Method for safe operation and control of a transport installation based on a flexible cableway

Abstract

The present application relates to the technical field of data fusion processing, in particular to a flexible cableway-based transportation facility safe operation management and control method, comprising the following steps: S1. Collecting real-time quantitative strain data of the bearing cable, attitude offset angle of the hanging car, wind speed along the line, humidity, temperature and multi-source data of cable icing thickness; S2. Accurately associating the space-time coupling relationship of cable deformation, icing distribution and the attitude of the hanging car; S3. Completing preliminary risk determination through lightweight determination logic; S4. Only uploading the suspected risk data that has completed preliminary determination to the cloud, and the cloud completes verification on the suspected risk data and outputs the working condition optimization parameters suitable for the operation of the flexible cableway; S5. When the safety threshold is exceeded, the multi-device cooperative control logic is executed and the physical execution component control instruction is issued; S6. Dynamically adjusting the space-time registration threshold and the cooperative control parameters. The present application can provide stable and reliable cooperative link support for the real-time safety management and control of the flexible cableway.

Description

Technical Field

[0001] This invention relates to the field of data fusion processing technology, and in particular to a method for safe operation and management of special transportation facilities in complex mountainous canyon environments, especially a method for safe operation and management of transportation facilities based on flexible cableways. Background Technology

[0002] Due to their strong adaptability to complex terrain, flexible cableways have become core transportation equipment for special material transport in remote mountainous areas and material transfer in canyon engineering projects. Their operational safety directly determines the feasibility of transportation operations and the safety of personnel and materials. Therefore, safe operation management is crucial for the large-scale safe application of flexible cableways. For example, Chinese patent application number CN202510983647.9 discloses a construction method and system for intelligent control of the tension of the load-bearing cable in mountain freight cableways. Its core technical solution can be summarized as follows: the tension data of the load-bearing cable is collected in real time by a tension acquisition module and sent to a central control module via a data transmission module. The central control module compares the collected data with a preset tension threshold and then issues a control command to the hydraulic control execution module to achieve dynamic adjustment of the load-bearing cable tension, thereby ensuring the stability of the cableway's load-bearing operation.

[0003] As can be seen from the above description, it has the following drawbacks when used:

[0004] On the one hand, the above-mentioned scheme relies solely on adjusting the tension of the supporting cables to achieve cableway safety management. Since flexible cableway transportation facilities often need to traverse complex mountainous valleys, factors such as strong turbulent wind-induced vibrations, uneven distribution of freezing rain and ice thickness, and gondola attitude deviations can severely impact the structural stability and operational safety of the cableway. Therefore, this scheme has significant blind spots in its management and makes it difficult to perceive the risks of the cableway under extreme operating conditions.

[0005] On the other hand, the traditional data processing mode of the above-mentioned scheme does not take into account the special communication environment of complex mountainous canyons. Due to the complex mountainous canyon terrain, 5G / microwave communication links are often interrupted frequently, data transmission has random latency and packet loss rate, which can easily lead to delayed early warning and thus fail to trigger emergency control measures in a timely manner.

[0006] Therefore, it is necessary to design a method for the safe operation and management of flexible cableway transportation facilities that can adapt to the extreme working conditions and communication stability of complex mountain valleys. Summary of the Invention

[0007] To solve one of the above-mentioned technical problems, the present invention adopts the following technical solution: a method for safe operation and management of transportation facilities based on flexible cableways, comprising the following steps: S1. Simultaneously collecting real-time quantitative strain data of the carrying cable, attitude offset angle of the gondola, wind speed, humidity, temperature and ice thickness of the cable body at the middle of the carrying cable, the two end suspension points, the top of the support, and the anti-fall frame of the gondola.

[0008] S2. Using real-time quantitative strain data of the cable as the core time series benchmark, the acquired multi-source data is time-series aligned and a spatiotemporal registration threshold is set. The spatial mapping relationship of each acquisition point is constructed in combination with the three-dimensional digital twin model of the flexible cableway, and the spatiotemporal coupling relationship between cableway deformation, icing distribution and gondola attitude is accurately associated.

[0009] S3. Based on the multi-source data after completing the spatiotemporal coupling correlation, three core risk characteristics are extracted: strain vibration of the bearing cable, attitude deviation trend of the gondola trolley, and increase in icing thickness. The initial risk assessment is completed through lightweight judgment logic.

[0010] S4. Only the suspected risk data that has been preliminarily identified is uploaded to the cloud. The cloud verifies the suspected risk data and outputs operating condition optimization parameters adapted to the operation of the flexible cableway.

[0011] S5. Based on the risk assessment results or trend predictions verified in the cloud, when the safety threshold is exceeded, execute the multi-device collaborative control logic and issue control commands to the physical execution components.

[0012] The collaborative control logic includes at least extreme weather risk prediction and personnel emergency evacuation collaborative scenarios. The former is configured to trigger the pre-tightening device of the supporting cable and reduce the cableway speed in advance for extreme weather risks corresponding to the collected ice thickness and wind speed data. The latter is configured to simultaneously trigger audible and visual alarms, send alarm information to the on-site operation terminal and dispatch drones for video confirmation when personnel stay in the danger zone for an extended period of time.

[0013] S6. Based on the risk assessment deviation, dynamically adjust the spatiotemporal registration threshold and collaborative control parameters.

[0014] Based on any of the above technical solutions, the following optimizations are made: In step S1, the acquisition frequency of the real-time quantitative strain data of the supporting cable is 100Hz, the acquisition frequency of the attitude offset angle of the gondola trolley is 50Hz, the acquisition frequency of wind speed, humidity and temperature along the line is 50Hz, and the acquisition frequency of the cable icing thickness is 20Hz; and the acquisition process is achieved by GPS timing to realize the synchronous triggering of all acquisition nodes, with a trigger time difference ≤1ms.

[0015] Based on any of the above technical solutions, a further optimization is made in step S2, where linear interpolation is used to complete the timing alignment of multi-source data, and the timing alignment error satisfies the formula. ;in, To account for the time alignment error between real-time quantitative strain data and other monitoring data, the unit is ms. This serves as the original time-series reference for the real-time quantitative strain data of the bearing cable; The target time series is obtained by interpolating other monitoring data.

[0016] Based on any of the above technical solutions, a further optimization is made: in step S2, the spatial mapping relationship is obtained through a spatial mapping function. The expression for the mapping function is as follows:

[0017] ;

[0018] in, The three-dimensional spatial coordinates of the strain acquisition point for the cable are given in meters. The original three-dimensional spatial coordinates of the ice thickness and the attitude monitoring points of the gondola trolley are shown in meters.

[0019] This is a space mapping function, and its specific form is: In the formula, M is a 3×3 spatial transformation matrix, which includes the cableway support spacing and cable sag correction parameters; b is a three-dimensional translation vector.

[0020] Based on any of the above technical solutions, the following optimization is made: In step S2, after completing the spatiotemporal coupling correlation, the 3σ criterion is used to remove abnormal data caused by freezing rain and strong turbulent winds, and the valid data is retained for the extraction of core risk features in step S3.

[0021] Based on any of the above technical solutions, the following optimization is made: In step S3, the core risk feature extraction is achieved by combining multiple algorithms: the strain vibration features of the bearing cable are extracted by wavelet transform, the attitude offset trend features of the gondola trolley are extracted by HOG algorithm, and the time series features of ice thickness growth are extracted by LSTM network; and the processing delay of the lightweight judgment logic is ≤200ms.

[0022] Based on any of the above technical solutions, a further optimization is made as follows: In step S4, when the cloud performs a regression verification method based on historical operating data, which is a mature technology in the prior art, on the suspected risk data uploaded by the edge terminal, the suspected high-risk data uploaded by the edge terminal is verified. The judgment criteria of the evaluation method must meet the requirements of the industry standard for cableway safety specifications. After the accurate risk judgment is completed, operating condition optimization parameters adapted to the current operating status of the flexible cableway are generated and output. The operating condition optimization parameters include at least the load-bearing cable tension adjustment parameters, cableway operating speed parameters, and alarm threshold parameters.

[0023] Based on any of the above technical solutions, the following optimization is made: In step S4, an adaptive linkage mechanism for communication links along the cableway is added: the signal strength, latency, and packet loss rate of 5G / microwave base stations along the flexible cableway are monitored in real time, and the link anomaly thresholds are set as latency ≥300ms and packet loss rate ≥20%; when the link is abnormal, the local cache and independent computing module at the edge end are activated, and risk prediction is continuously completed based on the preset basic parameters for the safe operation of the flexible cableway; after the link is restored, the local cache data is synchronized to the cloud, and the cloud combines the historical operation data of the flexible cableway to correct the operating condition optimization parameters and feed them back to the edge end.

[0024] Based on any of the above technical solutions, the following optimization is made: In step S5, the triggering condition for the extreme weather risk prediction and collaborative scenario is: the wind speed along the route is predicted to reach 18 m / s within 10 minutes, or the ice thickness will exceed 8 cm within 30 minutes; after triggering, in addition to triggering the pre-tightening device of the supporting cable in advance and reducing the cableway running speed, early warning information is also sent to the control room and drones are dispatched to strengthen the monitoring of the target area.

[0025] Based on any of the above technical solutions, a further optimization is made in step S6: the risk assessment deviation is calculated using the formula... , calculate; where, Risk assessment deviation value, dimensionless; This is a preliminary risk index for the edge end; Risk index for cloud verification; when ,when When necessary, prioritize adjusting the coordinated control parameters.

[0026] Based on any of the above technical solutions, a further optimization is made: In step S6, when dynamically adjusting the collaborative control parameters, a gradient descent algorithm is used for iterative optimization, and the iterative formula is as follows:

[0027] ;in, These are the parameters after iteration; These are the parameters before iteration; The adaptive learning rate has a range of values. ; The partial derivatives of the loss function; This is a correction factor for the working condition under freezing rain and icing conditions. Under strong turbulent wind conditions The iteration stops when the loss function value is less than 0.01 or the number of iterations reaches 100.

[0028] Based on any of the above technical solutions, a further optimization is made: A high-altitude extreme low-temperature adaptation calibration step is added before step S1: The ambient temperature T is synchronously collected at each data acquisition device. When T ≤ -20℃, the heating module built into the data acquisition device is activated to maintain the device's operating temperature at 5±2℃; and this is achieved through the formula...

[0029] Temperature compensation is applied to the collected raw data; among which, For the compensated data; This is the original collected data; The sensor temperature coefficient; T represents the real-time ambient temperature, in °C.

[0030] Based on any of the above technical solutions, the following optimization is made: Step S6 adds a working condition parameter memory protection mechanism: according to typical working conditions such as strong turbulent wind, freezing rain and icing, and extreme low temperature, the optimal spatiotemporal registration threshold and collaborative control parameters under each working condition are classified and stored; by identifying the current operating condition, the historical optimal parameters of the corresponding working condition are retrieved to initialize the control model, and the parameter convergence time is shortened by ≥60%.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] 1. This invention constructs a hierarchical collaborative management and control architecture that includes preliminary judgment at the edge and mature evaluation in the cloud. It accurately defines the screening criteria for suspected risk data at the edge and the application boundaries of cloud evaluation. At the same time, it clearly limits the operating condition optimization parameters to include three core dimensions: cable tension adjustment, cableway operating speed, and alarm threshold. This effectively avoids the redundancy loss and computing power waste caused by full data transmission, and solves the problem of disconnect between cloud output parameters and subsequent collaborative control in traditional solutions. Ultimately, it shortens the edge-cloud collaboration latency, improves the accuracy of risk judgment, and provides a stable and reliable collaborative link for the real-time safety management and control of flexible cableways.

[0033] 2. This invention adds a high-altitude extreme low-temperature adaptation calibration step to the data acquisition pre-process, and adopts a dual-protection design of active heating temperature control and sensor-specific quantization compensation. The heating module can stably maintain the equipment's operating temperature at 5±2℃, reducing the probability of power failure in extreme environments of -20℃. The quantization compensation formula accurately eliminates data drift based on the sensor's temperature coefficient. At the same time, it is combined with extreme weather risk prediction and active defense mechanisms to trigger pre-tightening devices, speed reduction, and drone monitoring in advance. This not only ensures the accuracy of data acquisition and equipment stability in extreme environments, but also significantly improves the safety guarantee rate of the cableway under extreme conditions.

[0034] 3. This invention adds a working condition parameter memory protection mechanism in the parameter optimization stage, classifies and stores the optimal spatiotemporal registration threshold and collaborative control parameters according to typical extreme working conditions such as strong turbulent wind, freezing rain and icing, and extreme low temperature. It achieves accurate identification of working conditions by relying on real-time meteorological data collected by S1. This effectively solves the pain points of traditional solutions, which require iterating parameters from the initial value and have low convergence efficiency when switching working conditions. It shortens the parameter convergence time, improves the accuracy of working condition adaptation, and significantly reduces the computing power consumption at the edge, adapting to the lightweight operation requirements of embedded devices. Attached Figure Description

[0035] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or components are generally identified by similar reference numerals. In the drawings, the elements or components are not necessarily drawn to scale.

[0036] Figure 1 This is a flowchart of the control method of the present invention.

[0037] Figure 2 This is a structural block diagram of the control system of the present invention. Detailed Implementation

[0038] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore merely examples and should not be used to limit the scope of protection of the present invention. The specific structure of the present invention is as follows: Figures 1-2 As shown in the image.

[0039] Example 1: S1. Simultaneously collect real-time quantitative strain data of the carrying cable, the attitude offset angle of the gondola, wind speed, humidity, temperature, and icing thickness along the cable at the middle of the carrying cable, both end suspension points, the top of the support, and the anti-fall frame of the gondola. The flexible cableway in this invention specifically refers to engineering cableways in the prior art (e.g., but not limited to, the freight cableway disclosed in patent application number CN202410762884.8), a flexible cableway transportation system based on a steel cable track. Its power system uses flexible cable drive, where the flexible cable used for carrying is simply referred to as the carrying cable, and the flexible cable used for traction is simply referred to as the traction cable or traction device.

[0040] The middle of the load-bearing cable is the midpoint of the span between two adjacent supports (e.g., when the support span is 500m, the middle point is at 250m). This is the area where the cable deflection is the greatest and the stress is the most concentrated. The two end suspension points are the connection points between the existing gondola suspension device and the load-bearing cable. The top of the support is the contact point between the cable of the flexible cableway and the support structure of the support. The gondola anti-fall frame is the trigger sensing point of the anti-fall device. Those skilled in the art can determine the specific installation position according to the stress node markings on the existing cableway design drawings. All sensors at each point adopt the IP67 protection level, which is suitable for the dusty and humid environment in mountainous areas.

[0041] S2. Using real-time quantitative strain data of the cable as the core time series benchmark, the acquired multi-source data is time-series aligned and a spatiotemporal registration threshold is set. The spatial mapping relationship of each acquisition point is constructed in combination with the three-dimensional digital twin model of the flexible cableway, and the spatiotemporal coupling relationship between cableway deformation, icing distribution and gondola attitude is accurately associated.

[0042] When assessing the risks of lightweighting, three core features are extracted: strain of the load-bearing cable, offset of the gondola trolley, and thickness of icing. If any feature exceeds a preset threshold, it is judged as suspected high-risk data.

[0043] S3. Based on the multi-source data after completing the spatiotemporal coupling correlation, three core risk characteristics are extracted: strain vibration of the bearing cable, attitude deviation trend of the gondola trolley, and increase in icing thickness. The initial risk assessment is completed through lightweight judgment logic.

[0044] Lightweighting judgment logic: For example, if any one of the following conditions is met, the load-bearing cable strain vibration amplitude is greater than 100με, the gondola attitude offset angle is greater than 5°, and the icing thickness growth rate is greater than 0.5cm / h, it is judged as a suspected risk. The above thresholds are set based on the cableway safety specifications.

[0045] S4. Only the suspected risk data that has been preliminarily identified is uploaded to the cloud. The cloud verifies the suspected risk data and outputs operating condition optimization parameters adapted to the operation of the flexible cableway.

[0046] S5. Based on the risk assessment results or trend predictions verified in the cloud, when the safety threshold is exceeded, execute the multi-device collaborative control logic and issue control commands to the physical execution components.

[0047] The collaborative control logic includes at least extreme weather risk prediction and personnel emergency evacuation collaborative scenarios. The former is configured to trigger the pre-tightening device of the supporting cable and reduce the cableway speed in advance for extreme weather risks corresponding to the collected ice thickness and wind speed data. The latter is configured to simultaneously trigger audible and visual alarms, send alarm information to the on-site operation terminal and dispatch drones for video confirmation when personnel stay in the danger zone for an extended period of time.

[0048] S6. Based on the risk assessment deviation, dynamically adjust the spatiotemporal registration threshold and collaborative control parameters.

[0049] This solution addresses the complex technical challenges of scattered layout of flexible cableway equipment in complex mountainous canyons, spatiotemporal misalignment of multi-source data due to weak signal attenuation, making it impossible to establish effective correlations of risk factors; limited communication bandwidth in mountainous areas, resulting in control delays exceeding safety thresholds due to full data transmission; and the isolation between the control of extreme weather and personnel risks, making coordinated response impossible.

[0050] The above scheme first collects multi-source data synchronously at the core risk points of the cableway through S1 to ensure the risk correlation of the data sources; then, through S2, it uses the strain data of the supporting cable as the core time series benchmark to complete the time series alignment and spatial mapping of the multi-source data, eliminate spatiotemporal misalignment, and establish the coupling correlation of cable deformation, icing distribution, and gondola attitude; next, through S3, it extracts the core risk features and uses lightweight logic to complete the initial judgment at the edge end, adapting to the bandwidth bottleneck in mountainous areas; then, through the hierarchical transmission strategy of S4, it only uploads suspected risk data to the cloud, uses cloud computing power to complete the verification, and outputs the working condition optimization parameters; then, through the dual-scenario collaborative control of S5, it triggers pre-tensioning and deceleration actions for extreme weather risks, and triggers alarms and drone confirmation actions for personnel risks; finally, through S6, it dynamically adjusts the spatiotemporal registration threshold and collaborative control parameters based on the judgment deviation between the edge end and the cloud, forming a closed loop of the entire link, adapting to the dynamic changes of extreme working conditions. To achieve real-time, precise, and collaborative safety management of flexible cableways in complex mountainous canyons, dynamically adapt to extreme working conditions, and improve the adaptability of the solution under extreme conditions such as -20℃ low temperature and 18m / s strong wind through parameter closed-loop optimization.

[0051] In addition, the selected locations such as the middle of the load-bearing cable and the suspension points provide the necessary high-risk correlation data sources for S2 to establish the coupling correlation of risk factors. Then, by eliminating spatiotemporal misalignment, the identification of risk characteristics is improved. Considering the special characteristics of the mountainous environment, the lightweight judgment logic of S3 is the basis for the graded transmission of S4. Only by completing the preliminary judgment at the edge end can suspected risk data be accurately screened. If the full amount of data is transmitted directly, it will occupy the limited bandwidth in the mountainous area and cause control delay. This effectively solves the long-standing bottleneck technical problem of excessive control delay in complex mountain canyon cableways.

[0052] Based on any of the above technical solutions, the following optimizations are made: In step S1, the acquisition frequency of the real-time quantitative strain data of the supporting cable is 100Hz, the acquisition frequency of the attitude offset angle of the gondola trolley is 50Hz, the acquisition frequency of wind speed, humidity, and temperature along the line is 50Hz, and the acquisition frequency of the cable icing thickness is 20Hz; and the acquisition process is synchronized across all acquisition nodes via GPS timing, with a trigger time difference ≤1ms. This solution reduces redundant data through frequency adaptation and solves signal problems using a high-gain antenna.

[0053] Based on any of the above technical solutions, a further optimization is made in step S2, where linear interpolation is used to complete the timing alignment of multi-source data, and the timing alignment error satisfies the formula. ;in, To account for the time alignment error between real-time quantitative strain data and other monitoring data, the unit is ms. This serves as the original time-series reference for the real-time quantitative strain data of the bearing cable; The target time series is obtained by interpolating other monitoring data.

[0054] This feature is based on the S1 frequency division acquisition design. If S1 uses a uniform frequency, timing alignment is meaningless. Timing alignment error ≤ 5ms is part of the S2 spatiotemporal registration threshold. If the threshold is exceeded, the registration accuracy is insufficient. The aligned data is the input for S3 feature extraction. If it is not aligned, strain and attitude cannot be correlated.

[0055] Based on any of the above technical solutions, a further optimization is made: in step S2, the spatial mapping relationship is obtained through a spatial mapping function. The expression for the mapping function is as follows:

[0056] ;

[0057] in, The three-dimensional spatial coordinates of the strain acquisition point for the cable are given in meters. The original three-dimensional spatial coordinates of the ice thickness and the attitude monitoring points of the gondola trolley are shown in meters.

[0058] This is a space mapping function, and its specific form is: In the formula, M is a 3×3 spatial transformation matrix, which includes the cableway support spacing and cable sag correction parameters; b is a three-dimensional translation vector.

[0059] Existing technologies use planar coordinate matching, which does not consider the morphology of the catenary of the cable body, resulting in a spatial error of over 1m. This leads to a lack of spatial correlation between strain and icing, resulting in a high misjudgment rate of risk factors. This feature is designed based on the acquisition of core points in S1. If non-core points are used, spatial mapping is meaningless. Spatial mapping is a component of spatiotemporal registration in S2. If the error exceeds 0.1m, the registration accuracy will be insufficient. The mapped data is the input for feature extraction in S3.

[0060] The unique advantage of this feature is that it eliminates spatial misalignment. S1 provides spatially correlated data, while this feature provides spatially consistent data. The two work together to provide high-quality input for S2 registration, avoiding the problem that misalignment cannot be solved by collecting only core points.

[0061] This technical solution uses a spatial mapping function combining a matrix and a translation vector to adapt to the catenary shape of the cable body.

[0062] Based on any of the above technical solutions, the following optimization is made: In step S2, after completing the spatiotemporal coupling correlation, the 3σ criterion is used to remove abnormal data caused by freezing rain and strong turbulent winds, and the valid data is retained for the extraction of core risk features in step S3.

[0063] Characteristics for identifying abnormal data of freezing rain and strong turbulent wind: Abnormal freezing rain is characterized by a sudden change in strain of the bearing cable >500με / ice thickness jump >1cm when humidity >95% and 0℃≤temperature≤5℃; Abnormal strong turbulent wind is characterized by wind speed fluctuation >5m / s / cabin attitude offset angle jump >3°.

[0064] In existing technologies, freezing rain and strong turbulent winds can cause sensor drift / data abrupt changes. Current fixed threshold rejection methods cannot adapt to dynamic changes, leading to a high false positive rate for risk assessment. In this solution, after spatiotemporal coupling is completed in S2, statistical characteristics of the data are calculated using a sliding window. The 3σ criterion is used to accurately identify extreme weather anomalies. After interpolation to fill in gaps, a relatively clean and continuous data source is provided to S3, avoiding interference from anomalous data on the effectiveness of risk features. This solution uses fixed-point arithmetic to control computing power and is compatible with the 3σ criterion.

[0065] Based on any of the above technical solutions, the following optimization is made: In step S3, the core risk feature extraction is achieved by combining multiple algorithms: the strain vibration features of the bearing cable are extracted by wavelet transform, the attitude offset trend features of the gondola trolley are extracted by HOG algorithm, and the time series features of ice thickness growth are extracted by LSTM network; and the processing delay of the lightweight judgment logic is ≤200ms.

[0066] Adaptive algorithms are used for different risk data: wavelet transform is used to extract instantaneous features for non-stationary strain, HOG is used to extract trend features for trend attitude, and lightweight LSTM is used to extract time-series features for time-series icing. After extraction, a logistic regression classifier is used to complete lightweight judgment, balancing accuracy and edge computing power. A balance between accuracy and lightweight is achieved, and an edge-end risk judgment link is established to adapt to the bandwidth bottleneck in mountainous areas.

[0067] Based on any of the above technical solutions, a further optimization is made as follows: In step S4, when the cloud performs a regression verification method based on historical operating data, which is a mature technology in the prior art, on the suspected risk data uploaded by the edge terminal, the suspected high-risk data uploaded by the edge terminal is verified. The judgment criteria of the evaluation method must meet the requirements of the industry standard for cableway safety specifications. After the accurate risk judgment is completed, operating condition optimization parameters adapted to the current operating status of the flexible cableway are generated and output. The operating condition optimization parameters include at least the load-bearing cable tension adjustment parameters, cableway operating speed parameters, and alarm threshold parameters.

[0068] It should be explained that the edge terminal in this invention refers to an existing distributed computing node deployed at or near the monitoring site of the flexible cableway, which has lightweight data processing, local storage and short-range communication capabilities. Its hardware carrier is the edge risk initial judgment unit in the system. Its core function is to complete basic data processing tasks with high real-time requirements at a location close to the data acquisition source, so as to avoid the latency and computing power loss of long-distance transmission of full data and adapt to the network constraints of complex deployment environments such as cableway mountainous areas and high altitudes.

[0069] The edge is responsible for rapid front-end filtering and real-time response, solving the latency problem of long-distance computing in the cloud; the cloud is responsible for back-end optimization, and together they achieve efficient risk management of flexible cableways.

[0070] Typical extreme working condition judgment thresholds: strong turbulent wind (wind speed ≥15m / s and lasting ≥10min), freezing rain and icing (ambient temperature ~℃ and humidity ≥95%), extreme low temperature (ambient temperature ≤-20℃).

[0071] When communication between the edge device and the cloud is interrupted, the local operating condition parameter memory module is automatically activated to retrieve the historical optimal parameters and independently execute basic control actions; after communication is restored, the data is automatically synchronized to the cloud.

[0072] The suspected risk data screened at the edge provides accurate input for cloud assessment (avoiding redundancy of all data). The three types of core parameters output by the cloud directly adapt to the collaborative control requirements of step S5 (tension adjustment corresponds to the triggering of the pre-tensioning device, speed parameters correspond to the cableway deceleration, and alarm thresholds correspond to the control room warning). This solves the problem in the existing technology where the cloud output parameters do not match the subsequent control actions, requiring additional conversion and resulting in increased latency.

[0073] The technical features of this solution do not exist in isolation, but rather form a deep synergy with the initial edge judgment (S3) and subsequent collaborative control (S5): Synergy with S3 edge judgment: Suspected risk data screened at the edge eliminates the need for cloud assessment to process all data, improving assessment efficiency, while the application of existing mature assessment methods reduces cloud computing power consumption; Synergy with S5 collaborative control: The three types of core parameters output by the cloud can directly drive the actions of the pretensioning device, cableway speed regulation system, and alarm system without parameter conversion, shortening the response delay of control actions and reducing the peak stress of the cableway under extreme conditions; Synergy with S6 parameter optimization: Clear parameter types provide a clear iterative dimension for subsequent dynamic optimization (optimization only for tension, speed, and alarm thresholds), improving the convergence efficiency of parameter iteration and avoiding the waste of computing power caused by indiscriminate iteration.

[0074] Based on any of the above technical solutions, the following optimization is made: In step S4, an adaptive linkage mechanism for communication links along the cableway is added: the signal strength, latency, and packet loss rate of 5G / microwave base stations along the flexible cableway are monitored in real time, and the link anomaly thresholds are set as latency ≥300ms and packet loss rate ≥20%; when the link is abnormal, the local cache and independent computing module at the edge end are activated, and risk prediction is continuously completed based on the preset basic parameters for the safe operation of the flexible cableway; after the link is restored, the local cache data is synchronized to the cloud, and the cloud combines the historical operation data of the flexible cableway to correct the operating condition optimization parameters and feed them back to the edge end.

[0075] Example 2: Compared with Example 1, this example also includes the following technical features:

[0076] Based on any of the above technical solutions, the following optimization is made: In step S5, the triggering condition for the extreme weather risk prediction and collaborative scenario is: the wind speed along the route is predicted to reach 18 m / s within 10 minutes, or the ice thickness will exceed 8 cm within 30 minutes; after triggering, in addition to triggering the pre-tightening device of the supporting cable in advance and reducing the cableway running speed, early warning information is also sent to the control room and drones are dispatched to strengthen the monitoring of the target area.

[0077] The existing pretensioning device and deceleration action work together. The pretensioning device increases the stiffness of the cable, while the deceleration action reduces the load on the cable. The linkage of the two can reduce the stress level of the cable under extreme weather conditions. The control room early warning and UAV monitoring work together. The early warning information enables personnel to respond quickly, and the UAV monitoring provides real-time data support for response decisions. The linkage of the two can improve the efficiency of emergency response. The equipment control actions and monitoring actions are optimized in reverse. The real-time data collected by the UAV can dynamically adjust the increase of pretensioning force and the magnitude of deceleration, avoiding the loss of operational efficiency caused by over-control.

[0078] In this solution, the UAV flight path is planned based on the S2 three-dimensional digital twin model, avoiding mountainous terrain obstacles and ensuring that the UAV accurately reaches the target area; the prediction error is corrected by using a time series model combined with the cableway's historical operating data, thereby improving the prediction accuracy and meeting the needs of practical applications.

[0079] Based on any of the above technical solutions, a further optimization is made in step S6: the risk assessment deviation is calculated using the formula... , calculate; where, Risk assessment deviation value, dimensionless; This is a preliminary risk index for the edge end; Risk index for cloud verification; when ,when When necessary, prioritize adjusting the coordinated control parameters.

[0080] By quantifying the degree of deviation through formulas and prioritizing adjustments based on the root causes of deviations, parameter optimization is upgraded from experience-driven to data-driven.

[0081] When E ≥ 0.1, the spatiotemporal registration threshold of S2 is adjusted first, which can directly optimize the data source quality of risk feature extraction in S3, thereby improving the consistency between edge R1 and cloud R2. When E < 0.1, the collaborative control parameters of S5 are adjusted first, which can accurately improve the adaptability of control actions to the current working conditions, thereby improving the safety assurance rate of the cableway under extreme working conditions.

[0082] In addition, the quantification results of the deviation formula can also serve as the basis for optimizing the S4 data transmission strategy. When E is consistently ≥0.1, the edge data retransmission mechanism can be triggered to ensure the quality of the data source in the cloud.

[0083] Based on any of the above technical solutions, a further optimization is made in step S6, where the gradient descent algorithm is used for iterative optimization when dynamically adjusting the collaborative control parameters. The iterative formula is as follows:

[0084] ;in, These are the parameters after iteration; These are the parameters before iteration; The adaptive learning rate has a range of values. ; The partial derivatives of the loss function; This is a correction factor for the working condition under freezing rain and icing conditions. Under strong turbulent wind conditions The iteration stops when the loss function value is less than 0.01 or the number of iterations reaches 100.

[0085] Differentiated correction coefficients were set for two typical extreme working conditions: freezing rain and icing (γ=1.2) and strong turbulent wind (γ=1.1). Since the stiffness of the cable decreases more significantly under the freezing rain and icing condition, the parameter iteration range needs to be increased to adapt quickly. Under the strong turbulent wind condition, the parameter adjustment needs to be more stable to avoid excessive iteration leading to oscillations in control actions.

[0086] The introduction of the operating condition correction coefficient solves the drawback of the traditional gradient descent algorithm's one-size-fits-all iterative approach, enabling parameter optimization to accurately match the current operating environment of the cableway and avoiding control failures caused by mismatch between parameters and operating conditions.

[0087] The adaptive learning rate η is limited to the range of [0.01, 0.1] and can be dynamically adjusted according to the changes in the loss function. When the loss function decreases rapidly, a larger η (such as 0.1) is used to accelerate convergence; when the loss function tends to flatten, a smaller η (such as 0.01) is used to avoid parameter oscillation.

[0088] The gradient descent iterative optimization in this scheme is not isolated, but rather connects forward to the operational data acquisition in S1 and the risk assessment deviation calculation in S6, and supports the coordinated control action execution in S5, forming a complete closed loop: the meteorological data (such as humidity and wind speed) collected in S1 determines the value of the operational correction coefficient γ; the risk assessment deviation E in S6 serves as the core input of the loss function L (L=E). 2 The optimized parameters are directly sent to S5 to adjust control actions such as the trigger threshold of the pretensioning device and the speed reduction of the cableway. This linkage ensures that parameter optimization always revolves around the core objectives of reducing risk assessment bias and improving control effectiveness, avoiding meaningless parameter iterations.

[0089] Based on any of the above technical solutions, a further optimization is made: A high-altitude extreme low-temperature adaptation calibration step is added before step S1: The ambient temperature T is synchronously collected at each data acquisition device. When T ≤ -20℃, the heating module built into the data acquisition device is activated to maintain the device's operating temperature at 5±2℃; and this is achieved through the formula...

[0090] Temperature compensation is applied to the collected raw data; among which, For the compensated data; This is the original collected data; The sensor temperature coefficient; T represents the real-time ambient temperature, in °C.

[0091] For extreme low-temperature environments at altitudes of -20℃ and below, the built-in heating module of the data acquisition device precisely maintains the device's operating temperature at 5±2℃. This temperature range matches the optimal operating temperature (0-10℃) of mainstream industrial-grade data acquisition devices, effectively preventing power outages, malfunctions, or permanent damage to core components (such as sensor chips and data transmission modules) caused by low temperatures. Actual testing showed that without the heating module, the probability of power outage at -20℃ was 30%, and the data transmission interruption rate was 45%. With the heating module activated, the probability of power outage dropped to 0, and the transmission interruption rate was ≤2%, fully ensuring the continuous and stable operation of the data acquisition device under extreme low temperatures.

[0092] Based on any of the above technical solutions, the following optimization is made: Step S6 adds a working condition parameter memory protection mechanism: according to typical working conditions such as strong turbulent wind, freezing rain and icing, and extreme low temperature, the optimal spatiotemporal registration threshold and collaborative control parameters under each working condition are classified and stored; by identifying the current operating condition, the historical optimal parameters of the corresponding working condition are retrieved to initialize the control model, and the parameter convergence time is shortened by ≥60%.

[0093] Based on the three core extreme working conditions of the cableway—strong turbulent wind, freezing rain and icing, and extreme low temperature—the optimal spatiotemporal registration thresholds (such as timing alignment threshold ≤ 4ms and spatial mapping error threshold ≤ 0.08m under strong turbulent wind conditions) and collaborative control parameters (such as pretensioning force increment coefficient k = 6kN / με and speed reduction ratio 0.3 under freezing rain and icing conditions) are stored.

[0094] Collaborative control action trigger thresholds: pre-tightening adjustment is initiated when the tension deviation of the load-bearing cable is ≥±8%; the cableway speed is reduced by 30% when the wind speed is ≥15m / s; and a level one alarm is triggered in the UAV monitoring and control room when the ice thickness is ≥10mm.

[0095] Record the generation time, adaptation conditions, and verification accuracy of each parameter to provide traceable data support for subsequent parameter optimization and fault review, and solve the problems of chaotic parameter storage and lack of traceability in existing technologies.

[0096] By identifying the current operating conditions (based on real-time meteorological data collected by S1: wind speed >15m / s indicates strong turbulent wind, humidity >95% and 0℃≤T≤5℃ indicates freezing rain and icing, T≤-20℃ indicates extreme low temperature), the spatiotemporal registration module of S2 and the collaborative control module of S5 are directly initialized by retrieving the historical optimal parameters corresponding to the operating conditions, thus avoiding the invalid process of re-iterating parameters from the initial values ​​after the operating conditions are switched in traditional technologies.

[0097] Example 3: Compared with Example 2, this example also includes the following technical features:

[0098] This invention also discloses a control system for implementing a method for safe operation control of transportation facilities based on flexible cableways. As the execution carrier of the aforementioned control method, its components are as follows:

[0099] The system includes a data acquisition and calibration unit, an edge risk preliminary assessment unit, a cloud management and control unit, a collaborative control execution unit, and a working condition parameter database.

[0100] Among them, the data acquisition and calibration unit, the edge risk preliminary judgment unit, the cloud management and control unit, and the collaborative control execution unit are all connected to the operating condition parameter database in two directions. Each unit completes full-process data sharing, command transmission and status feedback through the database.

[0101] In any of the above schemes, the preferred option is that the data acquisition and calibration unit is responsible for collecting multi-dimensional operating parameters of key points of the flexible cableway, controlling equipment temperature and compensating data under extreme low temperature conditions, as well as data spatiotemporal registration and abnormal data purification.

[0102] The edge risk preliminary assessment unit uses a lightweight algorithm to initially determine the risk level of the purified data, filters out suspected high-risk data, uploads it to the cloud, and synchronizes the preliminary assessment results to the database. The edge risk preliminary assessment unit uses an ARM Cortex-A72 architecture embedded processor.

[0103] The cloud-based management unit verifies suspected high-risk data according to industry standards and generates operating condition optimization parameters. It also categorizes and stores the optimal parameters for typical operating conditions and completes iterative parameter optimization.

[0104] The collaborative control execution unit receives control commands from the edge or cloud, executes actions such as cable pretensioning, speed adjustment, early warning, and drone monitoring, and provides feedback on the execution status.

[0105] The operating condition parameter database is responsible for storing, classifying, managing, and sharing all data from all units of the system, supporting closed-loop control across the entire process. The operating condition parameter database uses the lightweight SQLite database.

[0106] In addition, the dimensional processing methods of each formula in this invention are all conventional settings for those skilled in the art, and the calculation logic can be clearly understood by those skilled in the art without additional explanation.

[0107] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein;

[0108] These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention;

[0109] For those skilled in the art, any alternative improvements or modifications made to the embodiments of the present invention fall within the protection scope of the present invention.

[0110] Any aspects of this invention not described in detail are well-known to those skilled in the art.

Claims

1. A method for safe operation and management of transportation facilities based on flexible cableways, characterized in that, Includes the following steps: S1. Simultaneously collect real-time quantitative strain data of the carrying cable, attitude deviation angle of the gondola, wind speed, humidity, temperature and ice thickness of the cable body at the middle of the carrying cable, the two end suspension points, the top of the support and the anti-fall frame of the gondola. S2. Using the real-time quantitative strain data of the supporting cable as the core time series benchmark, the acquired multi-source data is time-series aligned, and a spatiotemporal registration threshold is set. Combined with the three-dimensional digital twin model of the flexible cableway, the spatial mapping relationship of each acquisition point is constructed to accurately associate the spatiotemporal coupling relationship between cableway deformation, icing distribution and gondola attitude. S3. Based on the multi-source data after completing the spatiotemporal coupling correlation, three core risk characteristics are extracted: strain vibration of the bearing cable, attitude deviation trend of the gondola trolley, and icing thickness growth. The initial risk assessment is completed through lightweight judgment logic. S4. Only the suspected risk data that has been preliminarily identified is uploaded to the cloud. The cloud verifies the suspected risk data and outputs the operating condition optimization parameters adapted to the operation of the flexible cableway. S5. Based on the risk assessment results or trend prediction verified by the cloud, when the safety threshold is exceeded, execute the multi-device collaborative control logic and issue control commands to the physical execution components; S6. Based on the risk assessment deviation, dynamically adjust the spatiotemporal registration threshold and collaborative control parameters.

2. The control method according to claim 1, characterized in that, In step S1, the acquisition frequency of real-time quantitative strain data of the supporting cable is 100Hz, the acquisition frequency of attitude offset angle of the gondola trolley is 50Hz, the acquisition frequency of wind speed, humidity and temperature along the line is 50Hz, and the acquisition frequency of ice thickness of the cable body is 20Hz; and the acquisition process is synchronized with GPS timing to achieve synchronous triggering of all acquisition nodes, with a trigger time difference ≤1ms.

3. The control method according to claim 1, characterized in that, In step S2, linear interpolation is used to complete the time-series alignment of multi-source data, and the time-series alignment error satisfies the formula. ;in, To account for the time alignment error between real-time quantitative strain data and other monitoring data, the unit is ms. This serves as the original time-series reference for the real-time quantitative strain data of the bearing cable; The target time series is obtained by interpolating other monitoring data.

4. The control method according to claim 1, characterized in that, In step S2, the spatial mapping relationship is established through the spatial mapping function. The expression for the mapping function is as follows: ； in, The three-dimensional spatial coordinates of the strain acquisition point for the cable are given in meters. The original three-dimensional spatial coordinates of the ice thickness and the attitude monitoring points of the gondola trolley are shown in meters. This is a space mapping function, and its specific form is: In the formula, M is a 3×3 spatial transformation matrix, which includes the cableway support spacing and cable sag correction parameters; b is a three-dimensional translation vector.

5. The control method according to claim 1, characterized in that: In step S2, after completing the spatiotemporal coupling correlation, the 3σ criterion is used to remove abnormal data caused by freezing rain and strong turbulent winds, and the valid data is retained for the extraction of core risk features in step S3.

6. The control method according to claim 1, characterized in that: In step S3, the core risk feature extraction is achieved by combining multiple algorithms: wavelet transform is used to extract the strain vibration features of the bearing cable, HOG algorithm is used to extract the attitude offset trend features of the gondola trolley, and LSTM network is used to extract the time series features of ice thickness growth; and the processing delay of the lightweight judgment logic is ≤200ms.

7. The control method according to claim 1, characterized in that: In step S4, the cloud performs a mature regression verification method based on historical operating data on the suspected risk data uploaded from the edge terminal. This method verifies the suspected high-risk data uploaded from the edge terminal. The judgment criteria of the evaluation method must meet the requirements of the cableway safety specifications and industry standards. After the accurate risk judgment is completed, operating condition optimization parameters adapted to the current operating status of the flexible cableway are generated and output. The operating condition optimization parameters include at least the load-bearing cable tension adjustment parameters, cableway operating speed parameters, and alarm threshold parameters.

8. The control method according to claim 1, characterized in that: In step S4, an adaptive linkage mechanism for communication links along the cableway is added: the signal strength, latency and packet loss rate of 5G / microwave base stations along the flexible cableway are monitored in real time, and the link anomaly thresholds are set as latency ≥300ms and packet loss rate ≥20%; when the link is abnormal, the local cache and independent computing module at the edge end are activated, and risk prediction is continuously completed based on the preset basic parameters for the safe operation of the flexible cableway. After the link is restored, the local cached data is synchronized to the cloud. The cloud then combines the historical operating data of the flexible cableway to correct the operating condition optimization parameters and feed them back to the edge.

9. The control method according to claim 1, characterized in that: In step S5, the triggering conditions for the extreme weather risk prediction and collaborative scenario are: the wind speed along the route is predicted to reach 18 m / s within 10 minutes, or the ice thickness will exceed 8 cm within 30 minutes; after triggering, in addition to triggering the pre-tightening device of the supporting cable in advance and reducing the cableway operating speed, early warning information is also sent to the control room and drones are dispatched to strengthen the monitoring of the target area.

10. The control method according to claim 1, characterized in that: In step S6, the risk assessment deviation is determined by the formula. , calculate; where, Risk assessment deviation value, dimensionless; This is a preliminary risk index for the edge end; Risk index for cloud verification; when ,when When necessary, prioritize adjusting the coordinated control parameters.

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