Cableway transportation operation pre-permission and loading optimization method and system based on digital twinning and real-time simulation, medium and processor

Abstract

The invention discloses a cableway transportation operation pre-permission and loading optimization method and system based on digital twinning and real-time simulation, a medium and a processor, and belongs to the technical field of cableway transportation safety and intelligence. The method comprises the following steps: constructing a cableway digital twinborn model and setting a safety threshold value; performing digital modeling and rule presetting on the materials and the transportation carriers; generating an initial loading scheme according to the task and loading the initial loading scheme into the model for full-dynamic transportation simulation; performing safety assessment based on the time sequence data output by simulation, and generating an electronic homework pre-license if the time sequence data passes the safety assessment; and if not, triggering intelligent loading optimization, and finally outputting a safe and feasible optimization scheme and a corresponding license through iterative optimization of a multi-objective optimization model. According to the method, high-precision safety pre-evaluation and automatic optimization of the loading scheme before cableway transportation operation are realized, and the operation safety, the loading efficiency and the management digitization level are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of cableway transportation technology, and in particular to a method, system, medium, and processor for cableway transportation operation pre-permission and loading optimization based on digital twins and real-time simulation. Background Technology

[0002] Cableway transportation, a specialized freight transport method suitable for complex terrains (such as mountains, valleys, and hills), is widely used in power construction, mining, scenic area material transport, and forestry transportation. It uses an aerial cable system to carry transport vehicles (such as gondolas or cargo containers) along a predetermined route, effectively overcoming the limitations of inconvenient ground transportation and achieving efficient point-to-point transport. However, cableway operations also present significant safety risks and management challenges. First, the safety risks are high. Cableway systems are constantly exposed to variable outdoor environments, significantly affected by natural factors such as wind loads, temperature changes, and snow accumulation. During transport, improper material loading (e.g., center of gravity shift, overloading, insecure securing) or extreme weather conditions can easily lead to dangerous situations such as significant carrier swaying, excessive stress on rigging, and imbalance of support structures, potentially causing serious accidents like detachment or falling loads. Currently, pre-operation safety assessments rely heavily on manual experience and static calculations, making it difficult to comprehensively and dynamically simulate the multi-physics coupling effects and random environmental disturbances in actual transport, thus hindering accurate early warning and quantitative assessment of potential risks.

[0003] Secondly, loading schemes rely on experience and lack scientific optimization. Current loading operations typically involve operators arranging the order and location of materials based on experience, lacking unified and standardized digital guidance. For complex shapes and heterogeneous material combinations, it is difficult for humans to quickly provide optimized solutions that satisfy safety constraints while also considering space utilization and transportation efficiency. Especially when dealing with new materials or special transportation tasks, the lack of historical experience makes improper loading prone to occur, leading to multiple trial loadings and rework, impacting operational efficiency and economy.

[0004] Furthermore, the work permit process is traditional and lacks a digital closed loop. Currently, the permit management for cableway transportation operations is mostly based on paper approvals or simple electronic records, which are disconnected from actual loading plans, simulation verification, and risk control. It is impossible to conduct dynamic safety verification of specific loading plans before operations and generate electronic pre-licenses with data support, and it is also difficult to automatically trigger optimization processes when plans fail, resulting in low management efficiency and poor traceability.

[0005] Therefore, existing technologies lack an integrated method and system capable of performing high-precision, dynamic simulation and safety assessment of the entire cableway transportation process before operation, and automatically optimizing loading plans and generating digital work permits based on the assessment results. This makes it difficult to systematically improve the safety, efficiency, and management level of cableway transportation operations, and fails to meet the urgent needs of modern engineering for intelligent and refined operations.

[0006] Therefore, there is a need for a method, system, medium, and processor for cableway transportation operation pre-permission and loading optimization based on digital twins and real-time simulation to solve the above problems. Summary of the Invention

[0007] To address the limitations of existing cableway transportation operations in terms of safety, efficiency, and management level, which fail to meet the urgent needs of modern engineering for intelligent and refined operations, this invention provides a method, system, medium, and processor for cableway transportation operation pre-approval and loading optimization based on digital twins and real-time simulation. This method systematically improves the safety, efficiency, and management level of cableway transportation operations, thus meeting the urgent needs of modern engineering for intelligent and refined operations. The specific technical solution is as follows: A method for pre-permissioning and loading optimization of cableway transportation operations based on digital twins and real-time simulation includes: S1: Collect multi-source data for the cableway scenario, construct a digital twin model of the cableway based on the collected data, and set thresholds for each monitoring point; S2: Digitally model and standardize the input of materials and transport carriers, and pre-set the placement rules and constraints of materials within the carriers; S3: Obtain transportation task information to determine the initial loading plan, arrange the material model on the transportation carrier model according to the initial loading plan, and then load the entire cableway digital twin model for simulation configuration. S4: Perform full dynamic transportation process simulation and output time-series data of physical quantities at each monitoring point; S5: Conduct a safety assessment of the time-series data of physical quantities at each monitoring point. If the safety assessment is passed, an electronic work pre-license will be generated; otherwise, proceed to the next step of loading optimization. S6: Perform intelligent loading optimization for unlicensed device schemes.

[0008] Furthermore, in step S1, the process of collecting multi-source data on the cableway scene, constructing a digital twin model of the cableway based on the collected data, and setting thresholds for each monitoring point includes the following steps: S11: Comprehensive collection of geometric, structural, and environmental data of the cableway system; S12: Based on the collected multi-source data, establish a cableway digital twin model with multi-physics coupling capability in the simulation platform; S13: Arrange monitoring points in the cableway digital twin model and set safety thresholds for each monitoring point.

[0009] Furthermore, in step S3, the process of obtaining transportation task information to determine the initial loading plan, arranging the material model on the transportation carrier model according to the initial loading plan, and then loading the entire cableway digital twin model for simulation configuration includes the following steps: S31: Obtain and parse transportation task information; S32: Based on the task information, call the loading scheme of similar historical tasks as the initial loading scheme. If there are no similar historical tasks, calculate the material placement position based on the preset placement rules and constraints as the initial loading scheme. S33: Place the three-dimensional models of the materials one by one into the corresponding positions of the transport carrier model according to the initial loading plan; S34: Import the completed "transport carrier-material" overall model into the cableway digital twin model and configure the simulation parameters.

[0010] Furthermore, in step S4, the process of performing a full-dynamic transportation process simulation and outputting time-series data of physical quantities at each monitoring point includes the following steps: S41: Based on the configured simulation environment, start the multiphysics coupling simulation engine to simulate the complete operation process of the transport vehicle from the starting point to the destination; S42: During the simulation, the system dynamically loads wind load model, temperature field model and ice and snow load model according to the input environmental time series data, which affects the force and motion state of the cableway system and the transport vehicle in real time. S43: Record the physical quantity data of each preset monitoring point at each time step and form a physical quantity time series dataset; S44: Dynamically displays the operating status of the transport vehicle, structural deformation, stress cloud diagram, and risk warning information; S45: Summarize all time-series data and generate a structured simulation report.

[0011] Furthermore, in step S5, a security assessment is performed on the time-series data of physical quantities at each monitoring point. If the security assessment is passed, an electronic work pre-license is generated; otherwise, the process proceeds to the next step of loading optimization, which includes the following steps: S51: Compare the time-series data of physical quantities at each monitoring point with their corresponding safety thresholds point by point to identify whether there are any time periods, locations, or magnitudes of exceeding limits. S52: Conduct risk quantification and rating of identified out-of-limit events, classifying them according to the extent of the out-of-limit, duration, and scope of impact; S53: Conduct a comprehensive security assessment based on the number, level, and distribution of out-of-limit events; S54: If the evaluation result is "pass" or "conditionally pass", the system generates an electronic work pre-license with operational suggestions; if the evaluation result is "fail", the system automatically rejects the current loading plan and triggers the next loading optimization process.

[0012] Furthermore, in step S6, the intelligent loading optimization of the unlicensed device scheme includes the following steps: S61: Based on the safety assessment results, identify the key risk factors that lead to failure, and construct a multi-objective optimization model accordingly; S62: Use the placement parameters of materials within the carrier as design variables and define constraints; S63: Adaptive optimization algorithm is used to automatically search the design variable space and obtain the first batch of candidate solutions under the condition of satisfying the constraints. S64: Load the first batch of candidate schemes into the cableway digital twin model, re-execute the full dynamic transportation process simulation and safety assessment, and use the schemes that pass the safety assessment as the second batch of candidate schemes; S65: Establish a comprehensive evaluation system that includes safety, efficiency, and economy, score the second batch of candidate solutions, recommend the best comprehensive candidate solution as the loading solution, and issue pre-licenses to the second batch of candidate solutions.

[0013] Furthermore, in step S61, the objective function of the multi-objective optimization model is as follows: ; ; ; ; ; in, Let x be the physical quantity of monitoring point j at time t under the placement scheme x; Let x be the standard deviation of the physical quantity at monitoring point j. Let x be the effective volume occupied by the material inside the carrier. The safety threshold for monitoring point j; For all monitoring points; A subset of key monitoring points; The effective loading volume of the carrier; .

[0014] A cableway transportation operation pre-permission and loading optimization system based on digital twins and real-time simulation, applied to the aforementioned cableway transportation operation pre-permission and loading optimization method based on digital twins and real-time simulation, includes: The twin module is used to collect multi-source data on the cableway scene, build a digital twin model of the cableway based on the collected data, and set thresholds for each monitoring point. The data entry module is used for digital modeling and standardized data entry of materials and transport carriers, and pre-sets the rules and constraints for the placement of materials within the carrier. The initial module is used to acquire transportation task information to determine the initial loading plan, arrange the material model on the transportation carrier model according to the initial loading plan, and then load the entire cableway digital twin model for simulation configuration. The simulation module is used to perform full-dynamic transportation process simulation and outputs time-series data of physical quantities at each monitoring point. The assessment module is used to perform a safety assessment on the time-series data of physical quantities at each monitoring point. If the safety assessment is passed, an electronic work pre-license is generated; otherwise, the process proceeds to the next step of loading optimization. The optimization module is used to intelligently optimize the loading of unlicensed device schemes.

[0015] A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the above-described method for pre-approval and loading optimization of cableway transportation operations based on digital twins and real-time simulation.

[0016] A processor for running a program, wherein the program executes the above-described method for pre-permissioning and loading optimization of cableway transportation operations based on digital twins and real-time simulation.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Significantly improves operational safety and risk prevention and control capabilities.

[0018] Traditional methods struggle to accurately predict the behavior of cableway systems under the coupled effects of complex environments and dynamic loads. This invention constructs a high-fidelity, multi-physics coupled digital twin model of the cableway and performs full-dynamic transportation process simulations of specific loading schemes before operation. This allows for the advance and quantitative simulation of the temporal changes in physical quantities at key monitoring points (such as support stress, rigging tension, and carrier sway) throughout the transportation process. By automatically comparing simulation data with safety thresholds and conducting risk assessments, the system can accurately identify potential over-limit events, pinpoint the root causes of risks, and make safety predictions based on quantitative ratings. This represents a fundamental shift from "post-event remediation" to "pre-event prevention," mitigating safety accidents caused by improper loading or sudden environmental changes at the source and providing an unprecedented proactive safety barrier for cableway transportation.

[0019] 2. To achieve intelligent, scientific, and optimized loading solutions.

[0020] Traditional loading methods heavily rely on operator experience and lack efficient and standardized optimization techniques. This invention combines digital modeling of materials and carriers, pre-defined placement rules, and intelligent optimization algorithms. The system can automatically recall historical similar solutions or generate initial solutions based on rules, and automatically trigger the loading optimization process when a solution fails the safety assessment. By constructing a multi-objective optimization model centered on safety, stability, and efficiency, and employing an adaptive optimization algorithm for spatial search, the system can quickly generate multiple candidate loading solutions that meet safety constraints and offer superior overall performance. This effectively solves the challenge of loading complex and irregularly shaped materials, reduces reliance on operator experience, maximizes space utilization while ensuring safety, reduces transport trips, and improves the scientific and economical nature of loading operations.

[0021] 3. Build a new paradigm for digitalized, closed-loop pre-approval and management of operations.

[0022] Traditional work permit processes and management are relatively isolated and static. This invention pioneers an electronic work pre-license generation mechanism tightly integrated with dynamic simulation verification and intelligent optimization. Only loading schemes that pass rigorous safety assessments (or optimizations) will automatically generate an electronic pre-license with a digital signature and specific operational recommendations. This forms a digital management closed loop of "simulation assessment - scheme optimization - license issuance," providing robust data support and traceability for work permits. Simultaneously, the structured data generated by the system, such as simulation reports, optimization comparison tables, and 3D diagrams, greatly facilitates work training, scheme archiving, safety audits, and continuous improvement, significantly enhancing management efficiency and the scientific and transparent nature of decision-making.

[0023] 4. Enhance adaptability to complex environments and operational decision support capabilities.

[0024] This application integrates a dynamic environmental load model (wind, temperature, snow and ice), and the simulation process can respond to real-time or predicted environmental time-series data. This allows safety assessments and scheme optimizations to fully consider the environmental uncertainties in actual operations, providing refined guidance for safe operation under different meteorological conditions (such as recommended operating speeds and wind speed limits). Simultaneously, the system supports "conditional pass" assessment conclusions and provides suggestions for adjusting operating parameters, enhancing operational flexibility and risk management capabilities in non-ideal environments, and providing dispatching and command personnel with a powerful decision support tool.

[0025] In summary, this application, through the deep integration of digital twin, real-time simulation and intelligent optimization technologies, systematically solves the key bottlenecks in safety pre-control, loading optimization and digital management of cableway transportation, and provides a complete technical solution for realizing the intelligent, safe and efficient operation of cableway transportation. Attached Figure Description

[0026] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0027] Figure 1 This is a schematic diagram of a method for pre-permissioning and loading optimization of cableway transportation operations based on digital twins and real-time simulation. Figure 2 This is a schematic diagram of a cableway transportation operation pre-permission and loading optimization system based on digital twin and real-time simulation. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] It should be understood that, when used in this application, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0030] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0031] It should also be further understood that the term “and / or” as used in this application refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes such combinations.

[0032] Example 1 like Figure 1As shown, a method for pre-permissioning and loading optimization of cableway transportation operations based on digital twins and real-time simulation is presented. This method aims to construct a high-precision, multi-physics digital twin model of the cableway and combine it with real-time simulation technology to conduct safety pre-assessment and optimize loading schemes before material loading. This method enables risk prediction and proactive control throughout the entire cableway transportation process, preventing safety hazards caused by overloading, exceeding limits, or unreasonable loading from the outset. Specifically, it includes the following steps: S1: Collect multi-source data for the cableway scenario, construct a digital twin model of the cableway based on the collected data, and set thresholds for each monitoring point.

[0033] S11: Utilizing 3D laser scanners, UAV oblique photogrammetry technology, and sensors deployed at key locations along the cableway (such as strain sensors, tilt sensors, anemometers, and temperature sensors), comprehensive geometric, structural, and environmental data of the cableway system is collected, specifically including: Topographic and geomorphological data: elevation, slope, and surface features along the cableway; Structural geometric data: three-dimensional coordinates of the support frame, structural shape, saddle position, anchor point coordinates, spatial path and sag of the load-bearing cable and traction cable; Component attribute data: connector specifications, material type, rigging diameter, length, and initial prestressing state; Environmental data: real-time or historical records of wind speed, wind direction, temperature, humidity, and snow load.

[0034] S12: Based on the collected multi-source data, a digital twin model of the cableway with multi-physics coupling capability is established in the simulation platform. The model includes the following layers: Geometric model: A detailed 3D model of the entire cableway is reconstructed based on point cloud and photogrammetric data, including supports, load-bearing cables, traction cables, saddles, anchoring devices, etc. Structural mechanics model: The key components are discretized by finite element method, and material properties (such as elastic modulus, Poisson's ratio, density, yield strength) are assigned. Prestress distribution, connection stiffness and boundary conditions are set. Environmental load models: integrated wind field model (including mean wind and fluctuating wind spectrum), temperature field model, and dynamic action model of ice and snow load; Motion and constraint model: Define the degrees of freedom, damping characteristics, and friction coefficient of each moving part, and set up a fatigue accumulation algorithm to simulate the long-term operation effects.

[0035] S13: Based on national or industry safety standards (such as the "Safety Standard for Freight Ropeways" GB12141), design documents, and historical operating data, establish monitoring points in the ropeway digital twin model and set safety thresholds for each monitoring point. This mainly includes: Structural stress threshold: the maximum allowable stress and safety factor of each support, anchor point, and rigging; Motion state thresholds: maximum swing angle, lateral displacement, and acceleration limits of the transport vehicle; Geometric deformation thresholds: maximum deflection of the load-bearing cable and limit of the support tilt angle; Environmental impact thresholds: maximum permissible wind speed, temperature range, and upper limit of ice and snow load.

[0036] Monitoring points should cover key locations along the entire cableway, including the tops of each support structure, the lowest point in the middle of the span, anchorage areas, and curves.

[0037] S2: Perform digital modeling and standardized input of materials and transport carriers, and pre-set the placement rules and constraints of materials within the carriers.

[0038] S21: For various materials that need to be transported by cableway (such as transmission line tower materials, insulators, hardware, cable reels, construction tools, etc.), establish a unified format of 3D material model library, and associate the following attribute parameters for each material: Geometric dimensions: length, width, height, and outline dimensions; Physical properties: mass, center of mass position, moment of inertia; Structural features: location and number of lifting / binding points, allowable direction and magnitude of force; Surface properties: windward area coefficient, coefficient of friction; Material categories and codes: Classified and coded according to industry standards or company specifications.

[0039] For standard materials, they can be directly retrieved from the model library; for non-standard or new materials, models should be created through 3D scanning, image modeling, or manual input, and the above parameters should be supplemented. After review, they should be included in the model library.

[0040] S22: Based on the actual type of transport vehicle used (such as baskets, containers, special lifting equipment, etc.), establish the corresponding three-dimensional vehicle model and define its following parameters: Carrier geometry and structural form; Curves or calculation formulas showing how rated load, tare weight, and center of gravity position change with loading conditions; Internal partitioning and layout of fixing devices (such as binding rings, slots, and limiting plates) of the carrier. Constraints on the position and degrees of freedom of the connection point between the carrier and the rigging.

[0041] S23: Pre-set the placement rules and constraints of materials within the carrier in the system, for example: Placement rules include: Weight distribution rules: Heavy objects should be placed at the bottom of the carrier and close to the central axis, while light objects should be placed on top; the overall center of gravity should not deviate horizontally by more than 10% of the carrier's width.

[0042] Stacking and lamination rules: Materials of the same type may be stacked vertically, but the maximum number of layers shall not be exceeded; irregularly shaped materials shall be separated by padding to avoid point contact or local stress concentration.

[0043] Orientation and alignment rules: The long axis of the material should be placed along the direction of the carrier's movement to reduce wind resistance; materials with lifting points or binding holes should have their fixing points aligned with the carrier's binding device.

[0044] Zoning and placement rules: The interior of the carrier can be divided into areas according to function or material type, with similar materials placed together for easy loading, unloading and verification.

[0045] Safety distance rule: There should be a gap of no less than 5 cm between the material and the inner wall, top cover and door of the carrier to prevent collisions caused by shaking during transportation.

[0046] The constraints include: Carrier structural constraints: The placement of materials must not obstruct critical parts such as the carrier's observation window, ventilation opening, and emergency unlocking device.

[0047] Binding point matching constraints: Each material must be connected to at least two binding points, the resultant force of the binding force should pass through the center of mass of the material, and the force on a single point should not exceed 80% of its rated load.

[0048] Transportation route constraints: In turning sections or areas of gradient change, the portion of the material protruding from the carrier outline must not exceed the safety envelope, which is dynamically generated by the cableway digital twin model based on the actual route.

[0049] Dynamic stability constraints: In the simulation, the relative displacement between the material and the carrier must not exceed the allowable value, and the binding system must remain effective under extreme wind loads.

[0050] Environmental adaptability constraints: For temperature-sensitive and humidity-sensitive materials, space for temperature and humidity monitoring and control should be reserved inside the carrier, and direct exposure to ventilation openings should be avoided.

[0051] S3: Obtain transportation task information to determine the initial loading plan, arrange the material model on the transportation carrier model according to the initial loading plan, and then load the entire cableway digital twin model for simulation configuration.

[0052] S31: Obtain and parse transportation task information. The system receives detailed instructions for this transportation task, which should include: Bill of Materials: Select the corresponding material model and quantity from the standard material model library, or upload the digital model of the new material and its parameters; Transportation route and plan: Specify the origin and destination of the transportation, the operating speed curve, and whether there are intermediate stops; Environmental parameters: Real-time collected or predicted data such as wind speed, wind direction, temperature, and humidity are used as simulation inputs; Task constraints: such as transportation time windows, special safety requirements, unloading order, etc.

[0053] S32: Based on the task information, the loading scheme of a similar historical task is used as the initial loading scheme. If there is no similar historical task, the material placement position is initially calculated based on preset placement rules and constraints as the initial loading scheme, ensuring that it does not exceed the geometric boundaries of the carrier and meets basic stability requirements. Specifically, this includes the following steps: The system has a built-in historical task library, which contains each historical task It contains the following feature vectors: in, For material feature vectors; This represents the feature vector of the transportation path. For environmental feature vectors; The feature vector of the transport vehicle; current task Similarly, extract the feature vectors described above.

[0054] Obtain material similarity : ; in, This is an attribute similarity function (such as the similarity after Euclidean distance normalization); For the first The weight of a material can be set according to the degree of its impact on loading stability; The quantity of each material category; This represents the characteristic data of the k-th type of material in the current transportation task. It is a vector or a set of data, such as mass, volume, shape category, centroid position, etc. This represents the feature data of the k-th type of material in the i-th historical task in the historical task library.

[0055] Get path similarity : ; in, For path length similarity (e.g., normalized length ratio); This represents the similarity of terrain complexity (e.g., the ratio of standard deviations of elevation changes); Similarity between the number and angle of turning segments; Let be the weighting coefficient, satisfying .

[0056] Obtaining environmental similarity : ; in, These are the average wind speeds for the current mission and historical missions, respectively. These are the average temperatures for the current task and historical tasks, respectively; These are the reasonable ranges of wind speed and temperature variation (used for normalization).

[0057] Obtaining carrier similarity : If the carrier types are the same, then Otherwise, it is 0. It can also be further refined according to the internal partitions of the carrier, the layout of the binding points, etc.

[0058] Determine the overall similarity : in, The weight coefficients for each dimension satisfy... General recommendations Higher weight, such as .

[0059] Historical mission selection strategy: The system calculates the current task and all historical tasks. Select the one with the highest similarity that exceeds the set threshold (e.g., For historical tasks with a threshold of ≥0.7, their loading schemes are used as the initial loading schemes. If no task exceeds the threshold, it is considered "no similar historical tasks," and the process switches to generating an initial scheme based on rules.

[0060] S33: Place the 3D models of the materials one by one into the corresponding positions on the transport carrier model according to the initial loading plan. The system performs the following verifications in real time: Geometric interference check: Ensures there are no collisions between materials or between materials and the carrier structure; Center of gravity position assessment: Calculate the height and horizontal offset of the center of gravity after overall loading to determine whether it is within the allowable range of the carrier; Fixing point matching: Verify whether the material binding points are aligned with the carrier fixing device, and estimate the binding force requirements; Rule compliance check: Confirm that the number of stacked layers, orientation, etc., comply with the preset rules.

[0061] If the verification fails, the system will provide adjustment suggestions and support manual fine-tuning until the solution meets the basic loading feasibility.

[0062] S34: Import the completed "transport vehicle-materials" overall model into the cableway digital twin model and configure the simulation parameters: Set the simulation time step, total duration, and output frequency; Bind the environmental load model and input current or predicted environmental data; Define the initial state of the transport vehicle (e.g., starting position, initial velocity is zero); Configure simulation output items, including physical quantities such as stress, displacement, and acceleration at each monitoring point.

[0063] S4: Perform full dynamic transportation process simulation and output time-series data of physical quantities at each monitoring point.

[0064] S41: Based on the configured simulation environment, launch the multiphysics coupled simulation engine to simulate the complete operation of the transport vehicle from the starting point to the destination. The simulation progresses step by step with a set time step, simulating the complete operation of the transport vehicle from the starting point to the destination. During the simulation, the system calculates the following dynamic and structural responses in real time: Tension distribution of the load-bearing cable and traction cable and its variation over time; Lateral force, vertical force, torque, and actual tilt angle of each support (including intermediate supports and end supports); The oscillation trajectory of the transport vehicle in three-dimensional space (including lateral, longitudinal, and torsional components), linear acceleration, and angular acceleration; Stress concentration and fatigue accumulation effects at key connection points (such as saddles, anchor points, and lifting points); The relative displacement between the material and the carrier, and the stress state at the binding point.

[0065] S42: During the simulation, the system dynamically loads wind load models, temperature field models, and snow and ice load models based on the input environmental time-series data (wind speed, wind direction, temperature, etc.), influencing the stress and motion states of the cableway system and the transport vehicle in real time. Specifically, this includes: Wind loads are distributed spatially on the load-bearing cables, traction cables, supports, and transport vehicles, taking into account the vibration response caused by pulsating winds; The cable length expansion and contraction effect caused by temperature changes is used to update the cableway geometry and internal force redistribution in real time. If ice and snow loads are present, the corresponding loads will be dynamically applied based on real-time meteorological data or prediction models.

[0066] S43: The simulation engine records the physical quantity data of each preset monitoring point at each time step and forms a time-series dataset, which mainly includes: Structural mechanics data: stress, strain, internal forces, displacement, vibration frequency; Kinematic data: position, velocity, acceleration, swing angle, angular velocity; Environmental response data: wind-induced vibration amplitude, temperature-induced deformation; System status data: real-time safety factor and cumulative fatigue damage.

[0067] S44: The system supports real-time 3D visualization of the simulation process, dynamically displaying the operating status of the transport vehicle, structural deformation, stress cloud diagrams, and risk warning information. Simultaneously, the simulation supports setting interruption conditions (such as a certain indicator exceeding a threshold). Once triggered, the simulation can be paused and the state at that moment recorded for subsequent analysis and optimization.

[0068] S45: After the simulation is completed, the system automatically summarizes all time-series data, generates a structured simulation report, and provides a data interface for the subsequent safety assessment (S5). The report includes simulation configuration information, key indicator curves, out-of-limit event records, and preliminary risk warnings.

[0069] S5: Conduct a safety assessment of the time-series physical quantity data at each monitoring point. If the safety assessment is passed, an electronic work pre-license is generated; otherwise, proceed to the next step of loading optimization. This includes the following steps: S51: Compare the time-series data of physical quantities at each monitoring point with their corresponding safety thresholds (from S13) point by point to identify whether there are any exceeding time periods, exceeding locations, and exceeding magnitudes. The system automatically marks all exceeding events and records their start time, duration, peak value, and the monitoring point number.

[0070] S52: The identified exceedance events are quantified and rated for risk, categorized into high-risk, medium-risk, and low-risk levels based on the magnitude, duration, and scope of the exceedance. Simultaneously, the system combines load distribution, motion state, and structural response data from the simulation process to pinpoint the root cause of the exceedance, for example: The high center of gravity of the material leads to increased carrier oscillation. Wind load concentration at a certain support structure caused stress exceeding the limit. Localized wear on the rigging resulted in an insufficient safety factor. Sudden changes in environmental wind conditions can cause resonance.

[0071] Risk level classification calculation formula: For each event exceeding the limit, define its risk score. as follows: ; in: The severity of exceeding the limit is calculated using the following formula: ; in, This represents the peak measurement value during the period when the monitoring point exceeded the limit. This is the safety threshold for this monitoring point.

[0072] The formula for calculating the percentage of time exceeding the limit is as follows: ; in, For the total duration exceeding the limit, This represents the total duration of the transportation mission.

[0073] The formula for calculating the percentage of the affected area is as follows: ; in, The number of monitoring points that simultaneously exceed the limit. This represents the total number of monitoring points.

[0074] Let the weighting coefficients satisfy: ; Suggested value: .

[0075] Based on risk score Risk level classification: Low risk: ; Medium risk: ; High risk: .

[0076] S53: Conduct a comprehensive security assessment, and the assessment results are divided into the following three categories: Pass: All monitoring point data did not exceed the limits throughout the process, or only showed low risk, and the system judged it to be acceptable; Conditional approval: There is a medium risk of exceeding the limit, but the risk can be mitigated by adjusting operating parameters (such as reducing speed or segmenting transportation); Failed: High-risk exceedance or multiple medium-risk exceedances exist, and safety cannot be guaranteed through parameter adjustments.

[0077] S54: If the evaluation result is "Pass" or "Conditionally Pass," the system generates an electronic pre-license for the operation, along with operational suggestions (such as maximum permissible wind speed, recommended operating speed, and load distribution precautions). The license is stored digitally and can be accessed and audited. If the evaluation result is "Fail," the system automatically rejects the loading plan and triggers the next step, the S6 loading optimization process.

[0078] S6: Perform intelligent loading optimization for unlicensed device schemes.

[0079] S61: Based on the safety assessment results, identify the key risk factors leading to "failure" (such as excessive stress on a certain support, excessive carrier sway, etc.), and construct a multi-objective optimization model accordingly. Optimization objectives may include, but are not limited to: Minimize the maximum exceedance of physical quantities at each monitoring point (such as stress and sway angle); Minimize the standard deviation of key indicators during transportation to improve operational stability; Maximize the overall safety margin (the ratio of the actual value at each monitoring point to the threshold). While meeting safety constraints, maximize the utilization of loading space and reduce the number of transport trips.

[0080] The system automatically selects or allows users to specify priorities based on the main risk factors, forming a weighted comprehensive objective function.

[0081] Let x be the design variable (combination of material placement parameters). ; in, Indicates the first The placement parameter vector of each material (including three-dimensional coordinates, rotation angle, etc.) This represents the total number of materials.

[0082] The overall objective function is: ; in, ; ; ; ; Let x be the physical quantity of monitoring point j at time t under the placement scheme x; Let x be the standard deviation of the physical quantity at monitoring point j. Let x be the effective volume occupied by the material inside the carrier. The safety threshold for monitoring point j; For all monitoring points; A subset of key monitoring points; The effective loading volume of the carrier; .

[0083] S62: Use the placement parameters of the material within the carrier as design variables and define constraints.

[0084] The placement parameters of materials within the carrier are used as design variables, including: The three-dimensional coordinates (X, Y, Z) and rotation angles (yaw, pitch, roll) of each material within the carrier. Stacking order and interlayer padding configuration (if applicable); Selection of binding and fixing points and setting of pretension force.

[0085] The constraints include: Geometric constraints: No interference between materials or between materials and the carrier; Physical constraints: The center of gravity position does not exceed the allowable range of the carrier, and the force on each binding point does not exceed its bearing limit; Task constraints: Meet the requirements of the transportation task regarding material sequence and ease of unloading.

[0086] S63: Adaptive optimization algorithm is used to automatically search the design variable space and obtain the first batch of candidate solutions under the condition of satisfying the constraints.

[0087] S64: Load the first batch of candidate schemes (usually the first 3-5) into the cableway digital twin model, re-execute the full dynamic transportation process simulation and safety assessment of steps S4 and S5, and use the schemes that pass the safety assessment as the second batch of candidate schemes.

[0088] S65: Establish a comprehensive evaluation system that includes safety, efficiency and economy, score the second batch of candidate solutions, recommend the best comprehensive candidate solution as the loading solution, and other candidate solutions that have also passed the safety assessment as alternative solutions. At the same time, issue pre-licenses to the second batch of candidate solutions.

[0089] The loading scheme output includes: Optimized 3D diagram of material placement and detailed list of location coordinates; Comparison table of expected improvements in key safety indicators; Specific operational guidelines and suggestions (such as the binding sequence, the location for adding counterweights, etc.); Summary of the simulation report corresponding to this scheme.

[0090] Example 2 like Figure 2 As shown, a cableway transportation operation pre-permission and loading optimization system based on digital twins and real-time simulation, applied to the aforementioned cableway transportation operation pre-permission and loading optimization method based on digital twins and real-time simulation, includes: The twin module is used to collect multi-source data on the cableway scene, build a digital twin model of the cableway based on the collected data, and set thresholds for each monitoring point. The data entry module is used for digital modeling and standardized data entry of materials and transport carriers, and pre-sets the rules and constraints for the placement of materials within the carrier. The initial module is used to acquire transportation task information to determine the initial loading plan, arrange the material model on the transportation carrier model according to the initial loading plan, and then load the entire cableway digital twin model for simulation configuration. The simulation module is used to perform full-dynamic transportation process simulation and outputs time-series data of physical quantities at each monitoring point. The assessment module is used to perform a safety assessment on the time-series data of physical quantities at each monitoring point. If the safety assessment is passed, an electronic work pre-license is generated; otherwise, the process proceeds to the next step of loading optimization. The optimization module is used to intelligently optimize the loading of unlicensed device schemes.

[0091] Example 3 A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the above-described method for pre-approval and loading optimization of cableway transportation operations based on digital twins and real-time simulation.

[0092] Example 4 A processor for running a program, wherein the program executes the above-described method for pre-permissioning and loading optimization of cableway transportation operations based on digital twins and real-time simulation.

[0093] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Significantly improves operational safety and risk prevention and control capabilities.

[0094] Traditional methods struggle to accurately predict the behavior of cableway systems under the coupled effects of complex environments and dynamic loads. This invention constructs a high-fidelity, multi-physics coupled digital twin model of the cableway and performs full-dynamic transportation process simulations of specific loading schemes before operation. This allows for the advance and quantitative simulation of the temporal changes in physical quantities at key monitoring points (such as support stress, rigging tension, and carrier sway) throughout the transportation process. By automatically comparing simulation data with safety thresholds and conducting risk assessments, the system can accurately identify potential over-limit events, pinpoint the root causes of risks, and make safety predictions based on quantitative ratings. This represents a fundamental shift from "post-event remediation" to "pre-event prevention," mitigating safety accidents caused by improper loading or sudden environmental changes at the source and providing an unprecedented proactive safety barrier for cableway transportation.

[0095] 2. To achieve intelligent, scientific, and optimized loading solutions.

[0096] Traditional loading methods heavily rely on operator experience and lack efficient and standardized optimization techniques. This invention combines digital modeling of materials and carriers, pre-defined placement rules, and intelligent optimization algorithms. The system can automatically recall historical similar solutions or generate initial solutions based on rules, and automatically trigger the loading optimization process when a solution fails the safety assessment. By constructing a multi-objective optimization model centered on safety, stability, and efficiency, and employing an adaptive optimization algorithm for spatial search, the system can quickly generate multiple candidate loading solutions that meet safety constraints and offer superior overall performance. This effectively solves the challenge of loading complex and irregularly shaped materials, reduces reliance on operator experience, maximizes space utilization while ensuring safety, reduces transport trips, and improves the scientific and economical nature of loading operations.

[0097] 3. Build a new paradigm for digitalized, closed-loop pre-approval and management of operations.

[0098] Traditional work permit processes and management are relatively isolated and static. This invention pioneers an electronic work pre-license generation mechanism tightly integrated with dynamic simulation verification and intelligent optimization. Only loading schemes that pass rigorous safety assessments (or optimizations) will automatically generate an electronic pre-license with a digital signature and specific operational recommendations. This forms a digital management closed loop of "simulation assessment - scheme optimization - license issuance," providing robust data support and traceability for work permits. Simultaneously, the structured data generated by the system, such as simulation reports, optimization comparison tables, and 3D diagrams, greatly facilitates work training, scheme archiving, safety audits, and continuous improvement, significantly enhancing management efficiency and the scientific and transparent nature of decision-making.

[0099] 4. Enhance adaptability to complex environments and operational decision support capabilities.

[0100] This application integrates a dynamic environmental load model (wind, temperature, snow and ice), and the simulation process can respond to real-time or predicted environmental time-series data. This allows safety assessments and scheme optimizations to fully consider the environmental uncertainties in actual operations, providing refined guidance for safe operation under different meteorological conditions (such as recommended operating speeds and wind speed limits). Simultaneously, the system supports "conditional pass" assessment conclusions and provides suggestions for adjusting operating parameters, enhancing operational flexibility and risk management capabilities in non-ideal environments, and providing dispatching and command personnel with a powerful decision support tool.

[0101] In summary, this application, through the deep integration of digital twin, real-time simulation and intelligent optimization technologies, systematically solves the key bottlenecks in safety pre-control, loading optimization and digital management of cableway transportation, and provides a complete technical solution for realizing the intelligent, safe and efficient operation of cableway transportation.

[0102] This application discloses a method, system, medium, and processor for cableway transportation operation pre-permitting and loading optimization based on digital twins and real-time simulation, belonging to the field of cableway transportation safety and intelligent technology. The method includes: constructing a cableway digital twin model and setting safety thresholds; digitally modeling and pre-setting rules for materials and transport vehicles; generating an initial loading plan based on the task and loading it into the model for full-dynamic transportation simulation; performing a safety assessment based on the time-series data output from the simulation; generating an electronic operation pre-permit if the assessment passes; triggering intelligent loading optimization if the assessment fails, iteratively finding the optimal solution through a multi-objective optimization model, and finally outputting a safe and feasible optimized solution and corresponding permit. This invention achieves high-precision safety pre-assessment and automatic optimization of loading plans before cableway transportation operations, significantly improving operational safety, loading efficiency, and the level of digital management.

[0103] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0104] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0105] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0106] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of this application.

Claims

1. A method for pre-permissioning and loading optimization of cableway transportation operations based on digital twins and real-time simulation, characterized in that, include: S1: Collect multi-source data for the cableway scenario, construct a digital twin model of the cableway based on the collected data, and set thresholds for each monitoring point; S2: Digitally model and standardize the input of materials and transport carriers, and pre-set the placement rules and constraints of materials within the carriers; S3: Obtain transportation task information to determine the initial loading plan, arrange the material model on the transportation carrier model according to the initial loading plan, and then load the entire cableway digital twin model for simulation configuration. S4: Perform full dynamic transportation process simulation and output time-series data of physical quantities at each monitoring point; S5: Conduct a safety assessment on the time-series data of physical quantities at each monitoring point. If the safety assessment is passed, an electronic work pre-license will be generated; otherwise, proceed to the next step of loading optimization. S6: Perform intelligent loading optimization for unlicensed device schemes.

2. The method for pre-permissioning and loading optimization of cableway transportation operations based on digital twins and real-time simulation according to claim 1, characterized in that, In step S1, the process of collecting multi-source data on the cableway scene, constructing a digital twin model of the cableway based on the collected data, and setting thresholds for each monitoring point includes the following steps: S11: Comprehensive collection of geometric, structural, and environmental data of the cableway system; S12: Based on the collected multi-source data, establish a cableway digital twin model with multi-physics coupling capability in the simulation platform; S13: Arrange monitoring points in the cableway digital twin model and set safety thresholds for each monitoring point.

3. The method for pre-approval and loading optimization of cableway transportation operations based on digital twins and real-time simulation according to claim 1, characterized in that, In step S3, obtaining transportation task information to determine the initial loading plan, arranging the material model on the transportation carrier model according to the initial loading plan, and then loading the entire cableway digital twin model for simulation configuration includes the following steps: S31: Obtain and parse transportation task information; S32: Based on the task information, call the loading scheme of similar historical tasks as the initial loading scheme. If there are no similar historical tasks, calculate the material placement position based on the preset placement rules and constraints as the initial loading scheme. S33: Place the three-dimensional models of the materials one by one into the corresponding positions of the transport carrier model according to the initial loading plan; S34: Import the completed "transport carrier-material" overall model into the cableway digital twin model and configure the simulation parameters.

4. The method for pre-approval and loading optimization of cableway transportation operations based on digital twins and real-time simulation according to claim 1, characterized in that, In step S4, the process of performing a full-dynamic transportation process simulation and outputting time-series data of physical quantities at each monitoring point includes the following steps: S41: Based on the configured simulation environment, start the multiphysics coupling simulation engine to simulate the complete operation process of the transport vehicle from the starting point to the destination; S42: During the simulation, the system dynamically loads wind load model, temperature field model and ice and snow load model according to the input environmental time series data, which affects the force and motion state of the cableway system and the transport vehicle in real time. S43: Record the physical quantity data of each preset monitoring point at each time step and form a physical quantity time series dataset; S44: Dynamically displays the operating status of the transport vehicle, structural deformation, stress cloud diagram, and risk warning information; S45: Summarize all time-series data and generate a structured simulation report.

5. The method for pre-approval and loading optimization of cableway transportation operations based on digital twins and real-time simulation according to claim 1, characterized in that, In step S5, a security assessment is performed on the time-series data of physical quantities at each monitoring point. If the security assessment is passed, an electronic work pre-license is generated; otherwise, the process proceeds to the next step of loading optimization, which includes the following steps: S51: Compare the time-series data of physical quantities at each monitoring point with their corresponding safety thresholds point by point to identify whether there are any time periods, locations, or magnitudes of exceeding limits. S52: Conduct risk quantification and rating of identified out-of-limit events, classifying them according to the extent of the out-of-limit, duration, and scope of impact; S53: Conduct a comprehensive security assessment based on the number, level, and distribution of out-of-limit events; S54: If the evaluation result is "pass" or "conditionally pass", the system generates an electronic work pre-license with operational suggestions; if the evaluation result is "fail", the system automatically rejects the current loading plan and triggers the next loading optimization process.

6. The method for pre-permissioning and loading optimization of cableway transportation operations based on digital twins and real-time simulation according to claim 1, characterized in that, In step S6, the intelligent loading optimization of the unlicensed device scheme includes the following steps: S61: Based on the safety assessment results, identify the key risk factors that lead to failure, and construct a multi-objective optimization model accordingly; S62: Use the placement parameters of materials within the carrier as design variables and define constraints; S63: Adaptive optimization algorithm is used to automatically search the design variable space and obtain the first batch of candidate solutions under the condition of satisfying the constraints. S64: Load the first batch of candidate schemes into the cableway digital twin model, re-execute the full dynamic transportation process simulation and safety assessment, and use the schemes that pass the safety assessment as the second batch of candidate schemes; S65: Establish a comprehensive evaluation system that includes safety, efficiency, and economy, score the second batch of candidate solutions, recommend the best comprehensive candidate solution as the loading solution, and issue pre-licenses to the second batch of candidate solutions.

7. The method for pre-approval and loading optimization of cableway transportation operations based on digital twins and real-time simulation according to claim 6, characterized in that, In step S61, the objective function of the multi-objective optimization model is as follows: ； ； ； ； ； in, Let x be the physical quantity of monitoring point j at time t under the placement scheme x; Let x be the standard deviation of the physical quantity at monitoring point j. Let x be the effective volume occupied by the material inside the carrier. The safety threshold for monitoring point j; For all monitoring points; A subset of key monitoring points; The effective loading volume of the carrier; .

8. A cableway transportation operation pre-approval and loading optimization system based on digital twin and real-time simulation, characterized in that, The cableway transportation operation pre-permission and loading optimization method based on digital twin and real-time simulation as described in any one of claims 1 to 7 includes: The twin module is used to collect multi-source data on the cableway scene, build a digital twin model of the cableway based on the collected data, and set thresholds for each monitoring point. The data entry module is used for digital modeling and standardized data entry of materials and transport carriers, and pre-sets the rules and constraints for the placement of materials within the carrier. The initial module is used to acquire transportation task information to determine the initial loading plan, arrange the material model on the transportation carrier model according to the initial loading plan, and then load the entire cableway digital twin model for simulation configuration. The simulation module is used to perform full-dynamic transportation process simulation and outputs time-series data of physical quantities at each monitoring point. The assessment module is used to perform a safety assessment on the time-series data of physical quantities at each monitoring point. If the safety assessment is passed, an electronic work pre-license is generated; otherwise, the process proceeds to the next step of loading optimization. The optimization module is used to intelligently optimize the loading of unlicensed device schemes.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the cableway transportation operation pre-approval and loading optimization method based on digital twin and real-time simulation as described in any one of claims 1 to 7.

10. A processor, characterized in that, The processor is used to run a program, wherein the program executes the cableway transportation operation pre-permission and loading optimization method based on digital twin and real-time simulation as described in any one of claims 1 to 7.

Images