Passenger ropeway intelligent safety monitoring method and device and electronic equipment

Abstract

This application provides a method, device, and electronic equipment for intelligent safety monitoring of passenger ropeways, belonging to the field of electromechanical special equipment safety monitoring technology. The method includes: acquiring multi-source operational data of the target passenger ropeway and encapsulating the multi-source operational data to form a standardized data packet; verifying the validity of the standardized data packet based on the operating mode and physical fingerprint of the target passenger ropeway, and extracting feature parameters from the verified data to obtain feature data; performing multi-dimensional fusion analysis on the feature data using a time-series trend analysis method to obtain fused feature indicators reflecting the operational trend of the target passenger ropeway; and determining the comprehensive monitoring status of the target passenger ropeway based on the fused feature indicators and a preset safety threshold. The intelligent safety monitoring method, device, and electronic equipment for passenger ropeways provided in this application can improve the ability to predict and warn of potential safety hazards in passenger ropeways.

Description

Technical Field

[0001] This application belongs to the field of safety monitoring technology for special electromechanical equipment, and more specifically, it relates to an intelligent safety monitoring method and device for passenger ropeways, as well as electronic equipment. Background Technology

[0002] Passenger ropeways, as an important type of special transportation equipment, play an irreplaceable role in tourist attractions, mountain transportation, and other fields.

[0003] Currently, existing safety monitoring methods for passenger ropeways mainly rely on single sensor monitoring and periodic manual inspections. Regarding sensor monitoring, various sensors are typically installed at key locations along the ropeway to independently monitor different parameters. When a parameter exceeds a preset, fixed threshold, the system will issue an alarm.

[0004] However, existing monitoring methods have many shortcomings. On the one hand, independent monitoring by a single sensor can only obtain local and isolated parameter information, failing to comprehensively reflect the overall operating status of the cableway. Furthermore, the signals have poor readability, making it difficult for non-professionals to identify defects immediately, and there is a lack of early fault prediction. Because of the complex dynamic relationships between various parameters, judging the cableway's safety status solely based on whether a single parameter exceeds its limits is prone to misjudgment or omission. It also lacks the ability to analyze and predict cableway operating trends in real time, making it impossible to detect potential safety risks early and take timely preventative measures. On the other hand, the results of manual inspections largely depend on the experience and skill level of the inspectors, introducing subjectivity and uncertainty, making it difficult to guarantee that every inspection accurately identifies all potential problems. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, and electronic equipment for intelligent safety monitoring of passenger ropeways, which can improve the ability to predict and warn of potential safety hazards. To achieve the above objective, the technical solution provided by this application is as follows: Firstly, a method for intelligent safety monitoring of passenger ropeways is provided, including: Acquire multi-source operational data of the target passenger ropeway and encapsulate the multi-source operational data to form a standardized data package; the multi-source operational data includes ropeway operation data and environmental data of the area where the target passenger ropeway is located. The ropeway operation data includes current data, power supply voltage data, operating power, power supply frequency data, shell temperature data of each motor, and real-time video display data of the target passenger ropeway. The environmental data of the area where the target passenger ropeway is located includes wind speed data and wind direction data. Based on the operating mode and physical fingerprint of the target passenger ropeway, the standardized data package is validated for validity, and feature parameters are extracted from the validated data to obtain feature data; the physical fingerprint of the equipment includes the reasonable value range of each operating parameter and dynamic association rules. By using time-series trend analysis methods to perform multi-dimensional fusion analysis on feature data, a fusion feature index reflecting the operating trend of the target passenger ropeway is obtained. The comprehensive monitoring status of the target passenger ropeway is determined based on the integrated characteristic indicators and preset safety thresholds.

[0006] Secondly, an intelligent safety monitoring device for passenger ropeways is provided, comprising: The data acquisition module acquires multi-source operational data of the target passenger cableway and encapsulates the multi-source operational data into a standardized data package. The multi-source operational data includes cableway operation data and environmental data of the area where the target passenger cableway is located. The cableway operation data includes current data, power supply voltage data, operating power, power supply frequency data, shell temperature data of each motor, and real-time video display data of the target passenger cableway. The environmental data of the area where the target passenger cableway is located includes wind speed data and wind direction data. The feature data acquisition module is used to verify the validity of standardized data packets based on the operating mode and physical fingerprint of the target passenger ropeway, and extract feature parameters from the verified data to obtain feature data; the physical fingerprint of the equipment includes the reasonable value range of each operating parameter and dynamic association rules. The fusion feature index acquisition module is used to perform multi-dimensional fusion analysis on feature data through time series trend analysis methods to obtain fusion feature indicators that reflect the operating trend of the target passenger ropeway. The integrated monitoring module is used to determine the integrated monitoring status of the target passenger ropeway based on the fusion characteristic indicators and preset safety thresholds.

[0007] Thirdly, embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the intelligent safety monitoring method for passenger ropeways provided in any possible implementation of the first aspect.

[0008] The beneficial effects of the technical solution provided in this application are as follows: Compared with related technologies, the intelligent safety monitoring method, device, and electronic equipment for passenger ropeways provided in this application embodiment acquire multi-source operational data and encapsulate it into a standardized data package, covering information on multiple aspects of ropeway operation and environment. This allows for a multi-dimensional description of the ropeway's operational status, solving the problem that independent monitoring by a single sensor can only obtain local, isolated parameter information and cannot comprehensively reflect the overall operational status of the ropeway. This embodiment verifies the validity of data and extracts feature parameters based on the physical fingerprint of the equipment, considering the reasonable range and dynamic correlation between various operational parameters, improving the accuracy of data processing and reducing misjudgments or omissions caused by relying solely on a single parameter. Furthermore, it uses a time-series trend analysis method to perform multi-dimensional fusion analysis of feature data, obtaining fusion feature indicators reflecting operational trends. This provides real-time analysis and prediction capabilities for ropeway operational trends, enabling early detection of potential safety risks and timely preventative measures. Based on data collection, analysis, and processing, this embodiment reduces the subjectivity and uncertainty caused by human factors, enabling a more objective and accurate determination of the comprehensive monitoring status of the target passenger ropeway. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0010] Figure 1 A flowchart illustrating the intelligent safety monitoring method for passenger ropeways provided in this application embodiment; Figure 2 Structural block diagram of the intelligent safety monitoring device for passenger ropeways provided in the embodiments of this application; Figure 3 A schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0011] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0012] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” can be implemented as “A,” or as “B,” or as “A and B.” When describing multiple (two or more) items, if the relationship between the multiple items is not explicitly defined, the multiple items can refer to one, several or all of the multiple items. For example, the description of "parameter A includes A1, A2, A3" can be implemented as parameter A includes A1 or A2 or A3, or it can be implemented as parameter A includes at least two of the three items A1, A2 and A3.

[0013] It is understood that in the embodiments of this application, data such as user information are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0014] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0015] This application provides an intelligent safety monitoring method for passenger ropeways, which can be executed by electronic devices, such as... Figure 1 As shown, the method may include: S101: Acquire multi-source operation data of the target passenger ropeway and encapsulate the multi-source operation data to form a standardized data packet.

[0016] In this embodiment, the multi-source operation data includes cableway operation data and environmental data of the area where the target passenger cableway is located. The cableway operation data includes current data, power supply voltage data, operating power, power supply frequency data, shell temperature data, and real-time video display data of the target passenger cableway. The environmental data of the area where the target passenger cableway is located includes wind speed data and wind direction data.

[0017] This embodiment collects data detected by various sensors (e.g., temperature, current, voltage, wind speed, wind direction, etc.) at the same time. Since the sensors are located in different positions, their internal clocks may have slight deviations, resulting in varying network latency when data is transmitted from the sensors to the electronic device. To reduce the deviation in data acquisition timing, this embodiment groups various types of data acquired at the same acquisition time from different sensors and assigns a unified clock master timestamp to each group of data as the unique time identifier for that data packet, ensuring all data has a consistent time base. For example, a unified and authoritative clock source is used to assign a unique timestamp to all sensor data received within a very short acquisition cycle, and this timestamp is used as the unified clock master timestamp. This embodiment uses a unified clock master timestamp to reduce the minute time differences caused by device clock asynchrony and network transmission jitter.

[0018] This embodiment can obtain standardized data packets by filling the grouped data into a predetermined data format according to fixed field definitions. The standardized data packets can include packet header information, data body, and packet footer information. The packet header information includes data packet ID, master timestamp, data packet generation time, data source identifier, and trigger event type, etc.; the data body can be stored in an ordered manner in the form of key-value pairs or arrays, containing all cableway operation data and actual measured values ​​of environmental data of the target passenger cableway area; the packet footer information can include data integrity verification information such as cyclic redundancy check codes to verify whether the data is complete and error-free during transmission or storage.

[0019] This embodiment encapsulates the data provided by each sensor into a unified format through an encapsulation process, reducing data complexity. The standardized data packet formed after encapsulation in this embodiment adds a cyclic redundancy check code, which can verify whether the data packet has been damaged or tampered with during transmission. The encapsulated data packet with complete length information and structural identifier obtained in this embodiment achieves standardization of multi-source heterogeneous data, which facilitates transmission and parsing, and provides convenience for subsequent unified timing analysis, validity verification and feature extraction.

[0020] S102: Based on the operating mode and physical fingerprint of the target passenger ropeway, verify the validity of the standardized data packet, and extract feature parameters from the verified data to obtain feature data.

[0021] In this embodiment, the physical fingerprint of the equipment includes the reasonable value range of each operating parameter and dynamic association rules. The feature data in this embodiment refers to indicators extracted from multi-source operating data that have undergone validity verification, used to characterize the operating performance, energy efficiency level, and structural health status of the target passenger ropeway at the current moment.

[0022] In one embodiment of this application, an operating condition mode is matched with a physical fingerprint of the equipment. Before validating the standardized data packet based on the operating condition mode and physical fingerprint of the target passenger ropeway, and extracting feature parameters from the verified data to obtain feature data, the method further includes: The standardized data packets are parsed according to the preset encapsulation format to extract the runtime data sequence; Obtain the operating mode corresponding to the running data sequence; Based on the operating condition mode in the operating data sequence, obtain the physical fingerprint of the equipment corresponding to the operating condition mode of the target passenger ropeway.

[0023] Specifically, based on the operating mode and physical fingerprint of the target passenger ropeway, the standardized data packets are validated for validity, and feature parameters are extracted from the validated data to obtain feature data, including: Based on the physical fingerprint of the equipment, anomaly detection and data cleaning are performed on the operating data sequence to obtain the valid operating data sequence. The valid operating data includes valid current data, valid voltage data, valid power data, valid power supply frequency data, valid temperature data, valid wind speed data, and valid wind direction data. Based on the effective operating data sequence, corresponding characteristic parameters are selected from the predefined set of characteristic parameters according to the operating mode, and the values ​​of the characteristic parameters are determined based on the effective operating data sequence to form characteristic data. The predefined set of characteristic parameters is used to characterize the performance and health status of the target passenger ropeway.

[0024] In this embodiment, after receiving the standardized data packet, it first deconstructs it according to its predefined encapsulation format. From the data packet, according to predefined field mapping relationships, it extracts current data, voltage data, power data, temperature data, wind speed data, wind direction data, and the corresponding timestamps. The extracted data is then sorted and aligned according to the chronological order of the timestamps to form a structured operational data sequence containing multi-dimensional data. This sequence represents the instantaneous state set of all monitored physical quantities of the target passenger ropeway within a specific acquisition period.

[0025] The operating modes in this embodiment may include an unloaded low-speed operation mode, a heavy-load full-speed operation mode, an acceleration / deceleration transition mode, and a parking braking mode. The unloaded low-speed operation mode refers to the target passenger cableway operating at a low speed when the carriages are empty of passengers or cargo; this is often used for equipment preheating or inspection. The heavy-load full-speed operation mode refers to the target passenger cableway operating at its rated speed when the carriages are fully loaded; this is the cableway's primary high-load operating state. The acceleration / deceleration transition mode refers to the dynamic process of the target passenger cableway accelerating from a standstill to the target speed, or decelerating from high speed to a stop. The parking braking mode refers to the target passenger cableway maintaining its operational state even after it has stopped running.

[0026] This embodiment can determine the current operating mode of the target passenger ropeway based on the operating data sequence. Then, based on this operating mode, the corresponding physical fingerprint of the equipment is retrieved from a pre-built fingerprint database and loaded.

[0027] In this embodiment, the physical fingerprint of the equipment is a data feature benchmark derived from historical healthy operation data learning or physical modeling for a specific target passenger ropeway (considering its model, line characteristics, and equipment wear and tear) under specific operating conditions.

[0028] This embodiment uses the acquired physical fingerprint of the equipment as a "benchmark" to clearly filter the operational data sequence. This clear filtering includes range screening, correlation verification, and the generation of a valid data sequence. Specifically, it checks whether each data point in the operational data sequence falls within the reasonable value range of its corresponding parameter. Data exceeding the range is marked as "out-of-limit anomaly," which may originate from transient sensor malfunctions or momentary strong interference. Dynamic correlation rules are used to verify the logical consistency between data. For example, it verifies whether the relationship between power, speed, and load at the same moment violates physical laws. Data violating the rules is marked as "logical anomaly." After removing all data points marked as anomaly (or using adjacent normal data interpolation), the data that passes the double verification is retained to form a valid operational data sequence. Data in this sequence, such as valid current data, valid voltage data, valid power data, valid temperature data, valid wind speed data, and valid wind direction data, are considered reliable and trustworthy data that can be used for subsequent accurate calculations.

[0029] This embodiment is based on the cleaned and valid operating data sequence. According to the current operating mode, it activates and calculates feature data applicable to the current mode from a predefined global feature parameter set. Specifically, according to the current operating condition, relevant features are selected from the feature parameter set, and a set is formed by calculating specific values ​​based on the valid operating data sequence. This feature data is used to quantitatively characterize the instantaneous performance level and structural health status of the target passenger ropeway under specific operating conditions. For example, in the "heavy load full speed mode", "power consumption per unit mass" directly reflects the efficiency of the transmission system, and "current fluctuation index" reflects the smoothness of the drive.

[0030] This embodiment realizes the intelligent conversion from standardized data packets to high-quality feature data. The entire process is guided by the operating mode and uses the physical fingerprint of the equipment itself as the verification standard, ensuring that the extracted feature data is not only accurate and reliable, but also highly correlated with the current actual operating status of the cableway, providing input with clear physical meaning and operating condition specificity for the final safety status determination.

[0031] In one embodiment of this application, the feature data includes current fluctuation index, power, and wind load factor.

[0032] In one embodiment of this application, the numerical values ​​of characteristic parameters are determined based on a valid operational data sequence to form characteristic data, including: The average value and standard deviation of the effective current data within a preset time window are obtained, and the current fluctuation index is determined based on the ratio of the average value to the standard deviation. The current fluctuation index is used to characterize the operational stability of the target passenger ropeway. The average active power is determined based on the effective voltage data, combined with the effective current data and power factor data. The average wind speed within a preset time window is obtained from the effective wind speed data. The prevailing wind direction is determined based on the effective wind direction data. The wind load coefficient is determined based on the average wind speed and the prevailing wind direction. The wind load coefficient is used to characterize the force effect exerted by wind speed and wind direction on the target passenger ropeway.

[0033] In this embodiment, effective current data is extracted from the effective operating data sequence. This effective current data is a sequence of measured three-phase current values ​​from motors such as the main drive motor, lubricating oil pump motor, tensioning hydraulic station motor, and electro-hydraulic brake motor within a preset monitoring period, after removing anomalies caused by sensor noise or transient interference. This embodiment sets a preset time window (e.g., 60 seconds) with engineering significance. The length of this window should cover several complete cycles of cableway operation (e.g., multiple gondolas passing the drive wheels) to smooth out instantaneous fluctuations and capture stable trends. Within the preset time window, this embodiment obtains the arithmetic mean μ and standard deviation σ of the effective current data. The mean value represents the overall level of motor load during this time period; the standard deviation represents the dispersion of current values ​​around the average level, with greater fluctuations indicating a larger standard deviation. This embodiment uses the ratio of the standard deviation to the mean value as the current fluctuation index.

[0034] In this embodiment, the current fluctuation index is a dimensionless quantity that eliminates the influence of absolute current magnitude and reflects the stability of the load. The lower the index value, the smoother the current curve, the smoother the mechanical transmission of the target passenger ropeway, and the more stable its operation. An abnormally high index value may indicate problems such as mechanical jamming, wire rope slippage, increased harmonics in the electrical system, or sudden load changes.

[0035] This embodiment extracts effective voltage data, effective current data, and power factor data from the effective operating data sequence. Within a preset time window, the average active power within that time window is calculated based on the instantaneous values ​​of the effective voltage and effective current, and the power factor. This average active power characterizes the average electrical power actually consumed and converted into mechanical work by the target passenger ropeway during that preset time period, and the unit is typically kilowatts (kW).

[0036] This embodiment acquires effective wind speed and effective wind direction data sequences within a preset time window. The average wind speed within the time window is calculated based on each wind speed data point in the effective wind speed data sequence. The prevailing wind direction angle within the time window is determined by statistically analyzing the wind direction data using the effective wind direction data sequence (e.g., using a wind rose diagram or principal component analysis). The angle between the prevailing wind direction and the cableway track normal direction is taken as the relative angle. The wind load is determined based on the average wind speed and the relative angle, using the wind load calculation formula. The wind load calculation formula can be: Where F represents wind load capacity, Indicates air density, This represents the average wind speed. This represents the projected area of ​​the passenger cableway on a plane perpendicular to the wind direction. The drag coefficient, determined by the structural shape and surface roughness, is used to characterize the influence of aerodynamic properties on drag. Indicates the relative angle, This represents the wind direction influence function. This is used to characterize the reduction effect of wind direction on the effective projected area.

[0037] In this embodiment, the wind load force obtained above is normalized to obtain the wind load coefficient, which directly and comprehensively quantifies the combined force effect of the current wind speed and wind direction on the cableway structure.

[0038] This embodiment converts the effective operational data sequence into a set of characteristic data including current fluctuation index, power, wind load coefficient, etc. These data points provide accurate and physically meaningful inputs for subsequent time-series trend fusion and comprehensive safety status determination from three key dimensions: operational stability, energy efficiency, and structural dynamic response.

[0039] S103: By using time-series trend analysis methods to perform multi-dimensional fusion analysis on feature data, a fusion feature index reflecting the operating trend of the target passenger ropeway is obtained.

[0040] In one embodiment of this application, a multi-dimensional fusion analysis of feature data is performed using a time-series trend analysis method to obtain a fusion feature index reflecting the operating trend of the target passenger ropeway, including: For each feature parameter in the feature data, extract the numerical sequence of the feature parameter within a consecutive preset time window. Based on the numerical sequence, obtain the trend index of the feature parameter between adjacent time windows. The trend index includes the trend slope reflecting the direction of change and the trend fluctuation reflecting the stability of change. Based on each trend indicator and its corresponding preset trend threshold, the trend anomaly score for each feature parameter is determined. The preset trend threshold is obtained by statistical analysis of the trend of the corresponding feature parameter under the same operating conditions in the historical health data of the target passenger ropeway. Based on the pre-set weights corresponding to each trend anomaly score and each feature parameter, the fusion feature index at the current moment is obtained, which serves as the fusion feature index reflecting the operating trend of the target passenger ropeway.

[0041] In this embodiment, a historical value sequence is obtained for a series of consecutive preset time windows. These preset time windows can be 24 windows, each lasting 10 minutes, resulting in 4 hours of historical values. For each feature parameter in the feature data (e.g., the current fluctuation index), the value within that consecutive time window is extracted from the historical value sequence, forming a chronologically arranged value sequence. This value sequence characterizes the behavior trajectory of that parameter (e.g., the current fluctuation index) over a past period.

[0042] This embodiment determines the trend slope and trend volatility of the numerical sequence of each characteristic parameter. The trend slope can be obtained through linear fitting. A positive trend slope indicates that the parameter value is continuously increasing; a negative trend slope indicates that the parameter value is gradually decreasing; and a trend slope approaching zero indicates that the parameter remains stable. In this embodiment, the trend slope is used to characterize the overall direction and rate of change of the characteristic parameter value within the analysis time window, and can identify whether the parameter shows a tendency to deteriorate or improve.

[0043] In this embodiment, trend volatility reflects the stability or oscillation severity of the parameter's change process. It can be obtained by calculating the standard deviation of the numerical sequence or the variance of the rate of change between adjacent windows. In this embodiment, trend volatility is mainly used to assess the predictability and stability of state evolution. For example, if the trend slope tends to flatten, but the trend volatility is higher than a preset volatility threshold (e.g., the preset volatility threshold can be 0.15), it indicates that the parameter change is extremely unstable, fluctuating between good and bad, possibly suggesting intermittent failures or unstable operating conditions. The preset volatility threshold in this embodiment is dynamically changing and can be obtained statistically based on the historical health data of the target passenger ropeway itself.

[0044] In this embodiment, a corresponding preset trend threshold is pre-set for each feature parameter. Each preset trend threshold is not a fixed value, but a statistical range obtained by statistically analyzing the trend slope and volatility of each feature parameter over a long period of time under the same operating conditions during the target passenger ropeway's historical healthy operation (e.g., the 99% confidence interval of the trend slope under healthy conditions, or a certain percentile of the trend volatility).

[0045] This embodiment compares the calculated trend slope and trend volatility of a certain feature parameter with its corresponding preset trend threshold to determine a trend anomaly score based on the comparison result. For example, if the current trend slope significantly exceeds the upper limit of the historical healthy trend, or the trend volatility is significantly higher than the historical stable level, the parameter is determined to be trend-abnormal. This embodiment assigns a trend anomaly score (e.g., 0-10 points) to each parameter. The higher the trend anomaly score, the greater the deviation of the parameter's current trend from its historical healthy baseline, and the higher the potential risk of anomaly.

[0046] In this embodiment, each characteristic parameter is assigned a preset weight (the sum of the weights is 1) based on its importance and sensitivity to the safety of the target passenger ropeway. For example, the "wind speed tension response coefficient" may be given a higher weight due to its direct correlation with structural safety (e.g., the weight of the wind speed tension response coefficient is 0.5), the "current fluctuation index" may be given a medium weight (e.g., the weight of the current fluctuation index is 0.3), and the "unit mass power consumption" may be given a lower weight (e.g., the weight of unit mass power consumption is 0.2). This embodiment multiplies the trend anomaly score of each characteristic parameter by its corresponding preset weight, and sums the weighted scores of all parameters to obtain the final fused characteristic index.

[0047] The fusion characteristic index in this embodiment is a comprehensive scalar value that can quantitatively and comprehensively reflect the overall deviation and deterioration trend of the target passenger ropeway's multi-dimensional operating status relative to its own healthy historical baseline at the current moment. The higher the value of the fusion characteristic index, the greater the possibility that multiple key performance indicators will show adverse trends simultaneously or successively, and the higher the risk that the overall operating status of the target passenger ropeway will develop abnormally.

[0048] In one embodiment of this application, for each feature parameter, obtaining a trend index of the feature parameter's change between adjacent time windows includes: For each time window in the numerical sequence, obtain the statistical characteristic value of the feature parameter within that time window. The statistical characteristic value is the mean of the feature parameter values ​​within that time window. Based on the statistical characteristic values ​​of each time window, the instantaneous rate of change corresponding to each adjacent time window is calculated to form an instantaneous rate of change sequence; Calculate the trend slope and trend volatility based on the instantaneous rate of change sequence.

[0049] In this embodiment, for each time window (e.g., the t-th window) in the numerical sequence, the arithmetic mean of all feature parameter values ​​within that window is calculated as a statistical feature value representing the overall level of that window. For each pair of adjacent time windows (e.g., the t-th window and the (t-1)-th window), the instantaneous rate of change of the statistical feature value of the latter window relative to the former window is calculated. In this embodiment, the formula for calculating the instantaneous rate of change can be: ,in, Indicates the instantaneous rate of change. Indicates the duration of a time window. This represents the statistical characteristic value of the t-th window. This represents the statistical characteristic value of the (t-1)th window. This embodiment obtains the instantaneous rate of change sequence by traversing all adjacent time windows.

[0050] In this embodiment, the instantaneous rate of change is used to characterize the instantaneous speed and direction of change of the feature parameter between two adjacent event windows. If the instantaneous rate of change is positive, it indicates that the feature parameter is increasing; if the instantaneous rate of change is negative, it indicates that the feature parameter is decreasing. The instantaneous rate of change can capture the micro-dynamics of the rate of change of the feature parameter, and is a direct basis for analyzing the details of trend evolution.

[0051] This embodiment performs linear regression analysis on the instantaneous rate of change sequence to obtain the average value of the feature parameter over the entire preset time window, and uses this average value as the trend slope. This embodiment can use the standard deviation of the instantaneous rate of change sequence as a measure of trend volatility. This trend volatility characterizes the stability and consistency of the feature parameter's rate of change. If the trend volatility is below a preset volatility threshold (0.15), it indicates that the rate of change of the feature parameter is relatively stable; if the trend volatility is not below the preset volatility threshold, it indicates that the rate of change of the feature parameter is sometimes fast and sometimes slow, potentially indicating intermittent disturbances or state switching.

[0052] S104: Determine the comprehensive monitoring status of the target passenger ropeway based on the fusion characteristic indicators and preset safety thresholds.

[0053] In one embodiment of this application, the preset safety threshold includes a warning threshold and an alarm threshold. Based on the fusion of characteristic indicators and preset safety thresholds, the comprehensive monitoring status of the target passenger ropeway is determined, including: If the fused characteristic index is not higher than the warning threshold, the initial monitoring status of the current target passenger ropeway is determined to be normal. If the fused feature index is higher than the warning threshold but not higher than the alarm threshold, the initial monitoring status of the current target passenger ropeway is determined to be a warning status. If the fused characteristic index is higher than the alarm threshold, the initial monitoring status of the current target passenger ropeway is determined to be an alarm status; the deterioration level of normal status, early warning status and alarm status increases in sequence. Obtain the historical comprehensive monitoring status of the target passenger ropeway in the previous monitoring period; Based on the initial monitoring status and historical comprehensive monitoring status, and in conjunction with the preset state transition constraint rules, the comprehensive monitoring status of the target passenger ropeway is determined; the preset state transition constraint rules are used to limit the arbitrariness of the initial monitoring status deteriorating to a higher level.

[0054] In this embodiment, a warning threshold and an alarm threshold are set, wherein the warning threshold is lower than the alarm threshold. The warning threshold and alarm threshold in this embodiment can be obtained based on the trend statistical analysis of the historical health operation data of the target cableway. For example, the warning threshold can be 0.75, and the alarm threshold can be 0.9. In this embodiment, if the fused characteristic index is not higher than the warning threshold, the initial monitoring state of the current target passenger cableway is determined to be "normal state"; if the fused characteristic index is higher than the warning threshold but not higher than the alarm threshold, the current initial monitoring state is determined to be "warning state"; if the fused characteristic index is higher than the alarm threshold, the current initial monitoring state is determined to be "alarm state". The severity of the normal state, warning state, and alarm state increases sequentially.

[0055] This embodiment can obtain the historical comprehensive monitoring status of the target passenger ropeway at the end of the previous monitoring period. This status is used to characterize the safety status of the target passenger ropeway in the previous monitoring period.

[0056] This embodiment does not simply take the initial monitoring state as the final output of the target passenger ropeway unit, but rather combines the historical comprehensive monitoring state with the preset state transition constraint rules to determine the current comprehensive monitoring state.

[0057] In this embodiment, the preset state transition constraint rules include state degradation rules and state escalation rules. Specifically, if the degradation level of the initial monitoring state is lower than that of the historical comprehensive monitoring state, it is updated according to the state degradation rule. If the degradation level of the initial monitoring state is equal to that of the historical comprehensive monitoring state, the initial monitoring state is directly updated to the comprehensive monitoring state. If the degradation level of the initial monitoring state is higher than that of the historical comprehensive monitoring state, it is updated according to the state escalation rule.

[0058] In one embodiment of this application, the preset state transition rules include state degradation rules and state upgrade rules. Based on the initial monitoring state and historical comprehensive monitoring states, and in conjunction with the preset state transition constraint rules, the comprehensive monitoring state of the target passenger ropeway is determined, including: If the initial monitoring status deterioration level is lower than the historical comprehensive monitoring status deterioration level, the comprehensive monitoring status is updated according to the status deterioration rules. If the deterioration level of the initial monitoring status is equal to the deterioration level of the historical comprehensive monitoring status, then the initial monitoring status will be updated to the comprehensive monitoring status. If the initial monitoring status deterioration level is higher than the historical comprehensive monitoring status deterioration level, the comprehensive monitoring status will be updated according to the status escalation rules. The state degradation rules include: Determine whether the fused feature indicators are all below the threshold corresponding to the historical comprehensive monitoring status within a consecutive preset monitoring period; if they are, update the initial monitoring status to the comprehensive monitoring status; if they are not, update the historical comprehensive monitoring status to the comprehensive monitoring status; if the historical comprehensive monitoring status is an alarm status, the corresponding threshold is the alarm threshold; if the historical comprehensive monitoring status is an early warning status, the corresponding threshold is the early warning threshold. The status escalation rules include: If the initial monitoring status is an early warning status, it is determined whether the fused feature indicators exceed the early warning threshold within a consecutive preset number of monitoring periods; if they do, the comprehensive monitoring status is updated to an early warning status; if they do not, the historical comprehensive monitoring status is used as the comprehensive monitoring status. If the initial monitoring status is alarm status, the overall monitoring status will be updated to alarm status.

[0059] In this embodiment, if the initial monitoring status deterioration level is lower than the historical comprehensive monitoring status, it indicates that the status corresponding to the current data may be improving, and the status is updated according to the status degradation rules. At this time, the historical comprehensive monitoring status serves as a reference line for determining whether the improvement is sustainable. If the historical status was an alarm status, the reference threshold corresponds to the alarm threshold; if the historical status was a warning status, the reference threshold corresponds to the warning threshold. This reference threshold is the risk threshold corresponding to the historical status. If the current initial monitoring status remains below the risk threshold, it is determined that the risk has fallen below that level, indicating that the current status improvement is stable and credible, and the overall monitoring status is updated to the better initial monitoring status (i.e., the status downgrade is approved).

[0060] If the sustainability condition is not met (for example, the current initial monitoring status remains below the risk threshold), it is determined that the current improvement may only be a temporary fluctuation, and the original risk has not been firmly eliminated. The historical comprehensive monitoring status will be used as the current comprehensive monitoring status.

[0061] If the deterioration level of the initial monitoring status is equal to the deterioration level of the historical comprehensive monitoring status, it indicates that the trend has not changed directionally, and the initial monitoring status is directly updated to the comprehensive monitoring status.

[0062] If the initial monitoring status deteriorates at a higher level than the historical comprehensive monitoring status, it indicates that the current data's corresponding status may worsen, and the initial monitoring status needs to be updated according to the status escalation rules. For example, if the initial monitoring status is a warning status and the historical comprehensive monitoring status is a normal status, and the current fusion characteristic indicator exceeds the warning threshold for the first time but does not exceed the alarm threshold, then it is determined whether the fusion characteristic indicator has exceeded the warning threshold in the most recent consecutive preset monitoring periods, including the current period: if the fusion characteristic indicator has exceeded the warning threshold, then the risk trend is confirmed, and the comprehensive monitoring status is updated to a warning status; if the fusion characteristic indicator has not exceeded the warning threshold, then it is considered an occasional fluctuation, and the historical comprehensive monitoring status is maintained as the current comprehensive monitoring status.

[0063] If the initial monitoring status is an alarm status, meaning that the current fused feature index has exceeded the alarm threshold, the overall monitoring status will be immediately updated to an alarm status without waiting for continuous confirmation.

[0064] As can be seen from the above, the embodiments of this application acquire multi-source operational data of the target passenger ropeway, including ropeway operational data (current, voltage, power, and other data) and environmental data of the area where the target passenger ropeway is located (temperature, wind speed, and wind direction data), and encapsulate them into standardized data packets. Subsequently, the multi-source data is validated, feature parameters are extracted, and multi-dimensional fusion analysis is performed. By comprehensively utilizing various data information, it is possible to acquire multi-source operational data and perform multi-dimensional fusion analysis, avoiding the shortcomings of a single sensor monitoring which can only obtain local isolated parameter information, and more comprehensively and accurately reflecting the overall operational status of the ropeway.

[0065] This application embodiment verifies the validity of standardized data packets based on the physical fingerprint of the equipment (the reasonable value range of each operating parameter and dynamic association rules), extracts feature parameters from the verified data, and then uses multi-dimensional analysis such as time series trend analysis to comprehensively consider multiple parameters and their correlations to determine the cableway status. By comprehensively judging the safety status through validity verification, feature parameter extraction, and time series trend analysis, compared with judging the safety status based solely on whether a single parameter exceeds the limit, the possibility of misjudgment and omission is reduced.

[0066] This application embodiment uses a time-series trend analysis method to perform multi-dimensional fusion analysis on feature data to obtain fusion feature indicators that reflect the operating trend of the target passenger ropeway. This can identify potential safety risks in advance and take preventive measures in a timely manner, and has the ability to analyze and predict operating trends.

[0067] Based on the same principle as the intelligent safety monitoring method for passenger ropeways provided in the embodiments of this application, the embodiments of this application also provide an intelligent safety monitoring device for passenger ropeways, such as... Figure 2As shown, the intelligent safety monitoring device 20 for passenger ropeways may specifically include: a data acquisition module 21, a feature data acquisition module 22, a fusion feature index acquisition module 23, and a comprehensive monitoring module 24.

[0068] The data acquisition module 21 acquires multi-source operation data of the target passenger cableway and encapsulates the multi-source operation data to form a standardized data package. The multi-source operation data includes cableway operation data and environmental data of the area where the target passenger cableway is located. The cableway operation data includes current data, power supply voltage data, operating power, power supply frequency data, shell temperature data of each motor, and real-time video display data of the target passenger cableway. The environmental data of the area where the target passenger cableway is located includes wind speed data and wind direction data. The feature data acquisition module 22 is used to verify the validity of the standardized data packet based on the operating mode and physical fingerprint of the target passenger ropeway, and extract feature parameters from the verified data to obtain feature data; the physical fingerprint of the equipment includes the reasonable value range of each operating parameter and dynamic association rules. The fusion feature index acquisition module 23 is used to perform multi-dimensional fusion analysis on feature data through time series trend analysis methods to obtain fusion feature indicators that reflect the operating trend of the target passenger ropeway. The integrated monitoring module 24 is used to determine the integrated monitoring status of the target passenger ropeway based on the fusion characteristic indicators and preset safety thresholds.

[0069] In one embodiment of this application, a working condition mode is matched with a device physical fingerprint, and the feature data acquisition module 22 is specifically used for: The standardized data packets are parsed according to the preset encapsulation format to extract the runtime data sequence; Obtain the operating mode corresponding to the running data sequence; Based on the operating condition mode in the operating data sequence, obtain the physical fingerprint of the equipment corresponding to the operating condition mode of the target passenger ropeway.

[0070] In one embodiment of this application, when verifying the validity of standardized data packets based on the operating mode and physical fingerprint of the target passenger ropeway, and extracting feature parameters from the verified data to obtain feature data, the feature data acquisition module 22 is specifically used for: Based on the physical fingerprint of the equipment, anomaly detection and data cleaning are performed on the operating data sequence to obtain the valid operating data sequence. The valid operating data includes valid current data, valid voltage data, valid wind speed data, valid power data, valid power supply frequency data, valid temperature data, valid wind speed data, and valid wind direction data. Based on the effective operating data sequence, corresponding characteristic parameters are selected from the predefined set of characteristic parameters according to the operating mode, and the values ​​of the characteristic parameters are determined based on the effective operating data sequence to form characteristic data. The predefined set of characteristic parameters is used to characterize the performance and health status of the target passenger ropeway.

[0071] In one embodiment of this application, the feature data includes current fluctuation index, power, and wind load factor; When determining the values ​​of feature parameters based on valid operational data sequences to form feature data, the feature data acquisition module 22 is specifically used for: The average value and standard deviation of the effective current data within a preset time window are obtained, and the current fluctuation index is determined based on the ratio of the average value to the standard deviation. The current fluctuation index is used to characterize the operational stability of the target passenger ropeway. The average active power is determined based on the effective voltage data, combined with the effective current data and power factor data. The average wind speed within a preset time window is obtained from the effective wind speed data. The prevailing wind direction is determined based on the effective wind direction data. The wind load coefficient is determined based on the average wind speed and the prevailing wind direction. The wind load coefficient is used to characterize the force effect exerted by wind speed and wind direction on the target passenger ropeway.

[0072] In one embodiment of this application, when performing multi-dimensional fusion analysis on feature data using a time-series trend analysis method to obtain fused feature indicators reflecting the operating trend of the target passenger ropeway, the fusion feature indicator acquisition module 23 is specifically used for: For each feature parameter in the feature data, extract the numerical sequence of the feature parameter within a consecutive preset time window. Based on the numerical sequence, obtain the trend index of the feature parameter between adjacent time windows. The trend index includes the trend slope reflecting the direction of change and the trend fluctuation reflecting the stability of change. Based on each trend indicator and its corresponding preset trend threshold, the trend anomaly score for each feature parameter is determined. The preset trend threshold is obtained by statistical analysis of the trend of the corresponding feature parameter under the same operating conditions in the historical health data of the target passenger ropeway. Based on the pre-set weights corresponding to each trend anomaly score and each feature parameter, the fusion feature index at the current moment is obtained, which serves as the fusion feature index reflecting the operating trend of the target passenger ropeway.

[0073] In one embodiment of this application, for each feature parameter, when obtaining the trend index of the feature parameter's change between adjacent time windows, the feature index acquisition module 23 is specifically used for: For each time window in the numerical sequence, obtain the statistical characteristic value of the feature parameter within that time window. The statistical characteristic value is the mean of the feature parameter values ​​within that time window. Based on the statistical characteristic values ​​of each time window, the instantaneous rate of change corresponding to each adjacent time window is calculated to form an instantaneous rate of change sequence; Calculate the trend slope and trend volatility based on the instantaneous rate of change sequence.

[0074] In one embodiment of this application, the preset safety threshold includes a warning threshold and an alarm threshold. When determining the comprehensive monitoring status of the target passenger ropeway based on the fusion characteristic indicators and preset safety thresholds, the comprehensive monitoring module 24 is specifically used for: If the fused characteristic index is not higher than the warning threshold, the initial monitoring status of the current target passenger ropeway is determined to be normal. If the fused feature index is higher than the warning threshold but not higher than the alarm threshold, the initial monitoring status of the current target passenger ropeway is determined to be a warning status. If the fused characteristic index is higher than the alarm threshold, the initial monitoring status of the current target passenger ropeway is determined to be an alarm status; the deterioration level of normal status, early warning status and alarm status increases in sequence. Obtain the historical comprehensive monitoring status of the target passenger ropeway in the previous monitoring period; Based on the initial monitoring status and historical comprehensive monitoring status, and in conjunction with the preset state transition constraint rules, the comprehensive monitoring status of the target passenger ropeway is determined; the preset state transition constraint rules are used to limit the arbitrariness of the initial monitoring status deteriorating to a higher level.

[0075] In one embodiment of this application, the preset state transition rules include state degradation rules and state upgrade rules. When determining the comprehensive monitoring status of the target passenger ropeway based on the initial monitoring status and historical comprehensive monitoring status, and in conjunction with preset state transition constraint rules, the comprehensive monitoring module 24 is specifically used for: If the initial monitoring status deterioration level is lower than the historical comprehensive monitoring status deterioration level, the comprehensive monitoring status is updated according to the status deterioration rules. If the deterioration level of the initial monitoring status is equal to the deterioration level of the historical comprehensive monitoring status, then the initial monitoring status will be updated to the comprehensive monitoring status. If the initial monitoring status deterioration level is higher than the historical comprehensive monitoring status deterioration level, the comprehensive monitoring status will be updated according to the status escalation rules. The state degradation rules include: Determine whether the fused feature indicators are all below the threshold corresponding to the historical comprehensive monitoring status within a consecutive preset monitoring period; if they are, update the initial monitoring status to the comprehensive monitoring status; if they are not, update the historical comprehensive monitoring status to the comprehensive monitoring status; if the historical comprehensive monitoring status is an alarm status, the corresponding threshold is the alarm threshold; if the historical comprehensive monitoring status is an early warning status, the corresponding threshold is the early warning threshold. The status escalation rules include: If the initial monitoring status is an early warning status, it is determined whether the fused feature indicators exceed the early warning threshold within a consecutive preset number of monitoring periods; if they do, the comprehensive monitoring status is updated to an early warning status; if they do not, the historical comprehensive monitoring status is used as the comprehensive monitoring status. If the initial monitoring status is alarm status, the overall monitoring status will be updated to alarm status.

[0076] The apparatus in this application embodiment can execute the method provided in this application embodiment, and the implementation principle is similar. The actions performed by each module in the apparatus of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0077] Figure 3 A schematic diagram of the structure of an electronic device to which this application embodiment applies is shown, such as... Figure 3 As shown, the electronic device can be used to implement the methods provided in any embodiment of this application.

[0078] like Figure 3 As shown, the electronic device 300 may primarily include at least one processor 301. Figure 3 The diagram shows components such as a memory 302, a communication module 303, and an input / output interface 304. Optionally, these components can be connected and communicate with each other via a bus 305. It should be noted that... Figure 3 The structure of the electronic device 300 shown is merely illustrative and does not constitute a limitation on the electronic devices to which the methods provided in the embodiments of this application are applicable.

[0079] The memory 302 can be used to store operating systems and applications, etc. The applications can include computer programs that implement the methods shown in the embodiments of this application when invoked by the processor 301, and can also include programs for implementing other functions or services. The memory 302 can be ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices that can store information and computer programs, or it can be EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0080] Processor 301 is connected to memory 302 via bus 305 and implements corresponding functions by calling the application programs stored in memory 302. Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0081] Electronic device 300 can connect to a network via communication module 303 (which may include, but is not limited to, components such as a network interface) to communicate with other devices (such as user terminals or servers) through the network and achieve data interaction, such as sending data to or receiving data from other devices. Communication module 303 may include wired network interfaces and / or wireless network interfaces, meaning the communication module may include at least one of wired or wireless communication modules.

[0082] The electronic device 300 can connect to necessary input / output devices, such as a keyboard and display device, via the input / output interface 304. The electronic device 300 itself may have a display device, and other display devices can also be connected externally via the interface 304. Optionally, a storage device, such as a hard drive, can also be connected via the interface 304 to store data from the electronic device 300, retrieve data from the storage device, or store data from the storage device in the memory 302. It is understood that the input / output interface 304 can be a wired interface or a wireless interface. Depending on the actual application scenario, the device connected to the input / output interface 304 can be a component of the electronic device 300 or an external device connected to the electronic device 300 when needed.

[0083] The bus 305 used to connect the components may include a path for transmitting information between the components. The bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Depending on its function, the bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0084] Optionally, for the solution provided in the embodiments of this application, the memory 302 can be used to store a computer program that executes the solution of this application, and the processor 301 runs the computer program. When the processor 301 runs the computer program, it implements the operation of the method or apparatus provided in the embodiments of this application.

[0085] It should be noted that the terms "first," "second," "third," "fourth," "1," "2," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text.

[0086] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0087] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart, based on the actual implementation scenario, may include multiple sub-steps or multiple stages. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0088] The above description is only an optional implementation method for some implementation scenarios of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application without departing from the technical concept of this application also fall within the protection scope of the embodiments of this application.

Claims

1. A method for intelligent safety monitoring of passenger ropeways, characterized in that, include: Acquire multi-source operation data of the target passenger ropeway and encapsulate the multi-source operation data to form a standardized data packet; The multi-source operation data includes cableway operation data and environmental data of the area where the target passenger cableway is located. The cableway operation data includes current data, power supply voltage data, operating power, power supply frequency data, shell temperature data of each motor, and real-time video display data of the target passenger cableway. The environmental data of the area where the target passenger cableway is located includes wind speed data and wind direction data. Based on the operating mode and physical fingerprint of the target passenger ropeway, the standardized data packet is validated for validity, and feature parameters are extracted from the validated data to obtain feature data; the physical fingerprint of the equipment includes the reasonable value range of each operating parameter and dynamic association rules. The feature data is analyzed in multiple dimensions using time-series trend analysis methods to obtain fused feature indicators that reflect the operating trend of the target passenger ropeway. The comprehensive monitoring status of the target passenger ropeway is determined based on the fusion feature indicators and the preset safety threshold.

2. The intelligent safety monitoring method for passenger ropeways as described in claim 1, characterized in that, The method of matching a working condition mode with a device physical fingerprint further includes: The standardized data packets are parsed according to a preset encapsulation format to extract the running data sequence; Obtain the operating mode corresponding to the running data sequence; Based on the operating condition mode in the operating data sequence, obtain the physical fingerprint of the equipment corresponding to the operating condition mode of the target passenger ropeway.

3. The intelligent safety monitoring method for passenger ropeways as described in claim 2, characterized in that, The standardized data packet is validated based on the operating condition mode and physical fingerprint of the target passenger ropeway, and feature parameters are extracted from the validated data to obtain feature data, including: Based on the physical fingerprint of the device, anomaly detection and data cleaning are performed on the operating data sequence to obtain a valid operating data sequence. The valid operating data includes valid current data, valid voltage data, valid power data, valid power supply frequency data, valid temperature data, valid wind speed data, and valid wind direction data. Based on the effective operating data sequence, and according to the operating mode, corresponding feature parameters are selected from a predefined set of feature parameters, and the values ​​of the feature parameters are determined based on the effective operating data sequence to form the feature data. The predefined set of feature parameters is used to characterize the performance and health status of the target passenger ropeway.

4. The intelligent safety monitoring method for passenger ropeways as described in claim 3, characterized in that, The characteristic data includes current fluctuation index, power, and wind load coefficient; The step of determining the value of the feature parameter based on the effective running data sequence to form the feature data includes: The average value and standard deviation of the effective current data within a preset time window are obtained, and the current fluctuation index is determined based on the ratio of the average value to the standard deviation. The current fluctuation index is used to characterize the operational stability of the target passenger ropeway. Based on the effective voltage data, and in combination with the effective current data and the power factor data, the average active power is determined; The average wind speed of the effective wind speed data within a preset time window is obtained, the dominant wind direction is determined based on the effective wind direction data, and the wind load coefficient is determined based on the average wind speed and the dominant wind direction. The wind load coefficient is used to characterize the force effect exerted by wind speed and wind direction on the target passenger ropeway.

5. The intelligent safety monitoring method for passenger ropeways as described in claim 1, characterized in that, The method of performing multi-dimensional fusion analysis on the feature data using time-series trend analysis to obtain fused feature indicators reflecting the operational trend of the target passenger ropeway includes: For each feature parameter in the feature data, extract the numerical sequence of the feature parameter within a consecutive preset number of time windows, and obtain the trend index of the feature parameter between adjacent time windows based on the numerical sequence. The trend index includes the trend slope reflecting the direction of change and the trend fluctuation reflecting the stability of change. Based on each of the aforementioned trend indicators and their respective preset trend thresholds, the trend anomaly score corresponding to each feature parameter is determined; the preset trend thresholds are obtained by statistical analysis of the trends of the corresponding feature parameters under the same operating conditions in the historical health data of the target passenger ropeway. Based on the trend anomaly scores and the preset weights corresponding to each feature parameter, the fusion feature index at the current moment is obtained, which serves as the fusion feature index reflecting the operating trend of the target passenger ropeway.

6. The intelligent safety monitoring method for passenger ropeways as described in claim 5, characterized in that, For each feature parameter, obtaining the trend index of the feature parameter's change between adjacent time windows includes: For each time window in the numerical sequence, the statistical characteristic value of the feature parameter within that time window is obtained, and the statistical characteristic value is the mean of the feature parameter values ​​within that time window. Based on the statistical characteristic values ​​of each time window, the instantaneous rate of change corresponding to each adjacent time window is calculated to form an instantaneous rate of change sequence; The trend slope and the trend volatility are calculated based on the instantaneous rate of change sequence.

7. The intelligent safety monitoring method for passenger ropeways as described in claim 6, characterized in that, The preset safety thresholds include a warning threshold and an alarm threshold. The step of determining the comprehensive monitoring status of the target passenger ropeway based on the fused feature indicators and the preset safety threshold includes: If the fusion feature index is not higher than the warning threshold, then the initial monitoring status of the target passenger ropeway is determined to be normal. If the fusion feature index is higher than the warning threshold but not higher than the alarm threshold, then the initial monitoring status of the target passenger ropeway is determined to be a warning status. If the fused feature index is higher than the alarm threshold, the initial monitoring state of the target passenger ropeway is determined to be an alarm state; the deterioration level of the normal state, the early warning state, and the alarm state increases sequentially. Obtain the historical comprehensive monitoring status of the target passenger ropeway in the previous monitoring period; Based on the initial monitoring status and the historical comprehensive monitoring status, and in conjunction with the preset state transition constraint rules, the comprehensive monitoring status of the target passenger ropeway is determined; the preset state transition constraint rules are used to limit the arbitrariness of the initial monitoring status deteriorating to a higher level.

8. The intelligent safety monitoring method for passenger ropeways as described in claim 7, characterized in that, The preset state transition rules include state degradation rules and state upgrade rules. The step of determining the comprehensive monitoring status of the target passenger ropeway based on the initial monitoring status and the historical comprehensive monitoring status, combined with the preset state transition constraint rules, includes: If the deterioration level of the initial monitoring status is lower than the deterioration level of the historical comprehensive monitoring status, the comprehensive monitoring status is updated according to the status degradation rules. If the deterioration level of the initial monitoring status is equal to the deterioration level of the historical comprehensive monitoring status, then the initial monitoring status is updated to the comprehensive monitoring status. If the deterioration level of the initial monitoring status is higher than the deterioration level of the historical comprehensive monitoring status, the comprehensive monitoring status is updated according to the status escalation rules. The state degradation rules include: Determine whether the fusion feature index meets the condition of being lower than the threshold corresponding to the historical comprehensive monitoring state within a consecutive preset number of monitoring periods; if it meets the condition, update the initial monitoring state to the comprehensive monitoring state; if it does not meet the condition, update the historical comprehensive monitoring state to the comprehensive monitoring state; wherein, if the historical comprehensive monitoring state is an alarm state, the corresponding threshold is an alarm threshold, and if the historical comprehensive monitoring state is an early warning state, the corresponding threshold is an early warning threshold. The status upgrade rules include: If the initial monitoring state is an early warning state, then it is determined whether the fusion feature indicators exceed the early warning threshold within a consecutive preset number of monitoring periods; if they do, the comprehensive monitoring state is updated to an early warning state; if they do not, the historical comprehensive monitoring state is used as the comprehensive monitoring state. If the initial monitoring status is an alarm status, then the overall monitoring status is updated to an alarm status.

9. A smart safety monitoring device for passenger ropeways, characterized in that, include: The data acquisition module acquires multi-source operation data of the target passenger ropeway and encapsulates the multi-source operation data to form a standardized data packet; The multi-source operation data includes cableway operation data and environmental data of the area where the target passenger cableway is located. The cableway operation data includes current data, power supply voltage data, operating power, power supply frequency data, shell temperature data of each motor, and real-time video display data of the target passenger cableway. The environmental data of the area where the target passenger cableway is located includes wind speed data and wind direction data. The feature data acquisition module is used to verify the validity of the standardized data packet based on the operating mode and physical fingerprint of the target passenger ropeway, and extract feature parameters from the verified data to obtain feature data; the physical fingerprint of the equipment includes the reasonable value range of each operating parameter and dynamic association rules. The fusion feature index acquisition module is used to perform multi-dimensional fusion analysis on the feature data through time series trend analysis methods to obtain fusion feature indicators that reflect the operating trend of the target passenger ropeway. The integrated monitoring module is used to determine the integrated monitoring status of the target passenger ropeway based on the fused feature indicators and the preset safety threshold.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the intelligent safety monitoring method for passenger ropeways according to any one of claims 1 to 8 when running the computer program.

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