A control system and method for a cable lifting device based on PLC

Abstract

The present invention discloses a control system and method for a cable lifting device based on PLC, which relates to the field of automatic control technology. The present invention includes an adjustment module, an adaptive speed regulation module, a predictive maintenance module and an autonomous navigation module; the adjustment module is used to control and adjust the cable lifting device, including a main controller. In the present invention, by integrating the adaptive speed regulation module, the predictive maintenance module and the autonomous navigation module, the efficiency and safety of cable lifting operations are significantly improved. The adaptive speed regulation module uses machine learning algorithms to dynamically adjust the motor operating speed to adapt to different cable types and tunnel conditions, thereby reducing time delays and equipment losses caused by speed mismatches. The predictive maintenance module predicts potential faults and maintenance requirements by real-time monitoring and analyzing equipment operation data, effectively reducing unexpected downtime and ensuring the continuity and stability of operations.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control, and specifically to a control system and method for a cable lifting device based on PLC. Background Art

[0002] In the laying operation of power cable tunnels, the traditional method relies on manual labor to lift the cable section by section onto the brackets; this method is extremely inefficient, labor-intensive, and consumes a high amount of human resources in long-distance cable tunnels; due to the heavy weight of the cable and the large amount of work, manual operation is not only inefficient but also poses a high safety risk;

[0003] The main problems faced by existing cable laying technologies include: low operation efficiency, high labor intensity, human resource intensiveness, and safety risks in dense operation environments; in addition, due to the lack of an automated control system, it is difficult for cable lifting operations to adapt to changing tunnel conditions and cable types, resulting in the inability to achieve efficient, stable, and reliable cable laying operations; these problems limit the development of cable laying technologies and increase project costs and maintenance difficulties.

[0004] To solve the above defects, a technical solution is provided herein. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems of low operation efficiency, high labor intensity, high safety risk, and lack of automated control during the laying process of power cable tunnels, and to propose a control system and method for a cable lifting device based on PLC.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A control system for a cable lifting device based on PLC, comprising:

[0008] An adjustment module for controlling and adjusting the cable lifting device;

[0009] An adaptive speed regulation module for analyzing historical data through machine learning algorithms to predict the optimal speed setting to adapt to different cable types and tunnel conditions;

[0010] A predictive maintenance module for collecting and analyzing the operating data of the device to predict equipment failures and maintenance requirements, and reducing unexpected downtime;

[0011] An autonomous navigation module for using sensors and algorithms to achieve autonomous navigation, enabling the cable lifting device to automatically plan paths within the tunnel and avoid obstacles. The specific steps are as follows:

[0012] First, conduct a survey of the tunnel environment to obtain the tunnel's dimensions, shape, obstacle types and distribution, select sensors to detect the surrounding environment and obstacles, and deploy the sensors on the cable lifting device;

[0013] Collect data on the tunnel environment and obstacles through sensors, fuse the data from different sensors, and use the sensor data to construct a map of the tunnel, including the positions of obstacles and the geometric structure of the tunnel;

[0014] Set a path planning algorithm to calculate the best path from the starting point to the ending point, and then use an obstacle avoidance algorithm to ensure that dynamic or static obstacles can be avoided in real time during the path planning process;

[0015] Perform priority screening on several paths planned by the path planning algorithm and the obstacle avoidance algorithm, and obtain the optimal path through comprehensive analysis of the selection parameters of several planned paths;

[0016] Convert the selected optimal path into specific movement instructions for the cable lifting device through a navigation control algorithm, simulate the tunnel environment in a virtual environment, test the performance of the navigation, and integrate the autonomous navigation module with the existing PLC control system and sensors;

[0017] Through the user interface, enable the operator to monitor the navigation status and provide a manual intervention function; integrate the functions of emergency stop, manual obstacle avoidance, and speed control in the automatic navigation module.

[0018] Furthermore, the analysis steps of the optimal path in the autonomous navigation module are as follows:

[0019] Among them, the selection parameters include:

[0020] Path length: The total length of the path;

[0021] Path time: The time required to complete the path calculated according to the expected speed;

[0022] Path curvature: The curvature change of the path;

[0023] Path complexity: After normalizing the number of turns and the frequency of direction changes in the planned path, use the number of turns as the bottom circle radius and the frequency of direction changes as the height to establish a cone model, calculate the surface area of the cone model, and record it as the complexity value. Use this complexity value as the standard to measure the path complexity;

[0024] Obstacle proximity: The distance from the path to the obstacles. Calculate the average value of the distances of the obstacles along the path, record it as the obstacle distance value, and use this obstacle distance value as the standard to measure the obstacle proximity;

[0025] Energy consumption assessment: Evaluate the energy consumption required for path travel, and use the power consumption as the evaluation unit;

[0026] Normalize the obtained path length, path time, path curvature, complexity value, obstacle distance value, and power consumption, and then substitute them into the following formula:

[0027]

[0028] To obtain the optimal value YXZ, where lc, lt, lq, zf, zl, and dx are the path length, path time, path curvature, complexity value, obstacle distance value, and power consumption respectively, and are the preset weight coefficients for the path length, path time, path curvature, complexity value, obstacle distance value, and power consumption respectively. Then, use the calculated optimal value YXZ as the priority evaluation criterion for measuring the planned path;

[0029] Sort the optimal values YXZ obtained from several planned paths in descending order, and select the planned path with the largest optimal value YXZ as the optimal path.

[0030] Furthermore, the adjustment module includes a main controller, a wheel drive unit, a cable conveyor drive unit, a swing arm drive unit, a steering control unit, a lifting control unit, a lighting control unit, and a remote control unit:

[0031] The main controller is a PLC;

[0032] The wheel drive unit is used for speed regulation control and data acquisition of the wheel drive motor;

[0033] Among them, the speed regulation control adjusts the speed of the wheel drive motor through the communication bus, and the data acquisition collects the data of the wheel drive motor through the communication bus for monitoring the motor status. The remote control unit drives, stops, or reverses all wheel drive motors simultaneously;

[0034] The cable conveyor drive unit is used for speed regulation control and data acquisition of the cable conveyor drive motor;

[0035] Among them, the speed regulation control adjusts the speed of the cable conveyor drive motor through the communication bus, and the data acquisition collects the data of the cable conveyor drive motor through the communication bus for monitoring the motor status. The speed of the cable conveyor drive motor is the same as that of the wheel drive motor. When the wheel drive motor is started, the cable conveyor drive motor starts synchronously;

[0036] The swing arm drive unit is used for speed regulation control and data acquisition of the swing arm drive motor;

[0037] Among them, speed control regulates the speed of the swing arm drive motor through the communication bus, data acquisition collects the data of the swing arm drive motor through the communication bus for monitoring the motor status, and the swing arm drive motor is driven and controlled through the wired remote control and wireless remote control in the remote control unit. The swing arm drive motor is used to drive the swing arm to rotate clockwise or counterclockwise;

[0038] The steering control unit includes a steering motor, a steering electric push rod and a steering contactor;

[0039] By reversing the positive and negative poles of the DC motor power supply through the steering contactor, the steering motor rotates forward and backward, drives the steering electric push rod, and pushes the wheels to turn. The steering contactor is steered and controlled through the wired remote control and wireless remote control in the remote control unit;

[0040] The lifting control unit includes a lifting motor, a lifting electric push rod and a lifting contactor;

[0041] By reversing the positive and negative poles of the DC motor power supply through the lifting contactor, the lifting motor rotates forward and backward, drives the lifting electric push rod, and pushes the platform to lift. The lifting contactor is lift-controlled through the wired remote control and wireless remote control in the remote control unit;

[0042] The lighting control unit includes an LED light strip and a normally open contactor;

[0043] The LED light strip is controlled through the wired remote control and wireless remote control in the remote control unit;

[0044] The remote control unit includes a wireless remote control and a wired remote control. The wireless remote control includes a wireless communication module and a remote control body. The wired remote control is connected to the control terminal and issues commands to the main controller through buttons;

[0045] Among them, the wheel drive unit, the conveying drive unit, the swing arm drive unit, the steering control unit, the lifting control unit and the lighting control unit are all connected to the main controller through communication lines. The main controller sends commands according to the wired remote control or wireless remote control in the remote control unit and executes corresponding controls.

[0046] Further, the specific operation steps of the adaptive speed regulation module for predicting the optimal speed setting by analyzing historical data through machine learning algorithms are as follows:

[0047] First, through the data acquisition function in the adjustment module, collect the historical operation data of the cable conveying drive motor and the wheel drive motor, and then collect and record the cable type, tunnel conditions, motor operating speed and load conditions;

[0048] Clean, standardize, and extract features from the collected data for training machine learning models. Then, screen key data features as input features for predicting motor speed, including cable weight, diameter, tunnel length, and curvature;

[0049] Use machine learning algorithms to train the model. The selected machine learning algorithms include regression analysis, decision trees, random forests, or neural networks. Predict the optimal motor operating speed under different conditions through the model, including cable conveyor drive motors and wheel drive motors;

[0050] Then, evaluate the accuracy and generalization ability of the model through cross-validation and test sets. Implement real-time data collection to monitor the current load and operating conditions. Through an adaptive speed control algorithm, dynamically adjust the motor speed according to the collected real-time data and the trained model. The adaptive speed control algorithm takes real-time data as input and outputs speed control commands for cable conveyor drive motors and wheel drive motors;

[0051] Integrate the above adaptive speed control algorithm into the PLC control system to ensure seamless cooperation between the algorithm and the motor speed control function. Then, update the user interface in the remote control unit so that the user interface can display real-time data and prediction results and allow operators to manually adjust the motor speed;

[0052] Implement monitoring and measurement to ensure that the adaptive speed control does not affect equipment and operation safety. Test the performance of the adaptive speed control in the actual operating environment. According to the test results, adjust the algorithm and model parameters.

[0053] Furthermore, the specific operation steps for the predictive maintenance module to predict equipment failures and maintenance requirements by collecting and analyzing equipment operation data are as follows:

[0054] First, identify key components in the system, install sensors on the key components to collect temperature, vibration, sound, current, and voltage parameters;

[0055] Utilize Internet of Things technology to transmit sensor data to the central monitoring system in real time and store the collected data in a database for historical analysis and trend tracking. Then, through data analysis tools, including statistical analysis or machine learning algorithms, analyze equipment operation data to identify abnormal patterns and potential failures;

[0056] Then, establish a prediction model, train the model using historical failure data to predict equipment failures and maintenance requirements. Implement real-time monitoring, continuously analyze the equipment status, use the prediction model to evaluate the equipment health status, set thresholds for key parameters of the equipment, and issue an alarm when the parameters exceed the normal range. Among them, adaptively adjust the set thresholds according to the performance factors of different equipment;

[0057] When the prediction model indicates that the device is about to fail, automatically trigger maintenance tasks, optimize the maintenance plan and spare parts inventory according to the prediction results, reduce unexpected downtime, and integrate predictive maintenance information on the wired and wireless remote control user interfaces so that operators can understand the device status in real time;

[0058] According to the instructions of predictive maintenance, perform preventive maintenance, including replacing components or adjusting device parameters, and feed back maintenance and failure data to continuously optimize the prediction model. Integrate the predictive maintenance module with the existing PLC control system and communication network to ensure seamless collaboration.

[0059] Furthermore, the specific process of the predictive maintenance module adaptively adjusting the set threshold for different device performance factors is as follows:

[0060] The performance parameters of the device include: temperature, vibration, sound, current and voltage, speed and load, pressure, flow, torque and power, running time and cycle, and fault codes;

[0061] Using historical data and machine learning algorithms, automatically identify the normal operation range, set the corresponding threshold, and then cooperate with the adaptive algorithm to dynamically adjust the threshold according to the actual operating conditions of the device to adapt to different working conditions and loads. Combine the data analysis of multiple parameters to obtain a comprehensive assessment of the device health status, so as to set the threshold more accurately;

[0062] Implement a real-time monitoring system, continuously analyze the device status, and adjust the threshold according to the real-time data. Through trend analysis, identify the change trend of parameters over time and adjust the threshold accordingly. Consider the impact of seasonal changes and environmental conditions on the device performance and adjust the threshold accordingly;

[0063] Integrate the prediction model, use the model output as the basis for adjusting the threshold, establish a feedback mechanism, allow operators and maintenance personnel to report the accuracy of the threshold according to actual experience and make adjustments, and learn from each maintenance and failure to continuously optimize the threshold setting.

[0064] Furthermore, a control method for a PLC-based cable lifting device includes the following steps:

[0065] S1. System construction and component definition: Construct a PLC-based cable lifting control system and define key components including the main controller, drive unit, control unit, and remote control unit;

[0066] S2. Data acquisition: Implement motor speed control and data acquisition, monitor and record the motor status, and provide basic data for subsequent control;

[0067] S3. Adaptive speed regulation module implementation: Training a machine learning model using historical operation data to achieve the adaptive speed regulation function of dynamically adjusting the motor speed according to real-time data;

[0068] S4. Predictive maintenance module implementation: Collecting the operating parameters of key components through sensors, using Internet of Things technology for real-time monitoring, and establishing a prediction model to predict faults and maintenance requirements;

[0069] S5. Autonomous navigation module implementation: Investigating the tunnel environment, deploying sensors to collect data, constructing a map, and achieving autonomous navigation to plan paths and avoid obstacles;

[0070] S6. Path planning and obstacle avoidance: Applying path planning and obstacle avoidance algorithms to calculate the optimal path and ensuring the ability to respond to dynamic and static obstacles in real time during navigation;

[0071] S7. Path optimization parameter analysis: Comprehensively considering the optimization parameters, calculating the optimal value to evaluate and screen the optimal path;

[0072] S8. Path selection and navigation control: Selecting the optimal path according to the optimal value, converting it into specific movement instructions, and integrating it into the PLC control system to achieve automatic navigation;

[0073] S9. Integration of user interface and safety features: Updating the user interface to display real-time data, integrating emergency stop and manual obstacle avoidance functions, and enhancing the interactivity and safety of the system;

[0074] S10. System testing and optimization: Conducting comprehensive tests in the actual operation environment, optimizing the system performance according to the feedback, and ensuring the effectiveness of adaptive speed regulation and predictive maintenance.

[0075] Compared with the prior art, the beneficial effects of the present invention are:

[0076] (1) In the present invention, by integrating the adaptive speed regulation module, the predictive maintenance module, and the autonomous navigation module, the efficiency and safety of the cable lifting operation are significantly improved. The adaptive speed regulation module uses machine learning algorithms to dynamically adjust the motor operating speed to adapt to different cable types and tunnel conditions, thereby reducing time delays and equipment losses caused by speed mismatches. The predictive maintenance module predicts potential faults and maintenance requirements by real-time monitoring and analyzing equipment operation data, effectively reducing unexpected downtime and ensuring the continuity and stability of the operation;

[0077] (2) In the present invention, the application of the autonomous navigation module enables the cable lifting device to achieve autonomous path planning and obstacle avoidance in complex tunnel environments, reducing the dependence on manual operations. Through priority screening and optimal parameter analysis, this module can intelligently select the optimal path, improving the accuracy and reliability of navigation. In addition, the user interface of the remote control unit integrates real-time data display and prediction results, enabling operators to more intuitively monitor the system status and perform manual intervention when necessary, enhancing the flexibility and responsiveness of the system;

[0078] (3) In the present invention, the predictive maintenance module dynamically adjusts the threshold through an adaptive algorithm and provides a comprehensive assessment of the equipment's health status by combining data analysis of multiple parameters, making maintenance work more accurate and timely. This data-driven maintenance strategy not only extends the service life of the equipment but also reduces unnecessary maintenance costs. At the same time, the emergency stop, manual obstacle avoidance, and speed control functions of the autonomous navigation module further improve the safety of operations, avoiding accidents and losses that may be caused by operational errors, thereby reducing the overall operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings;

[0080] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0081] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0082] It should be understood that the terms "comprising" and "including" used in the specification and claims of this disclosure indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0083] It should also be understood that the terms used in this disclosure specification are merely for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure specification and the claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms. It should be further understood that the term "and / or" used in this disclosure specification and the claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0084] As Figure 1 shown, a control system of a cable lifting device based on PLC includes an adjustment module, an adaptive speed regulation module, a predictive maintenance module, and an autonomous navigation module;

[0085] The adjustment module is used to control and adjust the cable lifting device, including a main controller, a wheel drive unit, a conveying drive unit, a swing arm drive unit, a steering control unit, a lifting control unit, a lighting control unit, and a remote control unit; wherein the cable lifting device is composed of the main controller, the wheel drive unit, the conveying drive unit, the swing arm drive unit, the steering control unit, the lifting control unit, and the lighting control unit in the adjustment module;

[0086] The main controller is a PLC;

[0087] The wheel drive unit is used for speed regulation control and data acquisition of the wheel drive motor; wherein the speed regulation control regulates the speed of the wheel drive motor through the communication bus, and the data acquisition collects the data of the wheel drive motor through the communication bus for monitoring the state of the motor, and the remote control unit drives, stops, or reverses all the wheel drive motors simultaneously;

[0088] The conveying drive unit is used for speed regulation control and data acquisition of the cable conveying drive motor; wherein the speed regulation control regulates the speed of the cable conveying drive motor through the communication bus, and the data acquisition collects the data of the cable conveying drive motor through the communication bus for monitoring the state of the motor, and the speed of the cable conveying drive motor is the same as that of the wheel drive motor. When the wheel drive motor is started, the cable conveying drive motor starts synchronously;

[0089] The swing arm drive unit is used for speed regulation control and data acquisition of the swing arm drive motor; wherein the speed regulation control regulates the speed of the swing arm drive motor through the communication bus, and the data acquisition collects the data of the swing arm drive motor through the communication bus for monitoring the state of the motor, and the swing arm drive motor is driven and controlled through the wired remote control and wireless remote control in the remote control unit, and the swing arm is driven by the swing arm drive motor to rotate clockwise or counterclockwise;

[0090] The steering control unit includes a steering motor, a steering electric push rod and a steering contactor; by reversing the positive and negative poles of the DC motor power supply through the steering contactor, the steering motor rotates forward and backward, drives the steering electric push rod, and pushes the wheels to turn. The steering contactor is controlled for steering through the wired remote control and wireless remote control in the remote control unit;

[0091] The lifting control unit includes a lifting motor, a lifting electric push rod and a lifting contactor; by reversing the positive and negative poles of the DC motor power supply through the lifting contactor, the lifting motor rotates forward and backward, drives the lifting electric push rod, and pushes the platform to lift. The lifting contactor is controlled for lifting through the wired remote control and wireless remote control in the remote control unit;

[0092] The lighting control unit includes an LED light strip and a normally open contactor; the LED light strip is controlled through the wired remote control and wireless remote control in the remote control unit;

[0093] The remote control unit includes a wireless remote control and a wired remote control. The wireless remote control includes a wireless communication module and a remote control body. The wired remote control is connected to the control terminal and issues instructions to the main controller through buttons;

[0094] Among them, the wheel drive unit, the conveying drive unit, the swing arm drive unit, the steering control unit, the lifting control unit and the lighting control unit are all connected to the main controller through communication lines. The main controller sends instructions according to the wired remote control or wireless remote control in the remote control unit and executes corresponding controls.

[0095] The adaptive speed regulation module is used to analyze historical data through machine learning algorithms and predict the optimal speed setting to adapt to different cable types and tunnel conditions;

[0096] First, through the data acquisition function in the adjustment module, collect the historical operation data of the cable conveying drive motor and the wheel drive motor, and then collect and record the cable type, tunnel conditions, motor operating speed and load conditions; clean, standardize and extract features from the collected data for training the machine learning model, and then screen the key data features as input features for predicting the motor speed, including cable weight, diameter, tunnel length and curvature; use machine learning algorithms to train the model. The selected machine learning algorithms include regression analysis, decision tree, random forest or neural network, and predict the optimal motor operating speed under different conditions through the model, including the cable conveying drive motor and the wheel drive motor;

[0097] Then, cross-validation and a test set are used to evaluate the accuracy and generalization ability of the model. Real-time data collection is implemented to monitor the current load and operating conditions. Through an adaptive speed control algorithm, the motor speed is dynamically adjusted based on the collected real-time data and the trained model. The adaptive speed control algorithm takes real-time data as input and outputs speed control commands for the cable conveyor drive motor and the wheel drive motor. The above adaptive speed control algorithm is integrated into the PLC control system to ensure seamless cooperation between the algorithm and the motor speed control function. Then, the user interface in the remote control unit is updated so that the user interface can display real-time data and prediction results and allow the operator to manually adjust the motor speed when necessary. Monitoring and measurement are implemented to ensure that the adaptive speed control does not affect the equipment and operation safety. The performance of the adaptive speed control is tested in the actual operating environment, and the algorithm and model parameters are adjusted according to the test results.

[0098] The predictive maintenance module is used to predict equipment failures and maintenance requirements by collecting and analyzing the operating data of the equipment, reducing unexpected downtime.

[0099] First, identify the key components in the system, including drive motors, electric push rods, contactors, etc. When a failure occurs in the identified key components, it may cause downtime. Install sensors on the key components to collect temperature, vibration, sound, current, and voltage parameters. Using Internet of Things (IoT) technology, transmit the sensor data to the central monitoring system in real-time and store the collected data in the database for historical analysis and trend tracking. Then, through data analysis tools, including statistical analysis or machine learning algorithms, analyze the equipment operating data to identify abnormal patterns and potential failures.

[0100] Then, establish a prediction model and train the model using historical failure data to predict equipment failures and maintenance requirements. Implement real-time monitoring, continuously analyze the equipment status, use the prediction model to evaluate the equipment health status, set thresholds for the key parameters of the equipment, and issue an alarm when the parameters exceed the normal range. Among them, for the performance factors of different equipment, adaptively adjust the set thresholds. The performance parameters of the equipment include:

[0101] Temperature: The temperature of equipment components can reflect their operating status. Excessive or too low temperature may indicate problems; Vibration: The vibration level can indicate mechanical faults, such as imbalance, wear or looseness; Sound: Abnormal sounds may indicate problems inside the equipment; Current and voltage: Fluctuations in the current and voltage of motors and other electrical equipment can indicate load changes or electrical problems; Speed and load: Changes in the operating speed and load of the equipment can indicate performance degradation or overload; Pressure: Changes in pressure in hydraulic or pneumatic systems can indicate leaks or blockages; Flow rate: Changes in the flow rate of liquids or gases can indicate changes in system efficiency; Torque and power: The torque and power output of the equipment can reflect its performance level; Operating time and cycle: The cumulative operating time and working cycle of the equipment can indicate the degree of wear; Fault codes: The equipment may provide fault codes indicating specific problems or errors;

[0102] Utilize historical data and machine learning algorithms to automatically identify the normal operating range, set corresponding thresholds, and then cooperate with adaptive algorithms to dynamically adjust the thresholds according to the actual operating conditions of the equipment to adapt to different working conditions and loads. Combine the data analysis of multiple parameters to obtain a more comprehensive assessment of the equipment's health status, so as to set the thresholds more accurately; Implement a real-time monitoring system to continuously analyze the equipment status and adjust the thresholds according to real-time data. Through trend analysis, identify the changing trends of parameters over time and adjust the thresholds accordingly. Consider the impact of seasonal changes and environmental conditions on the equipment performance and adjust the thresholds accordingly; Integrate the prediction models, use the model outputs as the basis for adjusting the thresholds, establish a feedback mechanism, allow operators and maintenance personnel to report the accuracy of the thresholds based on actual experience and make adjustments, and learn from each maintenance and failure to continuously optimize the threshold setting;

[0103] When the prediction model indicates that the equipment may be about to fail, automatically trigger maintenance tasks. According to the prediction results, optimize the maintenance plan and spare parts inventory, reduce unexpected downtime, integrate predictive maintenance information on the wired and wireless remote control user interfaces, enabling operators to understand the equipment status in real time; Execute preventive maintenance according to the instructions of predictive maintenance, including replacing components or adjusting equipment parameters, and provide feedback on maintenance and failure data to continuously optimize the prediction model. Integrate the predictive maintenance module with the existing PLC control system and communication network to ensure seamless cooperation.

[0104] The autonomous navigation module is used to enable the cable lifting device to automatically plan paths and avoid obstacles in the tunnel through autonomous navigation;

[0105] First, conduct a survey of the tunnel environment to obtain the tunnel's dimensions, shape, obstacle types and distribution. Select sensors, including lidar, ultrasonic sensors or cameras, for detecting the surrounding environment and obstacles, and deploy the sensors on the cable lifting device to ensure coverage of all movable directions; collect data on the tunnel environment and obstacles through the sensors, fuse the data from different sensors to obtain more comprehensive and accurate environmental information, and use the sensor data to construct a map of the tunnel, including the positions of obstacles and the geometric structure of the tunnel;

[0106] Set path planning algorithms, including Dijkstra algorithm or RRT (Rapidly-exploring Random Tree) algorithm, for calculating the optimal path from the starting point to the ending point, and then through an obstacle avoidance algorithm, ensure that dynamic or static obstacles can be avoided in real time during the path planning process; perform priority screening on several paths planned by the path planning algorithm and the obstacle avoidance algorithm, and conduct comprehensive analysis by collecting the optimization parameters of several planned paths. The optimization parameters include:

[0107] Path length: The total length of the path. Usually, the shortest path is selected to reduce travel time and distance; Path time: The time required to complete the path calculated according to the expected speed; Path curvature: The change in the curvature of the path. A smoother path is usually better to reduce steering stress and improve comfort; Path complexity: After normalizing the number of turns and the frequency of direction changes in the planned path, using the number of turns as the bottom circle radius and the direction change frequency as the height to establish a cone model, calculate the surface area of the cone model, and record it as the complexity value. This complexity value is used as the standard to measure the path complexity. A smaller complexity value means fewer changes and is usually better; Obstacle proximity: The distance from the path to the obstacle. A path far from the obstacle is safer. Calculate the average value of the distances of the obstacles along the path, record it as the obstacle distance value, and use this obstacle distance value as the standard to measure the obstacle proximity; Energy consumption assessment: Evaluate the energy consumption required for traveling along the path, and use the power consumption as the evaluation unit;

[0108] After normalizing the obtained path length, path time, path curvature, complexity value, obstacle distance value and power consumption, substitute them into the following formula:

[0109]

[0110] To obtain the optimal value YXZ, where lc, lt, lq, zf, zl and dx are the path length, path time, path curvature, complexity value, obstacle distance value and power consumption respectively, κ 1 、κ 2 、κ 3 、κ 4 、κ 5 、κ 6Preset weight coefficients for path length, path time, path curvature, complexity value, obstacle distance value, and power consumption, which are respectively 1.21, 0.97, 1.13, 0.84, 0.92, and 0.93, and use the calculated optimal value YXZ as the priority evaluation criterion for measuring the planned path; sort the optimal values YXZ obtained from several planned paths according to their magnitudes, and select the planned path with the largest optimal value YXZ as the optimal path;

[0111] Convert the selected optimal path into specific movement instructions for the cable lifting device through the navigation control algorithm, simulate the tunnel environment in the virtual environment, test the performance of the navigation, integrate the autonomous navigation module with the existing PLC control system and sensors to ensure that they can work together, and then enable the operator to monitor the navigation status through the user interface and perform manual intervention when necessary; integrate the functions of emergency stop, manual obstacle avoidance, and speed control in the automatic navigation module.

[0112] A method for a cable lifting device based on PLC, the specific steps are as follows:

[0113] System construction and component definition: Construct a control system, including an adjustment module, a main controller (PLC), a wheel drive unit, a conveyor drive unit, a swing arm drive unit, a steering control unit, a lifting control unit, a lighting control unit, and a remote control unit;

[0114] Data acquisition: Perform speed control and data acquisition on the motors through the wheel drive unit and the conveyor drive unit, and monitor the motor status;

[0115] Implementation of the adaptive speed regulation module: Collect historical operation data of the cable conveyor drive motor and the wheel drive motor, including cable type, tunnel conditions, motor operating speed, and load conditions; clean, standardize, and extract features from the data for training a machine learning model; use algorithms (such as regression analysis, decision tree, random forest, or neural network) to train the model to predict the optimal motor operating speed; implement real-time data acquisition and dynamically adjust the motor speed according to the model;

[0116] Implementation of the predictive maintenance module: Identify key components in the system and install sensors to collect operating parameters (such as temperature, vibration, sound, current, voltage); use Internet of Things technology to transmit sensor data to the central monitoring system in real time; analyze the device operating data through data analysis tools to identify abnormal patterns and potential faults; establish and train a prediction model to predict device failures and maintenance requirements;

[0117] Implementation of the autonomous navigation module: Conduct research on the tunnel environment to obtain dimensions, shapes, obstacle types, and distributions; select and deploy sensors (such as lidar, ultrasonic sensors, or cameras); Path planning and obstacle avoidance:

[0118] Collect and fuse sensor data to construct a tunnel map; set path planning algorithms (such as Dijkstra or RRT algorithms) to calculate the optimal path; implement obstacle avoidance algorithms to ensure that the path planning can avoid obstacles in real time; path optimization parameter analysis:

[0119] Collect optimization parameters such as path length, time, curvature, complexity, obstacle proximity, and energy consumption assessment; calculate the optimal value YXZ through the weighted summation formula as the path priority evaluation criterion;

[0120] Path selection and navigation control: Sort the paths according to the optimal value YXZ and select the optimal path; convert the optimal path into specific movement instructions, test the navigation performance, and integrate it into the PLC control system;

[0121] Integration of user interface and safety features: Update the user interface of the remote control unit to display real-time data and prediction results; integrate emergency stop, manual obstacle avoidance, and speed control functions into the automatic navigation module;

[0122] System testing and optimization: Test the performance of adaptive speed control and predictive maintenance in the actual operating environment; adjust the algorithm and model parameters according to the test results to optimize the system performance;

[0123] Maintenance and update: Regularly maintain and update the system to adapt to environmental changes and improve performance.

[0124] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A control system for a cable lifting device based on PLC, characterized in that: include: An adjustment module, used for controlling and adjusting the cable lifting device; Adaptive speed control module, which uses machine learning algorithms to analyze historical data and predict optimal speed settings to adapt to different cable types and tunnel conditions; Predictive maintenance module, which is used to predict equipment failure and maintenance requirements by collecting and analyzing equipment operation data, thereby reducing unexpected downtime; The autonomous navigation module is used to achieve autonomous navigation using sensors and algorithms, so that the cable lifting device can automatically plan a path in the tunnel and avoid obstacles. The specific steps are as follows: First, investigate the tunnel environment to obtain the size, shape, obstacle type and distribution of the tunnel, select sensors, detect the surrounding environment and obstacles, and deploy sensors on the cable lifting device; Collect data about the tunnel environment and obstacles through sensors, fuse the data from different sensors, and use the sensor data to build a map of the tunnel, including the location of obstacles and the geometry of the tunnel; Set up a path planning algorithm to calculate the best path from the starting point to the end point, and then use an obstacle avoidance algorithm to ensure that dynamic or static obstacles can be avoided in real time during the path planning process; Prioritize several paths planned by the path planning algorithm and the obstacle avoidance algorithm, and obtain the optimal path by collecting the optimal parameters of several planned paths and conducting a comprehensive analysis; The analysis steps of the optimal path in the autonomous navigation module are as follows: The optimal parameters include: Path length: the total length of the path; Path time: the time required to complete the path calculated based on the expected speed; Path curvature: the change in curvature of the path; Path complexity: After normalizing the number of turns and the frequency of direction changes in the planned path, a cone model is established with the number of turns as the base circle radius and the frequency of direction changes as the highest. The surface area of ​​the cone model is calculated and recorded as the complexity value, which is used as the standard for measuring path complexity. Obstacle proximity: The distance to the obstacle on the path. The average distance of the obstacles along the path is calculated and recorded as the obstacle distance value, which is used as the standard to measure the obstacle proximity. Energy consumption assessment: Evaluate the energy consumption required for route travel, and use the power consumption as the evaluation unit; The obtained path length, path time, path curvature, complexity value, obstacle value and power consumption are normalized and then input into the following formula: To obtain the optimal value YXZ, where lc, lt, lq, zf, zl and dx are path length, path time, path curvature, complexity value, obstacle value and power consumption respectively. They are respectively the preset weight coefficients of path length, path time, path curvature, complexity value, obstacle value and power consumption, and the calculated optimal value YXZ is used as the priority evaluation standard for measuring the planned path; Sort the preferred values ​​YXZ obtained from several planned paths by size, and select the planned path with the largest preferred value YXZ as the optimal path; The selected optimal path is converted into specific movement instructions of the cable lifting device through the navigation control algorithm, the tunnel environment is simulated in a virtual environment, the navigation performance is tested, and the autonomous navigation module is integrated with the existing PLC control system and sensors; Through the user interface, the operator can monitor the navigation status and provide manual intervention functions; the emergency stop, manual obstacle avoidance and speed control functions are integrated into the automatic navigation module.

2. A control system for a cable lifting device based on PLC according to claim 1, characterized in that: The adjustment module includes a main controller, a wheel drive unit, a conveying drive unit, a swing arm drive unit, a steering control unit, a lifting control unit, a lighting control unit and a remote control unit: The main controller is PLC; The wheel drive unit is used for speed control and data collection of the wheel drive motor; The speed control controls the speed of the wheel drive motors through the communication bus, the data acquisition collects the data of the wheel drive motors through the communication bus to monitor the status of the motors, and the remote control unit drives, stops or reverses all the wheel drive motors simultaneously; The transmission drive unit is used for speed control and data collection of the cable transmission drive motor; The speed control controls the speed of the cable conveying drive motor through the communication bus, and the data acquisition collects the data of the cable conveying drive motor through the communication bus to monitor the state of the motor. The speed of the cable conveying drive motor is consistent with that of the wheel drive motor. When the wheel drive motor is started, the cable conveying drive motor is started synchronously. The swing arm drive unit is used for speed control and data collection of the swing arm drive motor; The speed control controls the speed of the swing arm drive motor through the communication bus, and the data acquisition collects the data of the swing arm drive motor through the communication bus to monitor the state of the motor. The swing arm drive motor is driven and controlled through the wired remote control and wireless remote control in the remote control unit, and the swing arm drive motor is used to drive the swing arm to rotate clockwise or counterclockwise; The steering control unit includes a steering motor, a steering electric push rod and a steering contactor; By exchanging the positive and negative poles of the DC motor power supply through the steering contactor, the steering motor can be reversed to drive the steering electric push rod to push the wheel to steer. The steering contactor can be controlled by the wired remote control and wireless remote control in the remote control unit. The lifting control unit includes a lifting motor, a lifting electric push rod and a lifting contactor; By exchanging the positive and negative poles of the DC motor power supply through the lifting contactor, the lifting motor can be reversed to drive the lifting electric push rod to push the platform up and down. The lifting contactor can be controlled by the wired remote control and wireless remote control in the remote control unit. The lighting control unit includes an LED light strip and a normally open contactor; The LED light strip is controlled by wired remote control and wireless remote control in the remote control unit; The remote control unit includes wireless remote control and wired remote control, wherein the wireless remote control includes a wireless communication module and a remote control body, and the wired remote control is connected to the control terminal and sends instructions to the main controller through buttons; The wheel drive unit, conveying drive unit, swing arm drive unit, steering control unit, lifting control unit and lighting control unit are all connected to the main controller through communication lines. The main controller sends instructions according to the wired remote control or wireless remote control in the remote control unit and executes corresponding control.

3. A control system for a cable lifting device based on PLC according to claim 1, characterized in that: The specific operation steps of the adaptive speed control module to analyze historical data and predict the optimal speed setting through machine learning algorithm are as follows: First, the historical operation data of the cable conveying drive motor and the wheel drive motor are collected through the data acquisition function in the adjustment module, and then the recorded cable type, tunnel conditions, motor operating speed and load conditions are collected; Clean, standardize and extract features from the collected data for training machine learning models, and then select key data features as input features for predicting motor speed, including cable weight, diameter, tunnel length and curvature; Using a machine learning algorithm to train a model, such as regression analysis, decision tree, random forest, or neural network, to predict the optimal motor speed under different conditions, including cable-driven motors and wheel-driven motors; The accuracy and generalization ability of the model are then evaluated through cross-validation and test sets. Real-time data collection is implemented to monitor the current load and operating conditions. The motor speed is dynamically adjusted according to the collected real-time data and the trained model through the adaptive speed control algorithm. The adaptive speed control algorithm uses real-time data as input and outputs speed control instructions for the cable conveyor drive motor and the wheel drive motor. Integrate the above adaptive speed control algorithm into the PLC control system to ensure seamless cooperation between the algorithm and the motor speed control function, and then update the user interface in the remote control unit so that the user interface can display real-time data and prediction results and allow operators to manually adjust the motor speed; Implement monitoring measurements to ensure that the adaptive speed control does not affect equipment and operational safety, test the performance of the adaptive speed control in the actual operating environment, and adjust the algorithm and model parameters based on the test results.

4. A control system for a cable lifting device based on PLC according to claim 1, characterized in that: The specific operation steps of the predictive maintenance module to predict equipment failures and maintenance requirements by collecting and analyzing equipment operation data are as follows: First, identify the key components in the system and install sensors on them to collect temperature, vibration, sound, current and voltage parameters; Using IoT technology, sensor data is transmitted to a central monitoring system in real time and the collected data is stored in a database for historical analysis and trend tracking. Data analysis tools, including statistical analysis or machine learning algorithms, are then used to analyze equipment operation data and identify abnormal patterns and potential failures. Then, a prediction model is established and trained using historical fault data to predict equipment failures and maintenance needs. Real-time monitoring is implemented to continuously analyze equipment status. The prediction model is used to assess equipment health status and set thresholds for key parameters of the equipment. When the parameters exceed the normal range, an alarm is issued. The set thresholds are adaptively adjusted according to the performance factors of different equipment. When the prediction model indicates that the equipment is about to fail, maintenance tasks are automatically triggered. Based on the prediction results, maintenance plans and spare parts inventory are optimized to reduce unexpected downtime. Predictive maintenance information is integrated into the user interface of wired and wireless remote controls, allowing operators to understand the equipment status in real time. According to the instructions of predictive maintenance, preventive maintenance is performed, including replacing parts or adjusting equipment parameters, and maintenance and failure data are fed back to continuously optimize the predictive model. The predictive maintenance module is integrated with the existing PLC control system and communication network to ensure seamless collaboration.

5. A control system for a cable lifting device based on PLC according to claim 4, characterized in that: The specific process of the predictive maintenance module adaptively adjusting the set thresholds according to the performance factors of different equipment is as follows: Equipment performance parameters include: temperature, vibration, sound, current and voltage, speed and load, pressure, flow, torque and power, operating time and cycles, and fault codes; Using historical data and machine learning algorithms, the normal operating range is automatically identified and the corresponding threshold is set. With the adaptive algorithm, the threshold is dynamically adjusted according to the actual operating status of the equipment to adapt to different working conditions and loads. Combined with data analysis of multiple parameters, a comprehensive equipment health assessment is obtained to set the threshold more accurately. Implement a real-time monitoring system to continuously analyze equipment status and adjust thresholds based on real-time data. Through trend analysis, identify the trend of parameter changes over time and adjust thresholds accordingly. The impact of seasonal changes and environmental conditions on equipment performance, adjust thresholds accordingly; Integrate the predictive model and use the model output as the basis for adjusting the threshold. Establish a feedback mechanism to allow operators and maintenance personnel to report the accuracy of the threshold based on actual experience and make adjustments, learn from each maintenance and failure, and continuously optimize the threshold setting.

6. A control method for a control system of a PLC-based cable lifting device according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1. System construction and component definition: Build a PLC-based cable lifting control system and define key components including the main controller, drive unit, control unit, and remote control unit; S2. Data acquisition: realize motor speed control and data acquisition, monitor and record motor status, and provide basic data for subsequent control; S3. Adaptive speed control module implementation: Use historical operation data to train the machine learning model to achieve adaptive speed control function that dynamically adjusts the motor speed according to real-time data; S4. Implementation of predictive maintenance module: collect operating parameters of key components through sensors, use IoT technology for real-time monitoring, and establish predictive models to predict failures and maintenance needs; S5. Implementation of autonomous navigation module: Survey the tunnel environment, deploy sensors to collect data, build maps, and implement autonomous navigation to plan paths and avoid obstacles; S6. Path planning and obstacle avoidance: Apply path planning and obstacle avoidance algorithms to calculate the best path and ensure real-time response to dynamic and static obstacles during navigation; S7. Path optimization parameter analysis: Comprehensively consider the optimization parameters and calculate the optimal value to evaluate and select the optimal path; S8. Path selection and navigation control: Select the optimal path based on the preferred value, convert it into specific movement instructions, and integrate it into the PLC control system to achieve automatic navigation; S9. User interface and safety feature integration: Update the user interface to display real-time data, integrate emergency stop and manual obstacle avoidance functions, and enhance the interactivity and safety of the system; S10. System testing and optimization: Conduct comprehensive testing in the actual operating environment, optimize system performance based on feedback, and ensure the effectiveness of adaptive speed regulation and predictive maintenance.

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