtaining the escalator operation angle data;

[0136] Perform load force analysis on the escalator operation angle data and elevator operation overload data to obtain the escalator operation load force data;

[0137] The escalator chain plastic deformation accumulation analysis is performed on the escalator operation load force data to obtain the escalator chain plastic deformation accumulation data;

[0138] The stress growth of the elevator running chain is calculated based on the accumulated data of the escalator chain plastic deformation, thereby obtaining the stress growth data of the escalator running chain;

[0139] According to the escalator chain stress growth data and the escalator chain plastic deformation accumulation data, the escalator chain heterogeneity tension test is carried out to obtain the escalator chain heterogeneity tension data;

[0140] Based on the escalator chain heterogeneity tension data and the escalator chain stress growth data, the chain metal fatigue cumulative statistics are performed to obtain the escalator chain metal fatigue cumulative data;

[0141] Based on the accumulated data of escalator chain metal fatigue and the heterogeneous tension data of escalator chain, the escalator chain damage analysis is carried out to obtain the escalator chain damage data;

[0142] According to the escalator chain damage data and the escalator chain heterogeneity tension data, the sprocket meshing mismatch detection is performed to obtain the escalator sprocket meshing mismatch data;

[0143] According to the escalator sprocket meshing mismatch data and escalator chain damage data, the escalator power simulation data analysis is carried out to obtain the escalator transmission system wear data.

[0144] In an embodiment of the present invention, according to the escalator dynamic simulation data, the angle change information of the escalator in different operation stages is obtained. Using the dynamic simulation model, each operation state of the escalator is analyzed, especially focusing on the operation angle of the escalator under different working conditions such as load, acceleration, and deceleration. By modeling the geometric relationship between the escalator platform and the steps, the operation angle of the escalator is calculated, and the angle change under each operation state is evaluated based on the simulation data. By building a mathematical model, considering factors such as motor drive power, load conditions, and friction, derivation and simulation are performed to obtain the operation angle data of the escalator. Combined with the escalator operation angle data and the elevator operation load overload data, the load stress condition of the escalator is analyzed in detail through mechanical analysis, and the force distribution of each part of the escalator (such as steps, chains, drive devices, etc.) under different loads is determined according to the escalator operation angle. Using the elevator load overload data, combined with the dynamic simulation model of the escalator, the specific conditions of each force point when the escalator is running are derived. Taking into account the problems of uneven local force and unbalanced power transmission caused by excessive load, the stress of each component of the escalator is accurately calculated by mechanical formulas, and a force distribution diagram is drawn. According to the load force data of the escalator operation, the plastic deformation of the escalator chain is analyzed using the finite element analysis method. During long-term use, the escalator chain will undergo plastic deformation, especially under high load or overload conditions. By analyzing the distribution of loads during escalator operation, combined with the material properties, fatigue limit and stress-strain relationship of the chain, the degree of plastic deformation of the chain under different loads is calculated. The load force data is input into the simulation system, considering the dynamic load changes under actual working conditions, and the plastic deformation accumulation process of the chain is gradually simulated through the calculation of the time step. Through these data, the plastic deformation accumulation data of the escalator chain is obtained, including deformation amount, deformation rate and potential fatigue area. The stress growth of the escalator chain is calculated by combining the accumulated data of plastic deformation of the escalator chain with the principle of material mechanics. By analyzing the accumulated plastic deformation of the escalator chain, the stress concentration area caused by plastic deformation of the chain during long-term use is identified. Then, the stress growth trend of the escalator chain is derived and simulated by using the stress-strain model, considering the material properties, load conditions and deformation history of the chain. These data are input into the dynamic simulation system, and the time domain and frequency domain analysis of the chain are performed to calculate the stress growth of the escalator chain under various working conditions. These stress growth data include the maximum stress value, stress concentration area and stress growth rate. According to the stress growth data of the escalator chain and the accumulated plastic deformation data of the escalator chain, the heterogeneous tension of the escalator chain is detected. By analyzing the stress growth data of the escalator chain, it is found that the tension changes at different positions on the chain are uneven, especially in the case of uneven load, different parts of the chain will be affected by different tensions.Using stress analysis tools, combined with the accumulated data of plastic deformation of the chain, the tension of each section of the escalator chain is calculated one by one to identify areas with uneven tension. These data are visualized to form a tension distribution diagram, and the heterogeneity of tension distribution is further analyzed, including differences in tension distribution, maximum tension points, and tension fluctuations. According to the heterogeneous tension data of the escalator chain and the stress growth data of the escalator chain during operation, the metal fatigue of the chain is accumulated and statistically analyzed. Metal fatigue is material fatigue damage caused by repeated loads, especially under high load and uneven load conditions, escalator chains are prone to fatigue. By using fatigue accumulation models such as Miner's law and combining the stress growth data of the escalator chain, the fatigue damage of the chain is gradually statistically analyzed. According to the actual operation data of the escalator chain and the stress-strain conditions under each working condition, the fatigue damage of the chain in each load cycle is calculated. Then, the fatigue damage of each chain is accumulated to obtain the metal fatigue accumulation data of the escalator chain, including the accumulated damage, damage distribution, and fatigue life prediction. According to the accumulated data of metal fatigue of escalator chain and the heterogeneous tension data of escalator chain, the damage analysis of escalator chain is carried out. Through the analysis of the accumulated data of metal fatigue, the fatigue damage area of ​​the chain under different load conditions is identified. Combined with the heterogeneous tension data of escalator chain, the distribution of local damage of the chain under uneven force is analyzed. The damage mechanics model is applied to predict the occurrence location, damage extension trend and damage degree of the chain damage, and the damage degree of the chain under different loads and service time is calculated to obtain the damage data of the escalator chain, including the location, degree and rupture point of the damage. Through the damage data of escalator chain and the heterogeneous tension data of escalator chain, the meshing state of the sprocket is analyzed to check whether there is meshing mismatch. According to the damage of escalator chain, especially the distribution and degree of damage, combined with the heterogeneous tension of escalator chain, the meshing state between sprocket and chain is analyzed. The meshing mismatch between sprocket and chain is usually manifested as excessive or insufficient impact and friction during meshing, which affects the transmission efficiency. By using the transmission system simulation model, the risk of sprocket meshing mismatch is evaluated, and the specific location, degree and impact of the mismatch are identified. The meshing mismatch data obtained include the mismatch position, distribution of meshing force, wear area, etc. The escalator sprocket meshing mismatch data and escalator chain damage data are used to analyze the escalator transmission system through the dynamic simulation model. Based on the sprocket meshing mismatch data, the transmission efficiency between the sprocket and the chain and its impact on the entire escalator transmission system are evaluated. Then, combined with the escalator chain damage data, a comprehensive dynamic analysis of the various components of the escalator transmission system (including chains, sprockets, drive systems, etc.) is performed to calculate the wear of the escalator transmission system. Through simulation, the wear data of the escalator system under different working conditions is obtained, including the degree of wear, wear area, wear rate, etc.

[0145] Preferably, step S3 comprises the following steps:

[0146] Step S31: performing an aging trend analysis of the elevator operation transmission system according to the abnormal state data of the elevator transmission system to obtain aging trend data of the elevator operation transmission system;

[0147] Step S32: performing an elevator component fracture risk assessment based on the elevator operation transmission system aging trend data to obtain elevator component fracture risk data;

[0148] Step S33: performing elevator operation performance degradation detection according to the elevator operation transmission system aging trend data and the elevator component fracture risk data to obtain elevator operation performance degradation data;

[0149] Step S34: Estimating the increase in elevator power demand based on the elevator operation performance degradation data to obtain elevator power demand increase data.

[0150] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0151] Step S31: performing an aging trend analysis of the elevator operation transmission system according to the abnormal state data of the elevator transmission system to obtain aging trend data of the elevator operation transmission system;

[0152] In an embodiment of the present invention, abnormal state data of the elevator transmission system are collected, including the operation of the elevator transmission system under different working conditions, such as gear box temperature, vibration frequency of transmission components, noise level, lubrication status, etc. These data are used to analyze the wear and aging trends of various components in the elevator transmission system by applying dynamic analysis and aging models. The aging trend analysis mainly simulates the operation of various components of the transmission system (such as motors, traction wheels, reducers, etc.) under long-term use, and considers factors such as load changes, operating cycles, and temperature changes. By establishing corresponding mathematical models and simulation models, combined with actual data, the aging of the transmission system is quantitatively analyzed. The aging trend data of each component is obtained, such as the degree of component aging, service life, and predicted performance degradation time.

[0153] Step S32: performing an elevator component fracture risk assessment based on the elevator operation transmission system aging trend data to obtain elevator component fracture risk data;

[0154] In an embodiment of the present invention, based on the aging trend data of the elevator transmission system, the fracture risk of each component in the transmission system is evaluated. Combined with the historical data of the elevator operation, the load conditions and aging speed of the transmission components under different operating conditions are analyzed to determine the material fatigue state of each component. Through the mechanical model and fracture mechanics theory, it is evaluated whether each component has a potential risk of fracture under the current aging trend. The specific approach includes modeling the fatigue damage of the main transmission components of the elevator (such as traction wheels, chains, gears, bearings, etc.), and simulating the stress changes under different loads, temperatures and operating conditions. Using the stress-strain relationship and the failure criterion, combined with the fatigue limit of the material, the fracture risk index of each component is calculated. The obtained elevator component fracture risk data includes the risk level, the probability of fracture, and the fatigue life of each component.

[0155] Step S33: performing elevator operation performance degradation detection according to the elevator operation transmission system aging trend data and the elevator component fracture risk data to obtain elevator operation performance degradation data;

[0156] In the embodiment of the present invention, the aging trend data of the elevator operation transmission system and the risk data of the breakage of elevator components are combined to detect the decline of the elevator operation performance. According to the aging trend of each component, the system performance model and the elevator dynamic response model are used to evaluate the performance decline of the elevator transmission system during long-term use. Through simulation, the impact of aging-induced component wear, uneven tension, and reduced transmission efficiency on the overall performance of the elevator is analyzed. A comprehensive evaluation is performed on the status of each component to detect the operating efficiency and stability of the elevator transmission system, such as the elevator's operating speed, acceleration and deceleration time, noise level, vibration amplitude, etc. The degree of performance decline of the elevator is quantified by comparison with normal operation data. The decline detection results include the percentage of decline in the operating capacity of the elevator system, the decline amplitude of the performance of each component, and the decline trend prediction.

[0157] Step S34: Estimating the increase in elevator power demand based on the elevator operation performance degradation data to obtain elevator power demand increase data.

[0158] In the embodiment of the present invention, the increase in the power demand of the elevator is estimated based on the elevator operating performance degradation data, and the reasons for the decrease in the elevator operating efficiency due to factors such as component wear and load changes are identified by analyzing the elevator operating performance degradation data. The elevator dynamics model and the power transfer model are used to calculate the additional power required for the elevator to maintain the original operating performance in the decay state. The specific steps include: estimating the load of the elevator under different working conditions, and calculating the additional traction force or additional power demand under the existing decay degree. At the same time, by adjusting the parameters of each component of the elevator transmission system, it is evaluated how much additional electrical energy or mechanical energy is needed to keep the elevator running smoothly. Through simulation calculation, the data on the increase in the power demand of the elevator is obtained, which specifically includes the additional power, the increase in traction force, and the load increase demand of the elevator.

[0159] Preferably, step S4 comprises the following steps:

[0160] Step S41: performing elevator operation efficiency attenuation analysis according to the elevator operation performance decay data and the elevator demand power increase data to obtain elevator operation efficiency attenuation data;

[0161] Step S42: Predicting the dynamic error accumulation effect according to the elevator operation efficiency attenuation data to obtain the elevator dynamic error accumulation effect data;

[0162] Step S43: performing power abnormal fluctuation analysis according to the elevator power error cumulative effect data to obtain elevator operation power abnormal fluctuation data;

[0163] Step S44: performing elevator power simulation model correction on the elevator power simulation model data based on the elevator running power abnormal fluctuation data and the elevator power error cumulative effect data to obtain an elevator power simulation correction model.

[0164] In the embodiment of the present invention, the attenuation analysis of the elevator operation efficiency is performed in combination with the operation performance decay data and the demand power increase data of the elevator. According to the historical operation data of the elevator, the change of the elevator operation efficiency in the decay state is analyzed, including the acceleration performance, braking performance and load adaptability of the elevator. The dynamic model and the energy conversion model are used to simulate the energy loss of the elevator in different operation states, and adjustments are made according to the actual measurement data (such as motor power consumption, drive system efficiency, etc.). The specific method is to simulate the efficiency change of the elevator in different decay stages by setting variables such as elevator load, speed and frequent start / stop. By gradually deducing the power demand, traction and speed loss of the elevator, the efficiency decay rate of the elevator is calculated. The obtained elevator operation efficiency decay data includes the percentage of energy efficiency loss of the elevator, the proportion of the decline in operation stability and the specific influencing factors of the decay. According to the elevator operation efficiency decay data, the accumulation effect of the elevator power error is estimated, and the elevator operation efficiency decay data is combined with the elevator power response data to analyze the error accumulation caused by efficiency decay during the elevator operation. During multiple starts, stops or load changes of the elevator, the system error will gradually accumulate over time. Through the dynamic model, the error accumulation of the elevator under different loads, speeds and cycles is simulated. In the specific implementation, based on the historical elevator power data, the error fluctuations caused by the changes in load and working state of the elevator system (such as motor, control system, traction system, etc.) are analyzed to evaluate the rate and impact of error accumulation. The error accumulation effect calculation includes the torque error, speed control error, position error, etc. of the elevator drive system, and the power error accumulation effect of the system is calculated based on these errors. The obtained elevator power error accumulation effect data includes the speed of error accumulation, the maximum error threshold and the influence range of each component. The abnormal power fluctuation analysis is carried out using the elevator power error accumulation effect data. By establishing an elevator power fluctuation analysis model, the power output fluctuation of the elevator under different working conditions is simulated. According to the elevator error accumulation effect data, the power fluctuations that occur in the system during starting, acceleration, braking, load changes, etc. are calculated. The fluctuation analysis mainly focuses on the output fluctuations of the elevator traction motor, the dynamic response of the system and the control accuracy. The specific analysis process is: according to the operating state of the elevator under different loads, the instantaneous fluctuations generated during the operation of the elevator are simulated, and the frequency and amplitude of the fluctuations are identified through spectrum analysis technology (such as Fourier transform). Analyze the power fluctuations of the elevator during starting, braking and frequent operation, identify abnormal fluctuations caused by control system errors, mechanical wear, load changes and other factors, and obtain the abnormal fluctuation data of the elevator operation power through analysis and calculation, including the amplitude, frequency, duration of the fluctuation and its impact on the elevator operation.According to the abnormal fluctuation data of elevator operation power and the cumulative effect data of elevator power error, the elevator power simulation model is corrected, and the actual elevator operation data is compared with the power simulation model to identify the differences between the simulation model and the actual operation, especially the differences in power fluctuation, load adaptability and efficiency change. Using the error correction method of the dynamic model and the control system, the abnormal fluctuation data of the elevator operation power and the cumulative effect data of the error are input into the simulation system to optimize the model. The specific operation includes: by analyzing the frequency and amplitude of the error fluctuation, adjusting the control algorithm, load adaptability and power calculation formula in the simulation model and other parameters to ensure that the simulation results are as close as possible to the actual situation. By correcting the simulation model, the power error in the model is reduced and its prediction accuracy of the actual operation is improved. The corrected elevator power simulation model can more accurately reflect the performance changes of the elevator under different working conditions, providing a more reliable basis for subsequent elevator maintenance and optimization. The obtained elevator power simulation correction model.

[0165] The present invention also provides a correction system based on an elevator dynamics model, which is used to execute the correction method based on an elevator dynamics model as described above. The correction system based on the elevator dynamics model includes:

[0166] Elevator power simulation model construction module: obtain elevator design data; perform elevator structure analysis based on the elevator design data to obtain elevator structure data; perform elevator power source analysis based on the elevator structure data to obtain elevator power source data; construct an elevator power simulation model based on the elevator power source data and elevator structure data to obtain elevator power simulation model data;

[0167] Transmission system abnormal state prediction module: based on the elevator power simulation model data, the elevator power simulation is performed to obtain the elevator power simulation data; based on the elevator power simulation data, the elevator transmission system abnormal state is predicted to obtain the elevator transmission system abnormal state data;

[0168] Elevator power demand increase estimation module: perform an elevator operation transmission system aging trend analysis based on the elevator transmission system abnormal state data to obtain elevator operation transmission system aging trend data; perform elevator operation performance degradation detection based on the elevator operation transmission system aging trend data to obtain elevator operation performance degradation data; perform an elevator power demand increase estimation based on the elevator operation performance degradation data to obtain elevator power demand increase data;

[0169] Elevator power simulation model correction module: the power error accumulation effect is estimated according to the elevator operation performance degradation data and the elevator demand power increase data to obtain the elevator power error accumulation effect data; the power abnormal fluctuation analysis is performed according to the elevator power error accumulation effect data to obtain the elevator operation power abnormal fluctuation data; the elevator power simulation model data is corrected based on the elevator operation power abnormal fluctuation data and the elevator power error accumulation effect data to obtain the elevator power simulation correction model.

[0170] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A correction method based on an elevator dynamics model, characterized in that: The following steps are involved: Step S1: acquiring elevator design data; performing elevator structure analysis according to the elevator design data to obtain elevator structure data; Perform elevator power source analysis based on elevator structure data to obtain elevator power source data; construct an elevator power simulation model based on the elevator power source data and elevator structure data to obtain elevator power simulation model data; Step S2: performing elevator power simulation based on the elevator power simulation model data to obtain elevator power simulation data; performing elevator transmission system abnormal state estimation based on the elevator power simulation data to obtain elevator transmission system abnormal state data; Step S3: performing an aging trend analysis of the elevator operation transmission system according to the abnormal state data of the elevator transmission system to obtain aging trend data of the elevator operation transmission system; Perform elevator operation performance degradation detection based on the elevator operation transmission system aging trend data to obtain elevator operation performance degradation data; Estimating the increase in elevator demand power based on the elevator operation performance degradation data to obtain the elevator demand power increase data; Step S4: according to the elevator operation performance decline data and the elevator demand power increase data, the power error accumulation effect is estimated to obtain the elevator power error accumulation effect data; according to the elevator power error accumulation effect data, the power abnormal fluctuation analysis is performed to obtain the elevator operation power abnormal fluctuation data; The elevator power simulation model data is corrected based on the abnormal fluctuation data of elevator running power and the cumulative effect data of elevator power error to obtain the elevator power simulation correction model.

2. The correction method based on the elevator dynamics model according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtain elevator design data; Step S12: performing elevator structure analysis according to the elevator design data, thereby obtaining elevator structure data; Step S13: Analyze the elevator power source according to the elevator structure data and the elevator design data to obtain the elevator power source data; Step S14: constructing an elevator power simulation model according to the elevator power source data and the elevator structure data to obtain elevator power simulation model data.

3. The correction method based on the elevator dynamics model according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: Calculate the elevator structure size according to the elevator structure data to obtain the elevator structure size data; Step S142: analyzing the position of the elevator guide rail according to the elevator structure data to obtain the position data of the elevator guide rail; Step S143: using the elevator guide rail position data and the elevator structure size data to collect the elevator's three-dimensional structure to obtain the elevator's three-dimensional structure data; Step S144: analyzing the elevator running direction according to the elevator three-dimensional structure data and the elevator guide rail position data to obtain the elevator running direction data; Step S145: performing elevator power size detection on the elevator power source data to obtain elevator power size data; Step S146: constructing an elevator power simulation model based on the elevator three-dimensional structure data, the elevator running direction data and the elevator power size data to obtain the elevator power simulation model data.

4. The correction method based on the elevator dynamics model according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing elevator power simulation based on the elevator power simulation model data to obtain elevator power simulation data; Step S22: performing elevator overload detection based on elevator dynamics simulation data to obtain elevator operation overload data; Step S23: Based on the elevator power simulation data, the elevator operation overload data is used to predict the abnormal state of the elevator transmission system to obtain the abnormal state data of the elevator transmission system.

5. The correction method based on the elevator dynamics model according to claim 4, characterized in that: Step S23 includes the following steps: Step S231: Classify the elevator types based on the elevator power simulation data to obtain elevator car power simulation data and escalator power simulation data; Step S232: Predicting the local wear of the elevator traction sheave based on the elevator car power simulation data and the elevator operation overload data, and obtaining the local wear data of the elevator traction sheave; Step S233: performing escalator power simulation data analysis on the elevator operation overload data according to the escalator power simulation data to obtain escalator transmission system wear data; Step S234: based on the wear data of the escalator transmission system and the local wear data of the elevator traction wheel, the abnormal state of the elevator transmission system is estimated to obtain the abnormal state data of the elevator transmission system.

6. The correction method based on the elevator dynamics model according to claim 5, characterized in that: Step S232 includes the following steps: Performing uneven load distribution detection on the elevator according to the elevator car dynamic simulation data and the elevator operation overload data to obtain uneven load distribution data on the elevator; According to the uneven distribution data of elevator running load and the excessive load data of elevator running, the growth trend of elevator traction rope tension is analyzed to obtain the growth trend data of elevator traction rope tension; Based on the elevator traction rope tension growth trend data, the traction wheel load direction is estimated to obtain the traction wheel positive load direction data; Based on the elevator operation overload data and the elevator traction rope tension growth trend data, the traction wheel positive pressure growth is estimated to obtain the traction wheel positive pressure growth data; According to the traction wheel positive pressure growth data, the traction wheel friction force growth analysis is carried out to obtain the elevator traction wheel friction force growth data; According to the traction wheel positive eccentric load direction data and the elevator running friction force growth data, the friction heat effect is processed to obtain the elevator running friction heat effect data; The local wear of the elevator traction sheave is estimated based on the frictional heat effect data of the elevator operation and the positive eccentric load direction data of the traction sheave, and the local wear data of the elevator traction sheave is obtained.

7. The correction method based on the elevator dynamics model according to claim 5, characterized in that: Step S233 includes the following steps: The escalator operation angle is calculated according to the escalator power simulation data, thereby obtaining the escalator operation angle data; Perform load force analysis on the escalator operation angle data and elevator operation overload data to obtain the escalator operation load force data; The escalator chain plastic deformation accumulation analysis is performed on the escalator operation load force data to obtain the escalator chain plastic deformation accumulation data; The stress growth of the elevator running chain is calculated based on the accumulated data of the escalator chain plastic deformation, thereby obtaining the stress growth data of the escalator running chain; According to the escalator chain stress growth data and the escalator chain plastic deformation accumulation data, the escalator chain heterogeneity tension test is carried out to obtain the escalator chain heterogeneity tension data; Based on the escalator chain heterogeneity tension data and the escalator chain stress growth data, the chain metal fatigue cumulative statistics are performed to obtain the escalator chain metal fatigue cumulative data; Based on the accumulated data of escalator chain metal fatigue and the heterogeneous tension data of escalator chain, the escalator chain damage analysis is carried out to obtain the escalator chain damage data; According to the escalator chain damage data and the escalator chain heterogeneity tension data, the sprocket meshing mismatch detection is performed to obtain the escalator sprocket meshing mismatch data; According to the escalator sprocket meshing mismatch data and escalator chain damage data, the escalator power simulation data analysis is carried out to obtain the escalator transmission system wear data.

8. The correction method based on the elevator dynamics model according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing an aging trend analysis of the elevator operation transmission system according to the abnormal state data of the elevator transmission system to obtain aging trend data of the elevator operation transmission system; Step S32: performing an elevator component fracture risk assessment based on the elevator operation transmission system aging trend data to obtain elevator component fracture risk data; Step S33: performing elevator operation performance degradation detection according to the elevator operation transmission system aging trend data and the elevator component fracture risk data to obtain elevator operation performance degradation data; Step S34: Estimating the increase in elevator power demand based on the elevator operation performance degradation data to obtain elevator power demand increase data.

9. The correction method based on the elevator dynamics model according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing elevator operation efficiency attenuation analysis according to the elevator operation performance decay data and the elevator demand power increase data to obtain elevator operation efficiency attenuation data; Step S42: Predicting the dynamic error accumulation effect according to the elevator operation efficiency attenuation data to obtain the elevator dynamic error accumulation effect data; Step S43: performing power abnormal fluctuation analysis according to the elevator power error cumulative effect data to obtain elevator operation power abnormal fluctuation data; Step S44: performing elevator power simulation model correction on the elevator power simulation model data based on the elevator running power abnormal fluctuation data and the elevator power error cumulative effect data to obtain an elevator power simulation correction model.

10. A correction system based on an elevator dynamics model, characterized in that: Used to execute the correction method based on the elevator dynamics model as claimed in claim 1, the correction system based on the elevator dynamics model comprises: Elevator power simulation model construction module: obtain elevator design data; perform elevator structure analysis based on the elevator design data to obtain elevator structure data; perform elevator power source analysis based on the elevator structure data to obtain elevator power source data; construct an elevator power simulation model based on the elevator power source data and elevator structure data to obtain elevator power simulation model data; Transmission system abnormal state prediction module: based on the elevator power simulation model data, the elevator power simulation is performed to obtain the elevator power simulation data; based on the elevator power simulation data, the elevator transmission system abnormal state is predicted to obtain the elevator transmission system abnormal state data; Elevator power demand increase estimation module: perform an elevator operation transmission system aging trend analysis based on the elevator transmission system abnormal state data to obtain elevator operation transmission system aging trend data; perform elevator operation performance degradation detection based on the elevator operation transmission system aging trend data to obtain elevator operation performance degradation data; perform an elevator power demand increase estimation based on the elevator operation performance degradation data to obtain elevator power demand increase data; Elevator power simulation model correction module: the power error accumulation effect is estimated according to the elevator operation performance degradation data and the elevator demand power increase data to obtain the elevator power error accumulation effect data; the power abnormal fluctuation analysis is performed according to the elevator power error accumulation effect data to obtain the elevator operation power abnormal fluctuation data; the elevator power simulation model data is corrected based on the elevator operation power abnormal fluctuation data and the elevator power error accumulation effect data to obtain the elevator power simulation correction model.

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