Multi-parameter fusion cableway carrying cable extension dynamic monitoring system

Abstract

The invention discloses a multi-parameter fusion cableway carrying cable extension dynamic monitoring system, which relates to the technical field of cableway monitoring, and comprises a data acquisition module, a data transmission and storage module and a data analysis and prediction module, and a laser ranging unit is composed of a laser ranging unit, an environmental parameter unit and a PLC communication interface. According to the multi-parameter fusion cableway carrying cable extension dynamic monitoring system, through cooperative use of the data acquisition module, the data transmission and storage module and the data analysis and prediction module, an intelligent cable cutting decision system integrating laser ranging, PLC data acquisition and multi-parameter fusion analysis is obtained; the method can solve the problem of accurate judgment of when to cut off the rope in the operation and maintenance of the carrying rope, can quantify the abrasion and wire breakage risk of the weaving section in real time, reduces the unplanned shutdown or dark rotation accidents of the cableway caused by out-of-control extension of the carrying rope, improves the application value of cableway equipment, improves the rate of return on investment, and guarantees the sustainability of cableway operation at the same time.

Description

Technical Field

[0001] This invention relates to the field of cableway monitoring technology, specifically to a multi-parameter fusion dynamic monitoring system for cableway cable elongation. Background Technology

[0002] As a vital transportation facility in scenic areas, passenger ropeways rely on their transport cables, which are the core load-bearing components. Over long-term operation, these cables are subject to tension, temperature variations, and fatigue loads, leading to irreversible residual elongation and degradation of elastic properties. Traditional rope cutting operations depend on manual daily recording of tension and rope length data (such as the stroke of the tensioning cylinder), estimating the elongation of the transport cable using empirical formulas, and then deciding when to cut the rope. However, this method has significant drawbacks: 1. Inaccurate data collection: Manual recording suffers from unstable time intervals and is susceptible to subjective interference, making it difficult to accurately record changes in the carrier cable. 2. Limitations of analysis and decision-making: The characteristics of the carrier cable are complex in the later stages, with its elasticity decreasing and stiffness increasing, making it difficult for traditional empirical models to accurately predict the critical cable cutting point. 3. Economic and safety risks: Cutting the rope too early may make it impossible to eliminate hidden dangers (such as concentrated wire breakage) through secondary rope cutting later, while delaying rope cutting may cause rope breakage accidents, resulting in significant economic losses and safety accidents.

[0003] Furthermore, existing technologies lack sufficient dynamic monitoring capabilities for the condition of the carrier cable. For example, the tensioning data is not linked to parameters such as the carrier cable rotation angle and temperature compensation for analysis; and the wear and breakage risks of spliced ​​sections cannot be quantified in real time. Therefore, there is an urgent need for a system that integrates automated monitoring and intelligent decision-making to solve the problem of accurately judging "when to cut the rope" in the operation and maintenance of cableways, thereby improving the safety of cableway operation and the economic benefits of the entire life cycle of the cableway. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-parameter fusion dynamic monitoring system for cableway cable elongation, which solves the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: 1. A multi-parameter fusion dynamic monitoring system for cableway cable elongation, comprising a data acquisition module, a data transmission and storage module, and a data analysis and prediction module; the laser ranging unit consists of a laser ranging unit, an environmental parameter unit, and a PLC communication interface. It also includes the following steps: S1: Data Acquisition S1.1: The displacement data of the tensioning trolley under no-load and full-load conditions are measured in real time using a laser ranging unit. S1.2: The environmental parameter unit includes a temperature sensor and a load sensor. By integrating the temperature sensor and the load sensor, it collects data on the ambient temperature and the number of trailers. S1.3: PLC communication interface: Real-time data exchange between PLC and laser ranging unit, temperature sensor and load sensor is realized through Modbus / TCP protocol, and displacement values ​​under no-load and full-load conditions are recorded daily at regular intervals; S2: Data transmission and storage. Data is uploaded to the computer via PLC and stored in text documents named with timestamps. Standardized storage in CSV format is supported for easy retrieval later. S3: Data Analysis and Prediction Module S3.1: Employs a Python data processing engine: loads historical data using Pandas and calculates the average daily elongation of the wire rope using NumPy; Using Matplotlib, dynamic change curves are generated to show the relationship between the position of the tensioning carriage and time, temperature, etc. S3.2: Using the wire rope cut prediction algorithm, a time-effect model is constructed based on the characteristics of wire rope elongation being rapid in the early stage and slow in the later stage: ; in This is the initial length of the wire rope. It is the maximum structural elongation of the wire rope. It is the decay rate coefficient; S3.3: Data Collection S3.3.1: Dataset Classification Construction: Working condition division: Data sets were established under the conditions of "no-load state" and "no-load wire rope state" respectively; Temperature range division: Each operating condition is divided into three subsets based on ambient temperature: low temperature range (< 0℃), normal temperature range (0℃ ≤ ≤ 20℃), and high temperature range (> 20℃). Total dataset: A total of 6 independent datasets were formed, namely 2 operating conditions × 3 temperature ranges; S3.3.2: Data acquisition and processing flow, acquisition frequency: acquire tension trolley position data and ambient temperature data once per hour; The data sources are raw data and derived data. The raw data is the real-time position of the tensioning trolley (sensor readings). The derived data is the change in wire rope length calculated from the position data (comparison between the initial position and the current position of the tensioning trolley during installation). Storage rules: Categorized storage: The temperature and calculated wire rope length change data are categorized into datasets corresponding to the working conditions and temperature ranges; Deduplication logic: If data with the same operating conditions and temperature range already exists in a dataset for the day, it is skipped from storage to avoid redundancy; S3.4: Model Parameter Calculation: Parameters are calculated using the nonlinear least squares method. import numpy as np from scipy.optimize import curve_fit # Define model function def model(t, delta_Lmax, lambda_): return delta_Lmax * (1 - np.exp(-lambda_ * t)) # Input data (example) t_data = np.array([1, 2, 3, 4, 5]) # Time series y_data = np.array([1.2, 2.1, 2.8, 3.3, 3.7]) # Observations # Initial guesses (estimating initial ΔL_max and λ based on different wire rope properties) p0 = [y_data.max(), 0.1] # [Initial value of ΔL_max, initial value of λ] # Fitting parameters params_opt, params_cov = curve_fit(model, t_data, y_data, p0=p0) delta_Lmax_opt, lambda_opt = params_opt print(f"ΔL_max = {delta_Lmax_opt:.3f}, λ = {lambda_opt:.3f}") S3.5: Based on model prediction, calculate the cutoff line for each operating temperature range dataset. and Find the model function, and then calculate the time required to reach a certain length based on six functions under different working conditions and temperatures. Select the shortest time as the latest time to cut the rope. S4: Decision Output: The prediction results (remaining safe days, recommended rope cutting date) are output through the GUI interface. Abnormal operating conditions (such as sudden changes in the elongation distance of the wire rope) trigger the abnormal data processing mechanism and record the fault log simultaneously.

[0006] Preferably, the raw data in S2 includes date, temperature, displacement value, and number of trailers.

[0007] Preferably, the laser ranging unit in S1.1 includes a high-precision laser rangefinder with an accuracy of ±0.1mm.

[0008] Preferably, in S3.3.1, the two working conditions refer to "no-load state" and "no-load wire rope state", and the three temperature ranges refer to the low temperature range, the normal temperature range, and the high temperature range.

[0009] This invention provides a multi-parameter fusion dynamic monitoring system for cableway cable elongation, which has the following advantages: This multi-parameter fusion cableway cable elongation dynamic monitoring system, through the coordinated use of data acquisition, data transmission and storage, and data analysis and prediction modules, results in an intelligent cable cutting decision system integrating laser ranging, PLC data acquisition, and multi-parameter fusion analysis. This system solves the problem of accurately determining "when to cut the cable" in cableway maintenance, quantifies wear and breakage risks in splicing sections in real time, reduces unplanned cableway shutdowns or accidents caused by uncontrolled cable elongation, improves the application value of cableway equipment, provides a return on investment, and ensures the sustainability of cableway operations. Furthermore, since the cableway is a core component of a cableway, it is valuable, difficult to replace, and expensive. Reasonably controlling the cableway elongation process and scientifically planning the splicing schedule can significantly reduce costs and improve operating profits. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of the data flow in the system of the present invention; Figure 2 This is a schematic diagram showing the position of the laser rangefinder of the present invention. Detailed Implementation

[0011] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0012] Please see Figures 1 to 2 This invention provides a technical solution: a multi-parameter fusion dynamic monitoring system for cableway cable elongation, comprising a data acquisition module, a data transmission and storage module, and a data analysis and prediction module. The laser ranging unit consists of a laser ranging unit, an environmental parameter unit, and a PLC communication interface. It also includes the following steps: S1: Data Acquisition S1.1: The displacement data of the tensioning trolley under no-load and full-load conditions are measured in real time using a laser ranging unit. S1.2: The environmental parameter unit includes a temperature sensor and a load sensor. By integrating the temperature sensor and the load sensor, it collects data on the ambient temperature and the number of trailers. S1.3: PLC communication interface: Real-time data exchange between PLC and laser ranging unit, temperature sensor and load sensor is realized through Modbus / TCP protocol, and displacement values ​​under no-load and full-load conditions are recorded daily at regular intervals; S2: Data transmission and storage. Data is uploaded to the computer via PLC and stored in text documents named with timestamps. Standardized storage in CSV format is supported for easy retrieval later. S3: Data Analysis and Prediction Module S3.1: Employs a Python data processing engine: loads historical data using Pandas and calculates the average daily elongation of the wire rope using NumPy; Using Matplotlib, dynamic change curves are generated to show the relationship between the position of the tensioning carriage and time, temperature, etc. S3.2: Using the wire rope cut prediction algorithm, a time-effect model is constructed based on the characteristics of wire rope elongation being rapid in the early stage and slow in the later stage: ; in This is the initial length of the wire rope. It is the maximum structural elongation of the wire rope. It is the decay rate coefficient; S3.3: Data Collection S3.3.1: Dataset Classification Construction: Working condition division: Data sets were established under the conditions of "no-load state" and "no-load wire rope state" respectively; Temperature range division: Each operating condition is divided into three subsets based on ambient temperature: low temperature range (< 0℃), normal temperature range (0℃ ≤ ≤ 20℃), and high temperature range (> 20℃). Total dataset: A total of 6 independent datasets were formed, namely 2 operating conditions × 3 temperature ranges; S3.3.2: Data acquisition and processing flow, acquisition frequency: acquire tension trolley position data and ambient temperature data once per hour; The data sources are raw data and derived data. The raw data is the real-time position of the tensioning trolley (sensor readings). The derived data is the change in wire rope length calculated from the position data (comparison between the initial position and the current position of the tensioning trolley during installation). Storage rules: Categorized storage: The temperature and calculated wire rope length change data are categorized into datasets corresponding to the working conditions and temperature ranges; Deduplication logic: If data with the same operating conditions and temperature range already exists in a dataset for the day, it is skipped from storage to avoid redundancy; S3.4: Model Parameter Calculation: Parameters are calculated using the nonlinear least squares method. import numpy as np from scipy.optimize import curve_fit # Define model function def model(t, delta_Lmax, lambda_): return delta_Lmax * (1 - np.exp(-lambda_ * t)) # Input data (example) t_data = np.array([1, 2, 3, 4, 5]) # Time series y_data = np.array([1.2, 2.1, 2.8, 3.3, 3.7]) # Observations # Initial guesses (estimating initial ΔL_max and λ based on different wire rope properties) p0 = [y_data.max(), 0.1] # [Initial value of ΔL_max, initial value of λ] # Fitting parameters params_opt, params_cov = curve_fit(model, t_data, y_data, p0=p0) delta_Lmax_opt, lambda_opt = params_opt print(f"ΔL_max = {delta_Lmax_opt:.3f}, λ = {lambda_opt:.3f}") S3.5: Based on model prediction, calculate the cutoff line for each operating temperature range dataset. and Find the model function, and then calculate the time required to reach a certain length based on six functions under different working conditions and temperatures. Select the shortest time as the latest time to cut the rope. S4: Decision Output: The prediction results (remaining safe days, recommended rope cutting date) are output through the GUI interface. Abnormal operating conditions (such as sudden changes in the elongation distance of the wire rope) trigger the abnormal data processing mechanism and record the fault log simultaneously.

[0013] The raw data in S2 includes date, temperature, displacement value, and number of trailers.

[0014] The laser ranging unit in S1.1 includes a high-precision laser rangefinder with an accuracy of ±0.1mm.

[0015] In S3.3.1, the two working conditions refer to "no-load condition" and "no-load wire rope condition", and the three temperature ranges refer to the low temperature range, normal temperature range, and high temperature range.

[0016] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A multi-parameter fusion dynamic monitoring system for cableway cable elongation, comprising a data acquisition module, a data transmission and storage module, and a data analysis and prediction module, characterized in that: The laser ranging unit consists of a laser ranging unit, an environmental parameter unit, and a PLC communication interface. return Includes the following steps: S1: Data Acquisition S1.1: The displacement data of the tensioning trolley under no-load and full-load conditions are measured in real time using a laser ranging unit. S1.2: The environmental parameter unit includes a temperature sensor and a load sensor. By integrating the temperature sensor and the load sensor, it collects data on the ambient temperature and the number of trailers. S1.3: PLC communication interface: Real-time data exchange between PLC and laser ranging unit, temperature sensor and load sensor is realized through Modbus / TCP protocol, and displacement values ​​under no-load and full-load conditions are recorded daily at regular intervals; S2: Data transmission and storage. Data is uploaded to the computer via PLC and stored in text documents named with timestamps. Standardized storage in CSV format is supported for easy retrieval later. S3: Data Analysis and Prediction Module S3.1: Employs a Python data processing engine: loads historical data using Pandas and calculates the average daily elongation of the wire rope using NumPy; Using Matplotlib, dynamic change curves are generated to show the relationship between the position of the tensioning carriage and time, temperature, etc. S3.2: Using the wire rope cut prediction algorithm, a time-effect model is constructed based on the characteristics of wire rope elongation being rapid in the early stage and slow in the later stage: ； in This is the initial length of the wire rope. It is the maximum structural elongation of the wire rope. It is the decay rate coefficient; S3.3: Data Collection S3.3.1: Dataset Classification Construction: Working condition division: Data sets were established under the "no-load state" and "no-load wire rope state" respectively; Temperature range division: Each operating condition is divided into three subsets based on ambient temperature: low temperature range (< 0℃), normal temperature range (0℃ ≤ ≤ 20℃), and high temperature range (> 20℃). Total dataset: A total of 6 independent datasets were formed, namely 2 operating conditions × 3 temperature ranges; S3.3.2: Data acquisition and processing flow, acquisition frequency: acquire tension trolley position data and ambient temperature data once per hour; The data sources are raw data and derived data. The raw data is the real-time position of the tensioning trolley (sensor readings). The derived data is the change in wire rope length calculated from the position data (comparison between the initial position and the current position of the tensioning trolley during installation). Storage rules: Categorized storage: The temperature and calculated wire rope length change data are categorized into datasets corresponding to the working conditions and temperature ranges; Deduplication logic: If data with the same operating conditions and temperature range already exists in a dataset for the day, it is skipped from storage to avoid redundancy; S3.4: Model Parameter Calculation: Parameters are calculated using the nonlinear least squares method. import numpy as np from scipy.optimize import curve_fit # Define model function def model(t, delta_Lmax, lambda_): return delta_Lmax * (1 - np.exp(-lambda_ * t)) # Input data (example) t_data = np.array([1, 2, 3, 4, 5]) # Time series y_data = np.array([1.2, 2.1, 2.8, 3.3, 3.7]) # Observations # Initial guesses (estimating initial ΔL_max and λ based on different wire rope properties) p0 = [y_data.max(), 0.1] # [Initial value of ΔL_max, initial value of λ] # Fitting parameters params_opt, params_cov = curve_fit(model, t_data, y_data, p0=p0) delta_Lmax_opt, lambda_opt = params_opt print(f"ΔL_max = {delta_Lmax_opt:.3f}, λ = {lambda_opt:.3f}") S3.5: Based on model prediction, calculate the cutoff line for each operating temperature range dataset. and Find the model function, and then calculate the time required to reach a certain length based on six functions under different working conditions and temperatures. Select the shortest time as the latest time to cut the rope. S4: Decision Output: The prediction results (remaining safe days, recommended rope cutting date) are output through the GUI interface. Abnormal operating conditions (such as sudden changes in the elongation distance of the wire rope) trigger the abnormal data processing mechanism and record the fault log simultaneously.

2. The multi-parameter fusion dynamic monitoring system for cableway cable elongation according to claim 1, characterized in that: The raw data in S2 includes date, temperature, displacement value, and number of trailers.

3. The multi-parameter fusion dynamic monitoring system for cableway cable elongation according to claim 1, characterized in that: The laser ranging unit in S1.1 includes a high-precision laser rangefinder with an accuracy of ±0.1mm.

4. The multi-parameter fusion dynamic monitoring system for cableway cable elongation according to claim 1, characterized in that: In S3.3.1, the two working conditions refer to "no-load state" and "no-load wire rope state", and the three temperature ranges refer to the low temperature range, normal temperature range, and high temperature range.

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