Passenger transport cableway operation monitoring method and device, electronic equipment and storage medium

Abstract

The invention provides a passenger ropeway operation monitoring method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining data collected by an intelligent sensor, and building a ropeway operation database; analyzing historical data to construct an anomaly diagnosis model; inputting the first data into the model to obtain an abnormality diagnosis result; controlling a camera to collect video image data, analyzing a video, and recognizing a compartment posture and passenger behaviors to obtain a video recognition result; analyzing the cableway abnormity diagnosis result and the video identification result to obtain an abnormity analysis result; constructing a three-dimensional digital model of the first passenger ropeway, and mapping the real-time operation data into the model to generate a digital twinborn model; and when an abnormal condition occurs, generating an abnormal processing scheme according to the digital twin model and an abnormal analysis result, triggering a braking system and a safety control device, and adjusting operation parameters. Through the scheme of the invention, the abnormity can be monitored and diagnosed in real time, and the safety and the operation efficiency can be improved through an automatic means.

Description

Technical Field

[0001] The present invention relates to the field of monitoring technology, and in particular to a passenger ropeway operation monitoring method, device, electronic equipment and storage medium. Background Art

[0002] As a high-altitude transportation device, cableway safety monitoring is crucial to operational safety. However, existing cableway safety monitoring methods mainly rely on regular manual inspections and basic sensor monitoring. This monitoring model has many limitations: First, while manual inspections are reliable, they are inefficient, especially in inclement weather. This not only significantly increases the difficulty of inspections but also exposes inspectors to significant safety risks. Second, while traditional sensor monitoring systems can provide basic operating parameters, they are significantly inadequate in detecting subtle anomalies and potential risks in cableway systems. The monitoring accuracy and real-time performance are insufficient to meet the requirements for safe modern cableway operations. Furthermore, existing monitoring methods often struggle to achieve all-weather, all-encompassing dynamic monitoring, and are prone to blind spots. Summary of the Invention

[0003] Based on the above problems, the present invention proposes a passenger ropeway operation monitoring method, device, electronic device and storage medium. By combining intelligent sensors, machine learning, image recognition, digital twin and other technologies, a comprehensive passenger ropeway operation monitoring system is constructed, which can not only monitor and diagnose abnormalities in real time, but also improve safety and operation efficiency through automation.

[0004] In view of this, one aspect of the present invention provides a passenger ropeway operation monitoring method, comprising: Obtain data collected by smart sensors installed on passenger ropeways; Clean, organize and store the data collected by intelligent sensors to establish a cableway operation database; Use machine learning algorithms to analyze and train historical data from the cableway operation database to build an abnormality diagnosis model; inputting first data collected from the first passenger ropeway into the abnormality diagnosis model to determine whether there is an abnormality in the operation of the first passenger ropeway, thereby obtaining a ropeway abnormality diagnosis result of the first passenger ropeway; Controlling cameras installed at preset key positions along the first passenger ropeway to collect video image data; Analyzing the video image data using an image recognition algorithm to identify the carriage posture and passenger behavior to obtain a video recognition result; Performing a comprehensive analysis and judgment based on the cableway abnormality diagnosis result and the video recognition result to obtain an abnormality analysis result; constructing a three-dimensional digital model based on the actual physical structure, three-dimensional data, geographical location, and operating parameters of the first passenger ropeway; Mapping the first cableway operation data of the first passenger cableway collected in real time into a three-dimensional digital model to generate a digital twin model; When it is determined according to the abnormality analysis result that an abnormality occurs in the operation of the first passenger ropeway, a corresponding abnormality handling solution is generated according to the digital twin model and the abnormality analysis result; Automatically triggering the braking system and / or corresponding safety control devices according to the abnormality handling plan to ensure the safe operation of the passenger ropeway, and automatically adjusting the operating parameters of the ropeway to optimize operating efficiency and comfort; Controlling a robot or drone equipped with a detection device to perform a remote inspection of the first passenger ropeway; Transmitting the collected inspection data to a control center to determine whether there is any foreign object on the first passenger ropeway; Generate maintenance plans and task lists when foreign matter is present.

[0005] Optionally, the step of analyzing and training the historical data of the cableway operation database using a machine learning algorithm to construct an abnormality diagnosis model includes: Classifying the historical data according to different ropeway structures and properties and / or different operating environment states to obtain first classified data; The first classification data is divided into empty-load operation data and non-empty-load operation data according to the passenger load status of each carriage in the passenger ropeway to obtain second classification data; For N carriages running simultaneously in the same direction between the starting and ending stations, the first to Nth carriages are selected as the target carriages in turn; Establishing a first abnormality diagnosis model when the target carriage is empty, the other N-1 carriages are empty or not empty, and the target carriage runs from the starting point to the end point, to obtain a first abnormality diagnosis model set; Establishing a second abnormality diagnosis model when the target carriage is not empty, the other N-1 carriages are empty or not empty, and the target carriage runs from the starting point to the end point, to obtain a second abnormality diagnosis model set; The first abnormality diagnosis model set and the second abnormality diagnosis model set are used as abnormality diagnosis models.

[0006] Optionally, the step of using an image recognition algorithm to analyze the video image data, identify the carriage posture and passenger behavior, and obtain a video recognition result includes: Preprocessing the collected video image data to obtain first image data; extracting key image features of the first image data using image processing technology; Combining the key features of the image, applying image recognition algorithms to analyze the posture of the carriage and calculate the tilt angle and swing amplitude; Use deep learning models to analyze passenger behavior and identify dangerous movements or overloading; Integrate the recognition results to generate a video recognition result report, including the tilt angle and swing amplitude of the carriage posture and the safety assessment of passenger behavior; The video recognition results are fed back to the monitoring system in real time. If any abnormal situation is detected, the system can automatically issue an alarm and record relevant data for subsequent analysis and processing.

[0007] Optionally, when it is determined according to the abnormality analysis result that an abnormality occurs in the operation of the first passenger ropeway, the step of generating a corresponding abnormality handling solution according to the digital twin model and the abnormality analysis result includes: Determine whether the first passenger ropeway has any abnormality based on real-time monitoring data and abnormality analysis results; When an abnormal situation occurs, a digital scene of the abnormal situation is constructed based on the abnormal analysis results and the digital twin model; Evaluate detected abnormalities, analyze their possible causes and impacts, use digital twin models to simulate the impact of abnormalities on cableway operations, and assess safety risks; Based on the evaluation results of abnormal situations and combined with the digital twin model, corresponding abnormality handling solutions are generated.

[0008] Optionally, the step of inputting the first data collected from the first passenger ropeway into the abnormality diagnosis model, determining whether there is an abnormality in the operation of the first passenger ropeway, and obtaining a ropeway abnormality diagnosis result of the first passenger ropeway includes: Acquire first structural data, first attribute data, first working environment data, and first passenger status data of each carriage between the starting and ending stations of the first passenger ropeway; selecting a corresponding optional abnormality diagnosis model from the abnormality diagnosis models according to the first structural data, the first attribute data, the first working environment data, and the first passenger status data; Acquire the current environmental status data and current passenger status data of the first passenger ropeway in real time; It is determined whether there is any abnormality in the operation of the first passenger ropeway according to the current environmental state data, the current passenger state data and the optional abnormality diagnosis model, and a ropeway abnormality diagnosis result of the first passenger ropeway is obtained.

[0009] Optionally, the step of determining whether there is an abnormality in the operation of the first passenger ropeway based on the current environmental state data, the current passenger state data, and the optional abnormality diagnosis model, and obtaining a ropeway abnormality diagnosis result of the first passenger ropeway includes: For M carriages running simultaneously in the same direction between the starting and ending stations of the first passenger ropeway, sequentially selecting the first to the Mth carriages as carriages to be diagnosed; selecting a corresponding first current abnormality diagnosis model from the optional abnormality diagnosis models according to the first current passenger state data corresponding to the carriage to be diagnosed in the current passenger state data; determining whether the first passenger ropeway has an abnormality according to the current environmental state data, the first current passenger state data, and the first current abnormality diagnosis model, and obtaining a first abnormality diagnosis result; All first abnormality diagnosis results obtained by performing the aforementioned abnormality diagnosis step on all the M carriages to be diagnosed are integrated and analyzed to obtain a cableway abnormality diagnosis result of the first passenger cableway.

[0010] Optionally, the step of controlling a robot or drone equipped with a detection device to perform a remote inspection of the first passenger ropeway includes: Develop a detailed inspection plan based on the structure and operation characteristics of the first passenger ropeway, including inspection routes, inspection points and inspection items; Determine the inspection schedule and select an inspection environment that meets the preset conditions to ensure inspection effectiveness; Establish wireless communication links with robots or drones to ensure data can be transmitted to the control center in real time; Configure the control system and set the parameters of the inspection tasks; Start the robot or drone to conduct inspections along the inspection route; During the inspection process, high-definition cameras and infrared thermal imagers are used to collect cableway data in real time to obtain inspection data.

[0011] Another aspect of the present invention provides a passenger ropeway operation monitoring device for executing a passenger ropeway operation monitoring method, comprising: a data acquisition module and a control processing module; The data acquisition module is used to acquire data collected by the intelligent sensors installed on the passenger ropeway; The control processing module is used for: Clean, organize and store the data collected by intelligent sensors to establish a cableway operation database; Use machine learning algorithms to analyze and train historical data from the cableway operation database to build an abnormality diagnosis model; inputting first data collected from the first passenger ropeway into the abnormality diagnosis model to determine whether there is an abnormality in the operation of the first passenger ropeway, thereby obtaining a ropeway abnormality diagnosis result of the first passenger ropeway; Controlling cameras installed at preset key positions along the first passenger ropeway to collect video image data; Analyzing the video image data using an image recognition algorithm to identify the carriage posture and passenger behavior to obtain a video recognition result; Performing a comprehensive analysis and judgment based on the cableway abnormality diagnosis result and the video recognition result to obtain an abnormality analysis result; constructing a three-dimensional digital model based on the actual physical structure, three-dimensional data, geographical location, and operating parameters of the first passenger ropeway; Mapping the first cableway operation data of the first passenger cableway collected in real time into a three-dimensional digital model to generate a digital twin model; When it is determined according to the abnormality analysis result that an abnormality occurs in the operation of the first passenger ropeway, a corresponding abnormality handling solution is generated according to the digital twin model and the abnormality analysis result; Automatically triggering the braking system and / or corresponding safety control devices according to the abnormality handling plan to ensure the safe operation of the passenger ropeway, and automatically adjusting the operating parameters of the ropeway to optimize operating efficiency and comfort; Controlling a robot or drone equipped with a detection device to perform a remote inspection of the first passenger ropeway; Transmitting the collected inspection data to a control center to determine whether there is any foreign object on the first passenger ropeway; Generate maintenance plans and task lists when foreign matter is present.

[0012] Another aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a passenger ropeway operation monitoring method when executing the computer program.

[0013] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a passenger ropeway operation monitoring method.

[0014] According to the technical solution of the present invention, a passenger ropeway operation monitoring method includes: obtaining data collected by intelligent sensors installed on a passenger ropeway; cleaning, organizing and storing the data collected by the intelligent sensors to establish a ropeway operation database; using a machine learning algorithm to analyze and train historical data in the ropeway operation database to construct an abnormality diagnosis model; inputting first data collected from a first passenger ropeway into the abnormality diagnosis model to determine whether there is an abnormality in the operation of the first passenger ropeway, thereby obtaining a ropeway abnormality diagnosis result for the first passenger ropeway; controlling cameras installed at preset key positions along the first passenger ropeway to collect video image data; using an image recognition algorithm to analyze the video image data to identify the carriage posture and passenger behavior to obtain a video recognition result; and performing a comprehensive analysis and judgment based on the ropeway abnormality diagnosis result and the video recognition result to obtain an abnormality analysis result. A three-dimensional digital model is constructed based on the actual physical structure, three-dimensional data, geographic location, and operating parameters of the first passenger ropeway. Real-time first ropeway operating data of the first passenger ropeway is mapped onto the three-dimensional digital model to generate a digital twin model. When an abnormality is detected in the operation of the first passenger ropeway based on the abnormality analysis results, a corresponding abnormality handling plan is generated based on the digital twin model and the abnormality analysis results. The braking system and / or corresponding safety control devices are automatically triggered according to the abnormality handling plan to ensure the safe operation of the passenger ropeway and automatically adjust the ropeway operating parameters to optimize operating efficiency and comfort. A robot or drone equipped with detection equipment is controlled to conduct remote inspections of the first passenger ropeway. The collected inspection data is transmitted to a control center to determine whether foreign objects are present in the first passenger ropeway. If foreign objects are present, a maintenance plan and task list are generated. By combining intelligent sensors, machine learning, image recognition, digital twins, and other technologies, a comprehensive passenger ropeway operation monitoring system has been constructed. This system not only monitors and diagnoses abnormalities in real time but also improves safety and operational efficiency through automation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of a passenger ropeway operation monitoring method provided by one embodiment of the present invention; Figure 2 It is a schematic block diagram of a passenger ropeway operation monitoring device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0018] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0019] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0020] Refer to the following Figures 1 to 2 A passenger ropeway operation monitoring method, device, electronic device, and storage medium are described according to some embodiments of the present invention.

[0021] like Figure 1 As shown, one embodiment of the present invention provides a passenger ropeway operation monitoring method, comprising: Obtain data collected by smart sensors installed on passenger ropeways; In an embodiment of the present invention, tension intelligent sensors are installed on the load-bearing ropes and traction ropes of the passenger ropeway. The tension intelligent sensors are based on the strain gauge principle or the fiber grating principle, and are used to collect rope tension data in real time and transmit the data to a data processing unit; speed intelligent sensors are installed on the drive motor shaft, drive wheel or detour wheel. The speed intelligent sensors are photoelectric or magnetoelectric, and are used to collect ropeway running speed data and transmit the data to the data processing unit; position intelligent sensors are installed on the ropeway cars and key positions. The position intelligent sensors are rotary encoders or laser rangefinders, and are used to determine the car position information and transmit it to the data processing unit; meteorological intelligent sensors are installed along the ropeway, including ultrasonic anemometers and platinum resistance temperature sensors, and are used to collect wind speed, wind direction and temperature data and transmit them to the data processing unit.

[0022] Clean, organize and store the data collected by intelligent sensors to establish a cableway operation database; Use machine learning algorithms (such as support vector machines and artificial neural networks) to analyze and train historical data from the cableway operation database to build an anomaly diagnosis model (including: tension anomaly diagnosis sub-model, speed anomaly diagnosis sub-model, position anomaly diagnosis sub-model, and meteorological impact assessment sub-model, etc.); inputting first data collected from the first passenger ropeway into the abnormality diagnosis model to determine whether there is an abnormality in the operation of the first passenger ropeway, thereby obtaining a ropeway abnormality diagnosis result of the first passenger ropeway; Controlling cameras installed at preset key locations along the first passenger ropeway (such as entry and exit stations, near towers, at bends, etc.) to collect video image data; In an embodiment of the present invention, key locations along the first passenger ropeway can be preset in advance, including entry and exit stations, near towers, and bends, for on-site inspection and analysis to determine the optimal camera installation location. Appropriate camera types (such as high-definition cameras, night vision cameras, etc.) are selected based on monitoring requirements to ensure that they can meet video acquisition requirements in different environments. The cameras are physically installed at the preset key locations to ensure that the camera's viewing angle covers the required monitoring area and is securely fixed to withstand external environmental influences such as wind and rain. A stable power supply is provided to the cameras, and they can be connected to the monitoring system via a wired or wireless network for real-time video data transmission. The camera parameters, including resolution, frame rate, image quality, etc., are configured through the monitoring system to optimize the video acquisition effect. After the camera is installed, it is tested and debugged to ensure that the camera can operate normally and accurately capture video image data. The camera is controlled to capture video image data, and the captured video image data is stored locally or in a cloud server, and is monitored and processed in real time using video analysis software. The cameras are regularly maintained and inspected to ensure their normal operation and to promptly address possible faults. By installing cameras at key locations, the first passenger ropeway can be monitored in real time, allowing potential safety hazards to be discovered and addressed promptly. The video image data collected by the cameras can serve as an important basis for accident investigation and analysis, improving safety management. The operating status of the passenger ropeway can be effectively monitored, enhancing passengers' sense of security and reducing the risk of accidents. Through video data analysis, the operation and management of the passenger ropeway can be optimized, improving service quality and efficiency.

[0023] Analyze the video image data using an image recognition algorithm to identify the carriage posture (such as tilt angle and swing amplitude) and passenger behavior (such as whether there are dangerous movements or whether there is overloading), and obtain a video recognition result; Performing a comprehensive analysis and judgment based on the cableway abnormality diagnosis result and the video recognition result to obtain an abnormality analysis result; In this step, the cableway abnormality diagnosis results (including information such as the carriage posture and operating status) and the video recognition results (including data such as passenger behavior, carriage tilt angle, swing amplitude, etc.) are formatted and cleaned to ensure data consistency and comparability; based on historical abnormality diagnosis results and historical video recognition results, a comprehensive analysis model is established (the model can be a rule-based system or a machine learning model that can process multi-dimensional data and make comprehensive judgments); the abnormality diagnosis results and video recognition results are input into the comprehensive analysis model for comprehensive analysis (the model will identify potential abnormal patterns, for example, the relationship between carriage tilt and passenger overload); based on the results of the comprehensive analysis, an abnormality analysis report is generated to indicate whether there are safety hazards and provide corresponding treatment suggestions; the abnormality analysis results are fed back to the monitoring system and operation management platform. By comprehensively analyzing the results of cableway abnormality diagnosis and video recognition, potential safety hazards can be more comprehensively identified, and timely measures can be taken to reduce accident risks. This provides data-driven decision support for operational management, helping managers develop more effective safety management strategies. By identifying abnormal situations, human and material resources can be rationally allocated to improve operational efficiency. Through effective safety monitoring and management, passengers' trust in cableway operations can be enhanced, improving the riding experience.

[0024] constructing a three-dimensional digital model based on the actual physical structure, three-dimensional data, geographical location, and operating parameters of the first passenger ropeway; In this step, the actual physical structure data of the first passenger ropeway is collected, including information such as the length of the ropeway, tower height, number of carriages and their dimensions; three-dimensional data is acquired, and three-dimensional point cloud data of the ropeway and its surrounding environment is generated using technologies such as laser scanning and photogrammetry; geographic location data is collected, including the coordinate information of the ropeway, terrain features and the location of surrounding buildings; operating parameter data such as the operating speed, tension, and load of the ropeway are collected; the collected three-dimensional point cloud data is processed, including denoising, filtering and reconstruction, to improve the accuracy and usability of the data; the physical structure data, geographic location data and operating parameter data are integrated to ensure the correlation between various types of data; and three-dimensional modeling software (such as Au toCAD, Revit, SketchUp, etc.) to construct a 3D digital model based on the processed data; accurately reflect the physical structure and geographical location of the ropeway in the model to ensure the realism and accuracy of the model; optimize the constructed 3D model and adjust the details and textures to improve the visual effect and visualization; conduct performance testing of the model to ensure its smooth operation on different devices; compare the constructed 3D digital model with the actual ropeway to verify the accuracy and reliability of the model; make necessary adjustments and corrections based on feedback to ensure that the model can truly reflect the actual situation of the First Passenger Ropeway; apply the constructed 3D digital model to the monitoring, management and decision support system to provide a visual operation interface. The 3D digital model can intuitively display the structure and operating status of the First Passenger Ropeway, enhance understanding and communication; provide data support for operation management, and help managers make more scientific decisions; through model analysis, potential maintenance needs can be identified in advance, maintenance plans can be optimized, and equipment reliability can be improved; through analysis of the 3D model, safety risks can be better assessed and corresponding safety management measures can be formulated.

[0025] Mapping the first cableway operation data (such as tension, speed, position, weather information, etc.) of the first passenger cableway collected in real time into a three-dimensional digital model to generate a digital twin model; In this step, sensors and monitoring equipment are used to collect real-time operational data from the first passenger ropeway, including tension, speed, position, and weather information. This data should be high-frequency and high-precision to ensure real-time and accuracy. The collected real-time operational data is preprocessed, including data cleaning, noise removal, and format conversion, to ensure data integrity and consistency. The preprocessed real-time operational data is mapped to the 3D digital model. Based on the characteristics of each component in the model (such as the carriage and tower), the corresponding operational data (such as tension and speed) is associated with the specific location in the model. A dynamic data update mechanism is implemented to ensure that the real-time collected data is promptly reflected in the 3D digital model. This can be achieved by setting the data update frequency and trigger conditions. Based on the real-time data and the 3D digital model, a digital twin model is generated. This model not only contains static 3D structural information but also reflects the ropeway's operating status and environmental changes in real time. The generated digital twin model is displayed using a visualization tool, providing an intuitive interface for managers to monitor the ropeway's operating status (including carriage position, rope status, equipment operation status, etc.). The digital twin model is used for data analysis to identify potential operational anomalies and provide decision support for operations management. By mapping real-time operating data into a three-dimensional digital model, the first passenger ropeway can be monitored in real time, allowing potential safety hazards to be discovered and addressed promptly. The digital twin model provides managers with a comprehensive perspective, helping them make more scientific operational decisions. By analyzing real-time data, the ropeway's operating parameters can be optimized, improving operational efficiency and safety. The mapping of real-time data makes maintenance management more precise and can identify equipment failures and maintenance needs in advance.

[0026] When it is determined based on the abnormality analysis result that an abnormality occurs in the operation of the first passenger ropeway (such as excessive tension, abnormal speed, car deviation from the track, etc.), a corresponding abnormality handling plan is generated based on the digital twin model and the abnormality analysis result; Automatically trigger the braking system and / or corresponding safety control devices according to the abnormal handling plan to ensure the safe operation of the passenger ropeway, and automatically adjust the ropeway operating parameters (such as speed, acceleration, etc.) to optimize operating efficiency and comfort; In this step, based on the anomaly analysis results, the system determines whether to initiate an anomaly handling plan. If an anomaly is detected (such as excessive tension or abnormal speed), the system automatically triggers the corresponding handling plan. Once the anomaly handling plan is triggered, the system automatically activates the braking system. Based on the severity of the anomaly, the system selects the appropriate braking method (such as emergency stop or progressive braking) to ensure the safety of the passenger ropeway. In addition to the braking system, relevant safety control devices (such as alarm systems and emergency stop devices) are automatically activated to ensure passenger safety and notify relevant personnel. While handling the anomaly, the system automatically adjusts the ropeway's operating parameters based on the current operating status and passenger comfort requirements. This includes speed adjustment (such as reducing or increasing the ropeway's operating speed based on passenger comfort and safety requirements) and acceleration adjustment (such as optimizing acceleration to reduce passenger discomfort during acceleration or deceleration). During the braking and operating parameter adjustment process, the system continuously monitors the ropeway's operating status and makes dynamic adjustments based on real-time data feedback to ensure a balance between safety and comfort. All operations and adjustments are recorded for subsequent analysis and optimization. By analyzing historical data, we can continuously improve exception handling solutions and operating parameter adjustment strategies. By automatically triggering the braking system and safety control devices, we can effectively prevent accidents and ensure passenger safety. Automatic adjustment of operating parameters (such as speed and acceleration) can improve ropeway operating efficiency and reduce unnecessary downtime.

[0027] Controlling a robot or drone equipped with detection equipment (such as a high-definition camera, an infrared thermal imager, etc.) to conduct a remote inspection of the first passenger ropeway; Transmitting the collected inspection data to a control center to determine whether there is any foreign object on the first passenger ropeway; Generate maintenance plans and task lists when foreign matter is present.

[0028] By adopting the technical solution of this embodiment, a comprehensive passenger ropeway operation monitoring system is constructed by combining intelligent sensors, machine learning, image recognition, digital twins and other technologies. This system can not only monitor and diagnose anomalies in real time, but also improve safety and operational efficiency through automation.

[0029] In some possible implementations of the present invention, the step of analyzing and training the historical data of the cableway operation database using a machine learning algorithm to construct an abnormality diagnosis model includes: Classifying the historical data according to different ropeway structures and properties and / or different operating environment states to obtain first classified data; In this step, the historical data is classified according to different ropeway structures, properties and / or operating environment states to obtain first classified data, thereby ensuring that the data used in model training is targeted and relevant.

[0030] Based on the passenger load status of each carriage in the passenger ropeway (the passenger ropeway here includes the starting and ending stations and their ancillary equipment, the load-bearing cables and traction cables between the starting and ending stations, the carriages and their ancillary equipment, and other related equipment), the first categorized data is divided into no-load operation data (such as tension, speed, position, etc.) and non-no-load operation data (such as tension, speed, position, etc. under conditions of different weights, different numbers of people, and different riding postures), thereby obtaining second categorized data; In this step, the first classification data is further divided into no-load operation data and non-no-load operation data. The no-load data includes parameters such as tension, speed, and position, while the non-no-load data considers the corresponding data under different weights, number of people, and riding postures. This classification helps the model better understand the performance under different operating conditions.

[0031] For N carriages running simultaneously in the same direction between the starting and ending stations, select the 1st to Nth carriages as the target carriages (sorted by the closest to the starting point); In this step, for the N carriages running simultaneously in the same direction between the starting and ending stations, the 1st to Nth carriages are selected as target carriages in turn, which ensures that the model can analyze the specific situation of each carriage.

[0032] Establishing a first abnormality diagnosis model when the target carriage is empty, the other N-1 carriages are empty or not empty, and the target carriage runs from the starting point to the end point, to obtain a first abnormality diagnosis model set; In this step, when the i-th carriage is empty among N carriages, the first abnormality diagnosis model for the i-th carriage has 2 N-1 Each first anomaly diagnosis model includes a tension anomaly diagnosis sub-model, a speed anomaly diagnosis sub-model, a position anomaly diagnosis sub-model, and a weather impact assessment sub-model. Carriages 1 through 4 are selected as target cars (sorted by closest to the starting point) to construct the first anomaly diagnosis models, resulting in a set of first anomaly diagnosis models for each of the N cars. This multi-layered model construction approach can improve anomaly detection accuracy.

[0033] Establishing a second abnormality diagnosis model when the target carriage is not empty, the other N-1 carriages are empty or not empty, and the target carriage runs from the starting point to the end point, to obtain a second abnormality diagnosis model set; In this step, when the i-th carriage is not empty among the N carriages, the second abnormality diagnosis model for the i-th carriage has 2 N-1Each second anomaly diagnosis model includes a tension anomaly diagnosis sub-model, a speed anomaly diagnosis sub-model, a position anomaly diagnosis sub-model, and a weather impact assessment sub-model. Carriages 1 through 4 are selected as target cars (sorted by closest to the starting point) to construct the second anomaly diagnosis models, resulting in a set of N car second anomaly diagnosis models. This approach allows the model to adapt to different operating conditions, further improving the comprehensiveness of anomaly detection.

[0034] The first abnormality diagnosis model set and the second abnormality diagnosis model set are used as abnormality diagnosis models.

[0035] In this step, the first abnormality diagnosis model set and the second abnormality diagnosis model set are used as the final abnormality diagnosis model, which can comprehensively consider abnormal situations under different operating states and improve the robustness and accuracy of the system.

[0036] In this implementation, a primary classification is performed based on the cableway's structure, attributes, and environmental status. A secondary classification is then performed based on passenger status (empty / loaded), taking into account the impact of varying loads, number of passengers, and posture on operating parameters. A diagnostic model is then built for N carriages traveling in the same direction, creating two broad categories: an empty first anomaly diagnostic model set and a non-empty second anomaly diagnostic model set. Each target carriage considers the impact of the other carriages' status. This approach not only considers different operating states but also ensures the accuracy and effectiveness of anomaly detection through a multi-layered model building and classification strategy.

[0037] In some possible implementations of the present invention, the step of constructing a tension abnormality diagnosis sub-model includes: Construct a tension data preprocessing module, including: noise reduction and filtering of the collected tension sensor data; identification and removal of abnormal data points; time series alignment and normalization of the data; and data resampling for different sampling frequencies. Establish a tension reference value calculation model, including: calculating theoretical tension values ​​based on cableway structural parameters; considering the dynamic effects of car weight and number of passengers; establishing a correction model for environmental factors (temperature, wind speed, etc.); and generating tension reference ranges under different operating conditions; Construct a tension variation feature extraction module, including: calculating the statistical characteristics of tension (mean, standard deviation, peak value, etc.); extracting the time domain characteristics of tension (rate of change, trend, etc.); analyzing the frequency domain characteristics of tension (dominant frequency, amplitude, etc.); and identifying abnormal fluctuation patterns of tension; Design a tension anomaly pattern recognizer, including: establishing primary anomaly judgment rules based on thresholds; building an anomaly classification model based on machine learning; designing a time series anomaly detector based on deep learning; and fusing multiple detection results to form a comprehensive judgment. Establish a tension influencing factor analysis module, including: analyzing the influence of car distribution on tension; evaluating the impact of weather conditions on tension; calculating the impact of operating condition conversion on tension; and establishing a causal relationship model for tension changes; Construct a tension warning mechanism, including: setting multi-level tension warning thresholds; establishing a tension trend prediction model; generating warning levels and disposal suggestions; and recording warning information and processing results.

[0038] In this embodiment, by constructing a special tension anomaly diagnosis sub-model, tension anomalies can be identified more accurately, reducing false alarms and missed alarms; the model can process monitoring data in real time, promptly discover potential safety hazards, and ensure the safe operation of the equipment; through accurate diagnosis of tension anomalies, more effective maintenance and inspection plans can be formulated to reduce equipment failure rates.

[0039] In some possible implementations of the present invention, the step of constructing a speed anomaly diagnosis sub-model includes: Construct a speed data preprocessing module, including: filtering the raw data collected by the speed sensor; detecting and correcting the mutation points in the speed data; synchronizing the time and unit of the speed data; and establishing quality assessment indicators for the speed data; Establish a speed reference range model, including: determining the standard operating speed based on cableway design parameters; calculating the permissible speed range under different load conditions; analyzing the impact of slope changes on speed; and establishing a speed correction model that takes weather conditions into account. Construct a speed feature analysis module, including: extracting the time series features of speed (acceleration, deceleration, etc.); calculating the statistical characteristics of speed fluctuations; analyzing the periodic characteristics of speed changes; and identifying abnormal speed change patterns; Design a multi-scenario speed anomaly detector, including: establishing speed anomaly detection rules for the startup phase; building a speed monitoring model for the stable operation phase; designing a speed change assessment model for the braking phase; and integrating multi-stage detection results to form an overall judgment. Establish a speed impact analysis module, including: evaluating the impact of drive system status on speed; analyzing the impact of brake system performance on speed; calculating the degree of interference from the external environment on speed; and establishing a diagnosis model for the cause of speed anomalies; Construct a speed warning mechanism, including: setting speed warning thresholds for different operating stages; establishing a speed trend prediction model; generating graded warning information; and recording abnormal speed events and handling processes.

[0040] In this embodiment, by constructing a dedicated speed anomaly diagnosis sub-model, speed anomalies can be identified more accurately, reducing false alarms and missed alarms; the model can process monitoring data in real time, promptly discover potential safety hazards, and ensure the safe operation of equipment; through accurate diagnosis of speed anomalies, more effective maintenance and repair plans can be formulated, reducing equipment failure rates.

[0041] In some possible implementations of the present invention, the step of constructing a position abnormality diagnosis sub-model includes: Construct a location data acquisition module, including: integrating GPS, Beidou and other satellite positioning data; collecting fixed sensor location data along the cableway; obtaining target tracking data from the video surveillance system; and recording the real-time coordinate information of the car position; Establish a position reference trajectory model, including: establishing a standard operating trajectory based on the cableway's physical structure; dividing key control points and monitoring areas; calculating the position allowable deviation of different linear sections; and establishing a trajectory correction model that takes temperature deformation into account. Build a position deviation analysis module, including: calculating the deviation between the real-time position and the reference trajectory; analyzing the lateral and longitudinal swing amplitude of the carriage; evaluating the changes in the distance between adjacent carriages; and identifying abnormal displacement and drift phenomena; Design a multi-dimensional position anomaly detector, including: establishing a threshold-based position deviation detection rule; building an anomaly recognition model based on trajectory features; designing an algorithm for monitoring the anomaly of the distance between train cars; and integrating multi-source data for comprehensive judgment. Establish a position anomaly cause analysis module, including: evaluating the impact of wind force on position deviation; analyzing position anomalies caused by support structure deformation; calculating the impact of load distribution on trajectory; and establishing a causal relationship model for position anomalies; Construct a location warning mechanism, including: setting location warning thresholds for different regions; establishing a location deviation trend prediction model; generating multi-level warning information; and recording location anomaly information and handling measures.

[0042] In this embodiment, by constructing a special position anomaly diagnosis sub-model, position anomalies can be identified more accurately and false alarms and missed alarms can be reduced; the model can process monitoring data in real time, promptly discover potential safety hazards, and ensure the safe operation of the equipment; through accurate diagnosis of position anomalies, more effective maintenance and inspection plans can be formulated to reduce equipment failure rates.

[0043] In some possible implementations of the present invention, the step of constructing a meteorological impact assessment sub-model includes: Construct a meteorological data collection module, including: collecting basic meteorological data such as wind speed, wind direction, temperature, and humidity; obtaining special weather data such as precipitation and visibility; recording environmental parameters such as air pressure and ice and snow load; and establishing a real-time update mechanism for meteorological data; Establish a quantitative analysis module for meteorological impacts, including: calculating the force exerted by wind loads on the cableway system; analyzing the structural deformation caused by temperature changes; evaluating the impact of precipitation on the friction coefficient; and establishing an accumulation model for ice and snow loads; Establish a meteorological condition classification mechanism, including: establishing multi-level assessment standards for meteorological conditions; setting operating limits for different meteorological levels; formulating dynamic adjustment rules for meteorological levels; and generating meteorological warning threshold parameters; Design a meteorological impact prediction model, including: obtaining meteorological forecast data; establishing a short-term meteorological trend model; predicting the impact of meteorological changes on the system; and generating preventive control recommendations; Establish a weather-system coupling analysis module, including: analyzing the correlation between weather factors and tension changes; evaluating the impact of weather conditions on speed control; calculating position deviations caused by weather changes; and establishing a multi-factor coupling effect model; Construct a meteorological response strategy module, including: formulating operation strategies under different meteorological conditions; designing emergency plans for extreme weather; generating recommendations for meteorological protection measures; and recording the process of dealing with meteorological impacts.

[0044] In this embodiment, by constructing a special meteorological impact assessment sub-model, the impact of meteorological factors on specific events can be assessed more accurately, reducing misjudgments and missed judgments; the model can process meteorological data in real time, promptly detect potential meteorological impacts, and ensure the scientific nature of decision-making.

[0045] In some possible implementations of the present invention, the step of using an image recognition algorithm to analyze the video image data, identify the carriage posture and passenger behavior, and obtain a video recognition result includes: Preprocessing the collected video image data to obtain first image data; In this step, the collected video image data is preprocessed, including: adjusting the resolution, denoising and contrast enhancement of the video image; extracting frames from the video image to obtain a key frame sequence; and performing distortion correction and image registration on the key frames to improve the accuracy of subsequent analysis.

[0046] Extracting key image features of the first image data (in the video frame) using image processing technology (such as the edge, outline, and motion trajectory of the vehicle compartment, which will be used for subsequent posture and behavior recognition); Combined with the key features of the image, an image recognition algorithm (such as a convolutional neural network (CNN)) is applied to analyze the carriage's posture and calculate the tilt angle and swing amplitude (by comparing the extracted features with a preset posture model, determining whether the carriage is in a normal state); In this step, first, car target detection and tracking are performed, including: using a deep learning target detection algorithm to identify the car position in the video; establishing a car target tracking model to achieve continuous tracking of the car; extracting the contour features and key point information of the car; secondly, analyzing the car posture parameters, including: calculating the tilt angle of the car based on the car contour features; calculating the swing amplitude and frequency of the car through continuous frame analysis; and combining sensor data to perform fusion correction of the posture parameters.

[0047] Analyze passenger behavior using deep learning models to identify dangerous actions (such as fighting or falling) or overloading (the model can identify abnormal behavior patterns by learning from historical data); In this step, deep learning models (such as RNN or LSTM) are used to analyze passenger behavior, including: using human detection algorithms to identify the number and location of passengers in the car; analyzing passenger postures based on human key point detection technology; and establishing a behavior classification model to identify dangerous actions.

[0048] Integrate the recognition results to generate a video recognition result report, including the tilt angle and swing amplitude of the carriage posture and the safety assessment of passenger behavior; In this step, first, an abnormal behavior recognition model is constructed, including: setting the threshold range of posture parameters for normal operation of the carriage; defining a characteristic pattern library of dangerous passenger behavior; establishing time-series-based abnormal behavior judgment rules; secondly, generating video recognition results, including: recording abnormal carriage posture events, including exceeding the limit of tilt angle, excessive swing amplitude, etc.; marking abnormal passenger behavior, including dangerous actions, overloading, etc.; generating real-time warning information and abnormal behavior description reports.

[0049] The video recognition results are fed back to the monitoring system in real time. If any abnormal situation is detected, the system can automatically issue an alarm and record relevant data for subsequent analysis and processing.

[0050] The solution of this embodiment can promptly identify potential safety hazards and reduce the risk of accidents by monitoring the carriage posture and passenger behavior in real time. The identification results provide data support for operational management, helping managers make more scientific decisions. By monitoring passenger behavior, passenger safety and comfort are ensured, and the overall riding experience is improved. The need for manual monitoring is reduced, monitoring efficiency and accuracy are improved, and labor costs are reduced.

[0051] In some possible implementations of the present invention, when it is determined based on the abnormality analysis result that an abnormality occurs in the operation of the first passenger ropeway, the step of generating a corresponding abnormality handling solution based on the digital twin model and the abnormality analysis result includes: Based on real-time monitoring data and abnormality analysis results, determine whether the first passenger ropeway has any abnormal conditions (such as excessive tension, abnormal speed, car deviating from the track, etc.); When an abnormal situation occurs, a digital scene of the abnormal situation is constructed based on the abnormal analysis results and the digital twin model; This step specifically includes: establishing a three-dimensional scene of the current abnormal state based on the digital twin model; mapping key parameters in the abnormal analysis results to the digital scene; and analyzing the abnormal development trend based on historical data.

[0052] Evaluate detected abnormalities, analyze their possible causes and impacts, use digital twin models to simulate the impact of abnormalities on cableway operations, and assess safety risks; This step includes the steps of grading abnormal situations, specifically: establishing a multi-dimensional abnormal rating standard including tension abnormality, speed abnormality, track deviation, etc.; calculating the deviation and danger level of each abnormal indicator; determining the abnormality level and judging whether emergency treatment is required.

[0053] Based on the evaluation results of abnormal situations and combined with the digital twin model, corresponding abnormality handling solutions are generated.

[0054] In this step, generating an abnormality handling plan includes: generating an emergency plan, formulating a control parameter adjustment plan, building a disposal measure execution plan, and establishing an effect evaluation mechanism. Specifically, generating an emergency plan includes: calling a preset emergency disposal rule library, conducting emergency measure simulation verification based on a digital twin model, and calculating the risk and effect evaluation indicators of different emergency plans; formulating a control parameter adjustment plan includes: analyzing the correlation between abnormal parameters and control parameters, calculating the optimal control parameter combination, and generating the execution sequence of parameter adjustment; building a disposal measure execution plan includes: determining the trigger conditions of the braking system and safety control device, designing the execution sequence of control instructions, and formulating a coordination plan for manual intervention; and establishing an effect evaluation mechanism includes: setting evaluation indicators for disposal effects, building a real-time feedback monitoring plan, and preparing alternative emergency plans.

[0055] Specifically, the treatment plan can be: Emergency shutdown measures: In the event of serious abnormalities, formulate a plan for immediate shutdown.

[0056] Adjust operating parameters: such as tension, speed, etc. to restore normal operation.

[0057] Maintenance and repair suggestions: Propose specific maintenance and repair measures for equipment failures.

[0058] The solution of this embodiment can effectively reduce accident risks and ensure passenger safety by timely judging and handling abnormal situations; quickly generating and implementing abnormal handling plans improves the ability to respond to emergencies and reduces potential losses.

[0059] In some possible implementations of the present invention, the step of inputting the first data collected from the first passenger ropeway into the abnormality diagnosis model, determining whether there is an abnormality in the operation of the first passenger ropeway, and obtaining a ropeway abnormality diagnosis result of the first passenger ropeway includes: Acquire first structural data, first attribute data, first working environment data, and first passenger status data of each carriage between the starting and ending stations of the first passenger ropeway; In this step, the first structural data includes the physical structural information of the first passenger ropeway, such as the erection shape, the number of carriages between the starting and ending stations, etc.; the first attribute data involves the physical and chemical properties of the material, which may affect the operational safety of the ropeway; the first working environment data includes environmental factors, such as weather conditions, temperature, humidity, etc., which may affect the operation of the ropeway; the first passenger status data involves the status information of the passengers, such as whether there are passengers, the number of passengers, the distribution of passengers and the posture of passengers, etc. These data are crucial for judging the load condition of the ropeway.

[0060] Selecting a corresponding optional abnormality diagnosis model from the abnormality diagnosis models based on the first structural data, the first attribute data, the first working environment data, and the first passenger status data (such as whether there are passengers, the number of passengers, and the passenger posture, etc.) (selecting an abnormality diagnosis model corresponding to a structure (such as the shape of the installation, the number of carriages between the starting and ending stations, etc.), attributes (such as the physical and chemical properties of the material, etc.), and working environment that are identical or similar (i.e., meet a preset similarity ratio) to the first passenger ropeway); In this step, a model matching the first passenger ropeway is selected from the abnormality diagnosis model based on the acquired first structural data, attribute data, working environment data and passenger status data; this process ensures that the selected model can effectively reflect the current operating status of the ropeway, especially when the structure and attributes reach a preset similarity ratio.

[0061] Acquire the current environmental status data and current passenger status data of the first passenger ropeway in real time; In this step, the current environmental status data: real-time monitoring of the environmental conditions of the first passenger ropeway, so as to promptly reflect the impact of external factors on the operation; the current passenger status data: real-time acquisition of passenger status information, so as to dynamically evaluate the load condition of the ropeway.

[0062] It is determined whether there is any abnormality in the operation of the first passenger ropeway according to the current environmental state data, the current passenger state data and the optional abnormality diagnosis model, and a ropeway abnormality diagnosis result of the first passenger ropeway is obtained.

[0063] In this step, a comprehensive analysis is performed based on the current environmental status data, the current passenger status data, and the optional abnormality diagnosis model to determine whether there is any abnormality in the operation of the first passenger ropeway. This process will generate a ropeway abnormality diagnosis result for the first passenger ropeway.

[0064] This embodiment describes how to use data collected from the first passenger ropeway in combination with an anomaly diagnosis model for real-time monitoring and anomaly judgment. This not only takes into account structural and environmental factors, but also dynamically reflects the passenger status, thereby improving the accuracy and timeliness of anomaly detection. A multi-dimensional similarity matching mechanism is established to give the model better generalization. Static and dynamic features are processed separately to improve system efficiency. This achieves a closed loop of real-time monitoring and anomaly diagnosis.

[0065] In some possible implementations of the present invention, the step of determining whether there is an abnormality in the operation of the first passenger ropeway based on the current environmental state data, the current passenger state data, and the optional abnormality diagnosis model, and obtaining a ropeway abnormality diagnosis result for the first passenger ropeway includes: For the M carriages running simultaneously in the same direction between the starting and ending stations of the first passenger ropeway, the first to the Mth carriages are selected in sequence as the carriages to be diagnosed (sorted by the one closest to the starting point); In this step, for the M carriages running simultaneously in the same direction between the starting and ending stations of the first passenger ropeway, the 1st to Mth carriages are selected in sequence as the carriages to be diagnosed. This selection method is carried out in order of distance from the starting point, ensuring the systematic and orderly nature of the diagnosis process.

[0066] selecting a corresponding first current abnormality diagnosis model from the optional abnormality diagnosis models according to the first current passenger state data corresponding to the carriage to be diagnosed in the current passenger state data; In this step, first current passenger state data (including information such as the passenger's behavior, posture, emotion, and location) related to the carriage to be diagnosed is determined from the current passenger state data. A model corresponding to the first current passenger state data is selected from the selectable abnormality diagnosis models (selection can be based on passenger similarity, model applicability, historical performance, and feature matching). Feature extraction is performed on the first current passenger state data to identify key features (such as passenger density and emotional fluctuations) for matching with the abnormality diagnosis model. The extracted features are input into the selected first current abnormality diagnosis model to perform abnormality diagnosis analysis. Based on the model's analysis results, a diagnostic report is generated, indicating the presence of abnormalities and their possible causes. Feedback after the model application is collected, the accuracy of the diagnostic results is analyzed, and the model selection and feature extraction process is continuously optimized based on the feedback. By selecting an abnormality diagnosis model that matches the current passenger state data, potential abnormalities can be more accurately identified, reducing false positives and missed negatives. This method enables real-time processing of passenger state data, rapid response to abnormalities, and improved passenger safety and comfort. Accurate abnormality diagnosis allows for more efficient resource allocation, timely implementation of measures, and reduced operational risks. According to the status information corresponding to the carriage to be diagnosed in the current passenger status data, the corresponding first current abnormality diagnosis model is selected from the optional abnormality diagnosis models. This process ensures that the model can analyze the actual passenger situation in the specific carriage.

[0067] determining whether the first passenger ropeway has an abnormality according to the current environmental state data, the first current passenger state data, and the first current abnormality diagnosis model, and obtaining a first abnormality diagnosis result; All first abnormality diagnosis results obtained by performing the aforementioned abnormality diagnosis step on all the M carriages to be diagnosed are integrated and analyzed to obtain a cableway abnormality diagnosis result of the first passenger cableway.

[0068] In this step, all the first abnormality diagnosis results obtained from the abnormality diagnosis steps for all M cars to be diagnosed are integrated and analyzed, and finally the abnormality diagnosis result of the first passenger ropeway is obtained. This integrated analysis can provide a more comprehensive operation status assessment and ensure the accurate identification of potential abnormalities.

[0069] This example describes how to perform anomaly diagnosis in multiple carriages and integrate the diagnostic results from each carriage to derive an overall cableway anomaly diagnosis. This not only improves the accuracy of anomaly detection but also enhances the system's adaptability to different operating conditions. This example reduces the complexity of the problem through a divide-and-conquer approach, ensuring that each carriage receives an independent and complete diagnosis. The final integrated analysis can identify systemic issues that might be missed by single-carriage diagnosis.

[0070] In some possible implementations of the present invention, the step of controlling a robot or drone equipped with a detection device to perform a remote inspection of the first passenger ropeway includes: Develop a detailed inspection plan based on the structure and operation characteristics of the first passenger ropeway, including inspection routes, inspection points and inspection items; In this step, the inspection route is formulated, including: generating inspection waypoint coordinates based on the three-dimensional digital model of the cableway; calculating the optimal inspection path to ensure the best observation angle for the inspection equipment; setting obstacle avoidance rules to prevent collisions with cableway facilities; and planning an alternative return path to deal with emergencies.

[0071] Determine the inspection schedule and select an inspection environment that meets preset conditions (such as preset weather parameters) to ensure inspection effectiveness; Establish wireless communication links with robots or drones to ensure data can be transmitted to the control center in real time; Configure the control system and set the parameters of the inspection task (such as flight altitude, speed and shooting frequency); Start the robot or drone to conduct inspections along the inspection route; In this step, the inspection equipment is started according to the inspection route, multi-source detection data of the cableway components are obtained in real time, the flight attitude and detection angle are dynamically adjusted according to the environmental conditions, and automatic obstacle avoidance and maintaining a safe distance are performed.

[0072] During the inspection process, high-definition cameras and infrared thermal imagers are used to collect cableway data in real time to obtain inspection data.

[0073] In this step, real-time data collection is performed, including: synchronous collection of visual images, thermal imaging and position data; real-time compression and transmission of collected data; establishment of a backup mechanism for data collection; and recording of inspection trajectories and environmental parameters.

[0074] The solution of this embodiment, through the use of robots or drones for remote inspections, can cover a wider area, reducing the time and cost of manual inspections. The use of equipment such as infrared thermal imagers can promptly identify potential safety hazards and reduce the risk of accidents. High-definition cameras and infrared thermal imagers provide high-quality data, enhancing the accuracy of fault detection. In some possible implementations of the present invention, the following further aspects are included: The collected inspection data is transmitted to the control center in real time for data processing and analysis; Identify potential faults and abnormal conditions (such as foreign objects on the surface, equipment overheating, structural damage, etc.) through image recognition and thermal imaging technology; In this step, potential faults and anomalies are identified, including: detecting surface defects using image recognition algorithms; analyzing equipment temperature anomalies through thermal imaging; calculating structural deformation based on point cloud data; and fusing multi-source data for comprehensive fault diagnosis.

[0075] Generate an inspection report based on the analysis results, recording any abnormalities found and recommended treatment measures.

[0076] In the solution of this embodiment, real-time data transmission and analysis enable managers to quickly respond to abnormal situations, thereby improving the efficiency of operations management.

[0077] See Figure 2 ,Another embodiment of the present invention provides a passenger ropeway operation monitoring device for executing a passenger ropeway operation monitoring method, comprising: a data acquisition module and a control processing module; The data acquisition module is used to acquire data collected by the intelligent sensors installed on the passenger ropeway; The control processing module is used for: Clean, organize and store the data collected by intelligent sensors to establish a cableway operation database; Use machine learning algorithms to analyze and train historical data from the cableway operation database to build an abnormality diagnosis model; inputting first data collected from the first passenger ropeway into the abnormality diagnosis model to determine whether there is an abnormality in the operation of the first passenger ropeway, thereby obtaining a ropeway abnormality diagnosis result of the first passenger ropeway; Controlling cameras installed at preset key positions along the first passenger ropeway to collect video image data; Analyzing the video image data using an image recognition algorithm to identify the carriage posture and passenger behavior to obtain a video recognition result; Performing a comprehensive analysis and judgment based on the cableway abnormality diagnosis result and the video recognition result to obtain an abnormality analysis result; constructing a three-dimensional digital model based on the actual physical structure, three-dimensional data, geographical location, and operating parameters of the first passenger ropeway; Mapping the first cableway operation data of the first passenger cableway collected in real time into a three-dimensional digital model to generate a digital twin model; When it is determined according to the abnormality analysis result that an abnormality occurs in the operation of the first passenger ropeway, a corresponding abnormality handling solution is generated according to the digital twin model and the abnormality analysis result; Automatically triggering the braking system and / or corresponding safety control devices according to the abnormality handling plan to ensure the safe operation of the passenger ropeway, and automatically adjusting the operating parameters of the ropeway to optimize operating efficiency and comfort; Controlling a robot or drone equipped with a detection device to perform a remote inspection of the first passenger ropeway; Transmitting the collected inspection data to a control center to determine whether there is any foreign object on the first passenger ropeway; Generate maintenance plans and task lists when foreign matter is present.

[0078] It should be known that Figure 2 The block diagram of the passenger ropeway operation monitoring device shown is for illustrative purposes only, and the number of modules shown does not limit the scope of protection of the present invention. The passenger ropeway operation monitoring device provided in this embodiment can be used to implement the various embodiments of the corresponding passenger ropeway operation monitoring method. For specific implementation procedures, please refer to the description of the various method embodiments and will not be repeated here.

[0079] Another embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a passenger ropeway operation monitoring method when executing the computer program.

[0080] Another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the steps of a passenger ropeway operation monitoring method.

[0081] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0082] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0084] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0086] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0087] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program. The program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0088] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0089] Although the present invention is disclosed above, it is not limited thereto. Any person skilled in the art may readily conceive of variations or substitutions, and may make various modifications and alterations without departing from the spirit and scope of the present invention. Combinations of the above-described functions and implementation steps, including software and hardware implementations, are all within the scope of protection of the present invention.

Claims

1. A passenger ropeway operation monitoring method, characterized in that: include: Obtain data collected by smart sensors installed on passenger ropeways; Clean, organize and store the data collected by intelligent sensors to establish a cableway operation database; Use machine learning algorithms to analyze and train historical data from the cableway operation database to build an abnormality diagnosis model; inputting first data collected from the first passenger ropeway into the abnormality diagnosis model to determine whether there is an abnormality in the operation of the first passenger ropeway, thereby obtaining a ropeway abnormality diagnosis result of the first passenger ropeway; Controlling cameras installed at preset key positions along the first passenger ropeway to collect video image data; Analyzing the video image data using an image recognition algorithm to identify the carriage posture and passenger behavior to obtain a video recognition result; Performing a comprehensive analysis and judgment based on the cableway abnormality diagnosis result and the video recognition result to obtain an abnormality analysis result; constructing a three-dimensional digital model based on the actual physical structure, three-dimensional data, geographical location, and operating parameters of the first passenger ropeway; Mapping the first cableway operation data of the first passenger cableway collected in real time into a three-dimensional digital model to generate a digital twin model; When it is determined according to the abnormality analysis result that an abnormality occurs in the operation of the first passenger ropeway, a corresponding abnormality handling solution is generated according to the digital twin model and the abnormality analysis result; Automatically triggering the braking system and / or corresponding safety control devices according to the abnormality handling plan to ensure the safe operation of the passenger ropeway, and automatically adjusting the operating parameters of the ropeway to optimize operating efficiency and comfort; Controlling a robot or drone equipped with a detection device to perform a remote inspection of the first passenger ropeway; Transmitting the collected inspection data to a control center to determine whether there is any foreign object on the first passenger ropeway; Generate maintenance plans and task lists when foreign matter is present.

2. The passenger ropeway operation monitoring method according to claim 1, characterized in that: The step of using a machine learning algorithm to analyze and train the historical data of the cableway operation database to build an abnormality diagnosis model includes: Classifying the historical data according to different ropeway structures and properties and / or different operating environment states to obtain first classified data; The first classification data is divided into empty-load operation data and non-empty-load operation data according to the passenger load status of each carriage in the passenger ropeway to obtain second classification data; For N carriages running simultaneously in the same direction between the starting and ending stations, the first to Nth carriages are selected as the target carriages in turn; Establishing a first abnormality diagnosis model when the target carriage is empty, the other N-1 carriages are empty or not empty, and the target carriage runs from the starting point to the end point, to obtain a first abnormality diagnosis model set; Establishing a second abnormality diagnosis model when the target carriage is not empty, the other N-1 carriages are empty or not empty, and the target carriage runs from the starting point to the end point, to obtain a second abnormality diagnosis model set; The first abnormality diagnosis model set and the second abnormality diagnosis model set are used as abnormality diagnosis models.

3. The passenger ropeway operation monitoring method according to claim 2, characterized in that: The step of using an image recognition algorithm to analyze the video image data, identify the carriage posture and passenger behavior, and obtain a video recognition result includes: Preprocessing the collected video image data to obtain first image data; extracting key image features of the first image data using image processing technology; Combining the key features of the image, applying image recognition algorithms to analyze the posture of the carriage and calculate the tilt angle and swing amplitude; Use deep learning models to analyze passenger behavior and identify dangerous movements or overloading; Integrate the recognition results to generate a video recognition result report, including the tilt angle and swing amplitude of the carriage posture and the safety assessment of passenger behavior; The video recognition results are fed back to the monitoring system in real time. If any abnormal situation is detected, the system can automatically issue an alarm and record relevant data for subsequent analysis and processing.

4. The passenger ropeway operation monitoring method according to claim 3, characterized in that: The step of generating a corresponding abnormality handling solution based on the digital twin model and the abnormality analysis result when it is determined that an abnormality occurs in the operation of the first passenger ropeway according to the abnormality analysis result includes: Determine whether the first passenger ropeway has any abnormality based on real-time monitoring data and abnormality analysis results; When an abnormal situation occurs, a digital scene of the abnormal situation is constructed based on the abnormal analysis results and the digital twin model; Evaluate detected abnormalities, analyze their possible causes and impacts, use digital twin models to simulate the impact of abnormalities on cableway operations, and assess safety risks; Based on the evaluation results of abnormal situations and combined with the digital twin model, corresponding abnormality handling solutions are generated.

5. The passenger ropeway operation monitoring method according to claim 4, characterized in that: The step of inputting the first data collected from the first passenger ropeway into the abnormality diagnosis model, determining whether there is an abnormality in the operation of the first passenger ropeway, and obtaining a ropeway abnormality diagnosis result of the first passenger ropeway includes: Acquire first structural data, first attribute data, first working environment data, and first passenger status data of each carriage between the starting and ending stations of the first passenger ropeway; selecting a corresponding optional abnormality diagnosis model from the abnormality diagnosis models according to the first structural data, the first attribute data, the first working environment data, and the first passenger status data; Acquire the current environmental status data and current passenger status data of the first passenger ropeway in real time; It is determined whether there is any abnormality in the operation of the first passenger ropeway according to the current environmental state data, the current passenger state data and the optional abnormality diagnosis model, and a ropeway abnormality diagnosis result of the first passenger ropeway is obtained.

6. The passenger ropeway operation monitoring method according to claim 5, characterized in that: The step of determining whether there is an abnormality in the operation of the first passenger ropeway according to the current environmental state data, the current passenger state data and the optional abnormality diagnosis model, and obtaining a ropeway abnormality diagnosis result of the first passenger ropeway, comprises: For M carriages running simultaneously in the same direction between the starting and ending stations of the first passenger ropeway, sequentially selecting the first to the Mth carriages as carriages to be diagnosed; selecting a corresponding first current abnormality diagnosis model from the optional abnormality diagnosis models according to the first current passenger state data corresponding to the carriage to be diagnosed in the current passenger state data; determining whether the first passenger ropeway has an abnormality according to the current environmental state data, the first current passenger state data, and the first current abnormality diagnosis model, and obtaining a first abnormality diagnosis result; All first abnormality diagnosis results obtained by performing the aforementioned abnormality diagnosis step on all the M carriages to be diagnosed are integrated and analyzed to obtain a cableway abnormality diagnosis result of the first passenger cableway.

7. The passenger ropeway operation monitoring method according to claim 6, characterized in that: The step of controlling a robot or drone equipped with a detection device to perform a remote inspection of the first passenger ropeway includes: Develop a detailed inspection plan based on the structure and operation characteristics of the first passenger ropeway, including inspection routes, inspection points and inspection items; Determine the inspection schedule and select an inspection environment that meets the preset conditions to ensure inspection effectiveness; Establish wireless communication links with robots or drones to ensure data can be transmitted to the control center in real time; Configure the control system and set the parameters of the inspection tasks; Start the robot or drone to conduct inspections along the inspection route; During the inspection process, high-definition cameras and infrared thermal imagers are used to collect cableway data in real time to obtain inspection data.

8. A passenger ropeway operation monitoring device, used to execute the passenger ropeway operation monitoring method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module and control processing module; The data acquisition module is used to acquire data collected by the intelligent sensors installed on the passenger ropeway; The control processing module is used for: Clean, organize and store the data collected by intelligent sensors to establish a cableway operation database; Use machine learning algorithms to analyze and train historical data from the cableway operation database to build an abnormality diagnosis model; inputting first data collected from the first passenger ropeway into the abnormality diagnosis model to determine whether there is an abnormality in the operation of the first passenger ropeway, thereby obtaining a ropeway abnormality diagnosis result of the first passenger ropeway; Controlling cameras installed at preset key positions along the first passenger ropeway to collect video image data; Analyzing the video image data using an image recognition algorithm to identify the carriage posture and passenger behavior to obtain a video recognition result; Performing a comprehensive analysis and judgment based on the cableway abnormality diagnosis result and the video recognition result to obtain an abnormality analysis result; constructing a three-dimensional digital model based on the actual physical structure, three-dimensional data, geographical location, and operating parameters of the first passenger ropeway; Mapping the first cableway operation data of the first passenger cableway collected in real time into a three-dimensional digital model to generate a digital twin model; When it is determined according to the abnormality analysis result that an abnormality occurs in the operation of the first passenger ropeway, a corresponding abnormality handling solution is generated according to the digital twin model and the abnormality analysis result; Automatically triggering the braking system and / or corresponding safety control devices according to the abnormality handling plan to ensure the safe operation of the passenger ropeway, and automatically adjusting the operating parameters of the ropeway to optimize operating efficiency and comfort; Controlling a robot or drone equipped with a detection device to perform a remote inspection of the first passenger ropeway; Transmitting the collected inspection data to a control center to determine whether there is any foreign object on the first passenger ropeway; Generate maintenance plans and task lists when foreign matter is present.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the passenger ropeway operation monitoring method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the passenger ropeway operation monitoring method according to any one of claims 1 to 7 are implemented.

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