Remote control method and system for hoisting equipment in electrified environment

Abstract

The embodiment of the invention provides a remote control method and system for hoisting equipment in an electrified environment. The method is applied to the technical field of Internet of Things, and comprises the following steps: acquiring real-time electrical data of hoisting equipment working in an electrified environment, inputting the real-time electrical data into a preset anomaly detection model, and determining whether electrical anomaly exists or not; if yes, determining an electrical isolation protection scheme through a preset anomaly detection model, and performing electrical isolation on the hoisting equipment according to the electrical isolation protection scheme; when a preset updating time interval is reached, re-determining whether the electrical abnormality still exists or not; if not, determining a current suggestion through a preset anomaly detection model and sending the current suggestion to the remote control platform; and if a recovery instruction of the remote control platform is received, the hoisting equipment is controlled to continue working. According to the scheme, fault diffusion and equipment damage can be reduced. The equipment safety and the operation efficiency are improved, the requirement of manual intervention is reduced, and the intelligent level of management and maintenance is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of Internet of Things, and particularly to a method and system for remotely controlling a hoisting device in a live environment. Background Art

[0002] During the construction and maintenance of power facilities, hoisting devices often need to operate in a live environment to ensure the continuity and stability of power supply. However, operations in a live environment pose extremely high safety risks. Operators directly exposed to the high-voltage environment may suffer electric shock or cause serious accidents such as short circuits. Therefore, it is particularly important to remotely control hoisting devices in a live environment.

[0003] Currently, the control method of hoisting devices in a live environment mainly uses manual operation. Manually operating a hoisting device usually relies on the operator directly operating the control device of the device (such as a joystick, button, or touch screen, etc.). The operator needs to manually adjust the control device according to the on-site situation and operation requirements to precisely control the movement trajectory, speed, and force of the hoisting device to complete tasks such as hoisting, handling, or installation.

[0004] However, manual operation focuses on data transmission and the sending of simple control instructions, lacks the monitoring of real-time electrical data and the function of abnormal detection, cannot detect and handle electrical abnormalities in a timely manner, and there are certain safety hazards. Summary of the Invention

[0005] To solve the deficiencies of the prior art, the present disclosure provides a method and system for remotely controlling a hoisting device in a live environment. The present disclosure solves the problem that the existing manual operation of hoisting devices focuses on data transmission and the sending of simple control instructions, lacks the monitoring of real-time electrical data and the function of abnormal detection, cannot detect and handle electrical abnormalities in a timely manner, and there are certain safety hazards.

[0006] According to a first aspect of the present disclosure, there is provided a method for remotely controlling a hoisting device in a live environment, including: obtaining real-time electrical data of the hoisting device operating in a live environment, inputting the real-time electrical data into a preset abnormal detection model to determine whether there is an electrical abnormality;

[0007] If there is an electrical abnormality, determining an electrical isolation protection plan through the preset abnormal detection model, and performing electrical isolation on the hoisting device according to the electrical isolation protection plan;

[0008] Sending the real-time electrical data and the electrical isolation protection plan to a remote control platform, and updating the real-time electrical data at a preset update time interval, inputting the updated real-time electrical data into the preset abnormal detection model to determine whether there is still an electrical abnormality;

[0009] If there is no electrical abnormality, determine the current suggestion through a preset abnormality detection model, and send the current suggestion to the remote control platform;

[0010] If a recovery instruction from the remote control platform is received, control the hoisting equipment to continue working according to the recovery instruction.

[0011] According to a second aspect of the present disclosure, there is provided a remote control system for a hoisting equipment in a live environment, which is used to execute the method as described in the first aspect, including: an abnormality determination module, configured to obtain real-time electrical data of the hoisting equipment working in a live environment, input the real-time electrical data into a preset abnormality detection model, and determine whether there is an electrical abnormality;

[0012] An electrical isolation module, configured to if there is an electrical abnormality, determine an electrical isolation protection scheme through a preset abnormality detection model, and perform electrical isolation on the hoisting equipment according to the electrical isolation protection scheme;

[0013] A continuous determination module, configured to send the real-time electrical data and the electrical isolation protection scheme to the remote control platform, update the real-time electrical data at a preset update time interval, input the updated real-time electrical data into a preset abnormality detection model, and determine whether there is still an electrical abnormality;

[0014] A suggestion generation module, configured to if there is no electrical abnormality, determine the current suggestion through a preset abnormality detection model, and send the current suggestion to the remote control platform;

[0015] An equipment control module, configured to if a recovery instruction from the remote control platform is received, control the hoisting equipment to continue working according to the recovery instruction.

[0016] According to a third aspect of the present disclosure, there is provided an electronic device, which includes: a memory and a processor, a computer program is stored on the memory, and when the processor executes the program, the method as described above is implemented.

[0017] In a method and system for remotely controlling a hoisting equipment in a live environment provided as above, the embodiments of the present disclosure can quickly detect electrical abnormalities and automatically perform isolation protection, reducing fault spread and equipment damage. The remote control platform ensures that the operator can monitor the equipment status in real time and intervene, automatically generate recovery suggestions or protection measures, and minimize downtime. It not only improves the equipment safety and operation efficiency, but also reduces the need for manual intervention and improves the intelligent level of management and maintenance. Description of the Drawings

[0018] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It shows a schematic flowchart of a method for remotely controlling a hoisting device in a live environment according to an embodiment of the present disclosure;

[0020] Figure 2 It shows a schematic flowchart of a method for remotely controlling a hoisting device in a live environment according to an embodiment of the present disclosure;

[0021] Figure 3 It shows a schematic block diagram of a system for remotely controlling a hoisting device in a live environment according to an embodiment of the present disclosure;

[0022] Figure 4 It shows a block diagram of an exemplary electronic device according to an embodiment of the present disclosure. Detailed implementation manners

[0023] Now, various exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. It should be noted that: unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure.

[0024] Those skilled in the art can understand that terms such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices, or modules, etc., and neither represent any specific technical meaning nor indicate an inevitable logical order between them. It should also be understood that in the embodiments of the present disclosure, "a plurality" may refer to two or more, and "at least one" may refer to one, two, or more. It should also be understood that for any component, data, or structure mentioned in the embodiments of the present disclosure, without clear definition or contrary indication in the context, it can generally be understood as one or more. In addition, the term "and / or" in the present disclosure is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present disclosure generally represents an "or" relationship between the associated objects before and after. It should also be understood that the present disclosure emphasizes the differences between various embodiments, and the same or similar parts can be referred to each other. For the sake of brevity, they will not be repeated one by one.

[0025] Meanwhile, it should be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn in actual proportional relationships. The following description of at least one exemplary embodiment is actually merely illustrative and in no way restricts the present disclosure, its application, or its use. Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods, and devices should be regarded as part of the specification. It should be noted that: like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts fall within the scope of protection of the present disclosure.

[0027] Figure 1 The figure is a schematic flowchart of a method for remotely controlling a hoisting device in a live environment provided for an embodiment of the present disclosure. As Figure 1 shown, the method includes:

[0028] S101, obtaining real-time electrical data of the hoisting device working in a live environment, inputting the real-time electrical data into a preset anomaly detection model, and determining whether there is an electrical anomaly.

[0029] A hoisting device can be a device used to carry, lift, and move heavy objects, such as a tower crane, a crane, a bridge crane, etc.

[0030] Real-time electrical data can refer to data regarding the electrical state generated by the hoisting device during operation. Specifically, it can include current: the working current of the hoisting device motor and other electrical components. Voltage: the voltage data of each electrical component of the device (such as motors, control systems, etc.). Power: the power consumption or output of the device. Frequency: the frequency data of the device operation, such as the motor speed or other key frequencies. Temperature: the temperature of the device electrical components such as motors, batteries, and controllers.

[0031] The preset anomaly detection model can be a model trained through data analysis methods (such as machine learning, statistical analysis, etc.), aiming to identify potential abnormal situations from real-time electrical data. These anomalies can include: overloading of electrical equipment. Abnormal fluctuations in current and voltage. Faults in electrical components (such as motor faults, short circuits, etc.). Thermal overload or temperature anomalies.

[0032] Sensors can be installed on the electrical components of the hoisting equipment (such as motors, controllers, transformers, etc.) to monitor electrical data such as current, voltage, power, frequency, and temperature in real time. These real-time electrical data are collected through a data acquisition system (such as PLC, SCADA system, etc.). The real-time electrical data obtained are input into a preset anomaly detection model. The model analyzes these data in real time or at regular intervals and identifies anomalies through rules or algorithms.

[0033] S102. If there is an electrical anomaly, determine an electrical isolation protection plan through the preset anomaly detection model, and perform electrical isolation on the hoisting equipment according to the electrical isolation protection plan.

[0034] The electrical isolation protection plan can be a strategy for protecting equipment, operators, and systems from further damage by disconnecting electrical connections or taking other electrical isolation measures when an electrical device malfunctions. The purpose of this plan is to immediately take measures to cut off the power supply or isolate the faulty part in case of an electrical fault, preventing further damage to the electrical equipment or causing more serious faults. Specifically, the electrical isolation protection plan can include power-off protection: automatically cutting off the power supply of the equipment to prevent equipment damage caused by excessive current or abnormal voltage. Electrical switch operation: using automatic circuit breakers, disconnecting switches, or relays to automatically cut off the electrical circuit when a fault occurs. Overload protection: setting up an overload protection device to prevent electrical equipment or cables from being damaged due to overcurrent. Electrical overvoltage / undervoltage protection: automatically performing electrical isolation if the voltage is abnormal (such as overvoltage or undervoltage) to ensure the safety of electrical equipment. Grounding protection: performing fault protection through a grounding device to prevent electric leakage or electrical breakdown. Temperature protection: when the temperature of the equipment exceeds the safety threshold, take measures to cut off the power supply to prevent overheating damage to the equipment.

[0035] When the model determines that there is an electrical anomaly in the equipment, the system automatically generates an electrical isolation protection plan according to the preset rules or parameters. According to the determined electrical isolation protection plan, the system will execute isolation protection measures on the hoisting equipment. For example: starting an automatic circuit breaker or disconnecting switch to cut off the power supply. Closing relevant parts of the electrical system (such as the motor power supply) to avoid further damage. Possibly starting an alarm system to prompt the operator that there is an anomaly in the equipment.

[0036] S103. Send the real-time electrical data and the electrical isolation protection plan to the remote control platform, update the real-time electrical data at the preset update time interval, and input the updated real-time electrical data into the preset anomaly detection model to determine whether there is still an electrical anomaly.

[0037] The remote control platform can be a centralized, remotely accessible control system, usually a cloud-based or on-premises server system, for monitoring, managing, controlling, and diagnosing faults in the operating status of mechanical equipment (such as lifting equipment). It connects to the equipment through a network, receives equipment data in real time, allows operators to remotely monitor the equipment status, perform maintenance operations, and execute instructions or take safety measures when necessary.

[0038] The protection scheme can be transmitted as a control signal to the remote control platform so that the operator can obtain timely feedback and can manually intervene when necessary. According to the preset update time interval, the real-time electrical data on the lifting equipment is regularly requested. At each update, the real-time electrical data is re-input into a preset anomaly detection model to confirm whether there is still an electrical anomaly in the equipment.

[0039] Based on the above technical solution, optionally, after inputting the updated real-time electrical data into the preset anomaly detection model to determine whether there is still an electrical anomaly, the method further includes:

[0040] If there is an electrical anomaly, re-determine the electrical isolation protection scheme through the preset anomaly detection model, and continue to perform electrical isolation on the lifting equipment according to the electrical isolation protection scheme;

[0041] Update the real-time electrical data every time the preset update time interval is reached, input the updated real-time electrical data into the preset anomaly detection model to determine whether there is still an electrical anomaly, and re-determine the electrical isolation protection scheme in the case of an electrical anomaly, and continue to perform electrical isolation on the lifting equipment according to the electrical isolation protection scheme until there is no electrical anomaly. Determine the current recommendation through the preset anomaly detection model and send the current recommendation to the remote control platform.

[0042] In this solution, the preset update time interval can be the time interval for the system to regularly update and check the electrical status of the lifting equipment, usually determined by the working cycle of the equipment, the fault detection requirements, or the system performance requirements. This time interval should be optimized according to the working environment of the equipment and the stability of the electrical system to ensure timely detection of potential electrical anomalies and avoid the risk of long-term undetected.

[0043] If the anomaly detection model determines that there is an electrical anomaly, the model will re-determine an appropriate electrical isolation protection scheme (such as power off, current limiting, alarm, etc.) based on the current electrical data, and immediately implement electrical isolation of the lifting equipment to prevent damage from expanding. The system will update the real-time electrical data according to the preset update time interval (for example, every 5 minutes or every 10 minutes). At this time, the real-time data will be input into the anomaly detection model to continue to detect whether there are new electrical anomalies. If the updated electrical data still indicates that there is an anomaly, the model will recalculate and output a new electrical isolation protection scheme, continue to isolate and protect the equipment, and ensure the safety of the equipment. When the model determines that there is no electrical anomaly, the system will generate recovery suggestions (such as resuming equipment operation, lifting electrical isolation, etc.), and send the suggestions to the remote control platform to guide the operator to take corresponding measures according to the suggestions.

[0044] This solution ensures that the lifting equipment can respond quickly and receive timely protection when electrical anomalies occur. At the same time, the system will continuously update and adjust the protection strategy until the anomaly disappears and normal operation is restored, thereby maximizing the safety and stability of the equipment.

[0045] S104: If there is no electrical abnormality, a current suggestion is determined through a preset abnormality detection model, and the current suggestion is sent to the remote control platform.

[0046] Current recommendations can be specific operations or decision recommendations generated based on the detection results and the equipment operating status after real-time electrical data monitoring and anomaly detection of the lifting equipment. Its purpose is to optimize equipment operation, ensure equipment safety, and improve efficiency. Depending on the results of electrical anomaly detection, current recommendations can include the following types: Recovery operation recommendations: If the equipment electrical system is detected to be normal, it is recommended to resume or continue normal operation. For example, the equipment can be restored to the predetermined working mode and continue the lifting operation. Safety inspection recommendations: If the equipment working environment is detected to be good, the system may recommend routine inspections or preventive maintenance to ensure the long-term safe operation of the equipment. Performance optimization recommendations: During the operation of the equipment, there may be recommendations to improve performance or reduce energy consumption, such as adjusting the lifting speed, optimizing load distribution, and selecting a more energy-saving working mode. Preventive maintenance recommendations: Based on the health status of the equipment, the model may recommend regular inspections, component replacements, or other preventive measures to reduce the risk of potential failures. Warning or monitoring recommendations: If the equipment status is normal but is in a high-risk or marginal condition, the system may recommend continued monitoring or set early warnings to respond to potential problems in a timely manner.

[0047] The real-time electrical data of the device is input into a preset anomaly detection model. By analyzing this data, the model determines whether there is an electrical anomaly. If no anomaly is found, the model proceeds to the next step and generates current suggestions. These suggestions usually depend on the real-time electrical data of the current device. For example: Recovery suggestion: The device is in normal condition and the current operation can continue. Operation optimization suggestions: For example, optimize the load, adjust the working parameters, adjust the working speed, etc. to improve efficiency. Preventive maintenance suggestions: Based on the electrical data of the device, propose inspection or maintenance suggestions. Safety monitoring suggestions: For example, the system may suggest continuously monitoring the device operation, especially if the device is operating at a high load or under marginal conditions. The current suggestions are sent to the remote control platform, and the operator can take appropriate measures according to these suggestions.

[0048] The training process of the preset anomaly detection model includes:

[0049] The training process of the preset anomaly detection model can indeed be divided into two modules: the anomaly determination and solution output module, and the suggestion output module.

[0050] The labels of the anomaly determination and solution output module should be divided into two parts: the anomaly label and the solution label. These two labels are used to identify whether there is an anomaly and the specific electrical isolation protection solution to be taken when an anomaly occurs.

[0051] The anomaly label is used to indicate whether the current electrical data indicates an abnormal situation, usually a binary classification label.

[0052] Normal (0): Indicates that there is no anomaly in the current electrical data and the device is in normal working condition.

[0053] Abnormal (1): Indicates that there is an anomaly in the current electrical data and the device has experienced some electrical fault or performance degradation.

[0054] The generation of the anomaly label is based on the analysis of historical electrical data to label whether an abnormal event has occurred. These anomalies can be: Excessive current (such as a short circuit). Voltage fluctuation outside the range (such as unstable voltage). Abnormal temperature (such as overheating). Other electrical faults (such as open circuit, short circuit, etc.).

[0055] The scheme label is used to indicate what electrical isolation protection scheme the model should adopt when an anomaly is detected. Each anomaly label (i.e., the situation where an anomaly exists) corresponds to one or more electrical isolation protection schemes. Common scheme labels include: power-off protection (e.g., when the device current is too large, the model recommends immediately disconnecting the power supply to avoid further damage). Current limiting (e.g., when the current exceeds the safe range, the model may suggest limiting the current to prevent overload). Alarm prompt (e.g., if the device temperature is too high, the model can suggest sending an alarm signal to remind the operator to check the device status). Other protection measures (such as automatically adjusting the voltage, isolating faulty components, etc.).

[0056] In the training dataset, each sample (electrical data) needs to be labeled with an anomaly label and a scheme label. If an anomaly occurs in the device, the anomaly label will be marked as "anomaly", and according to the type of anomaly, the corresponding scheme label will be output, such as "power-off protection" or "current limiting". If there is no anomaly in the device, the anomaly label will be marked as "normal", and there is no corresponding protection scheme.

[0057] The training process is a supervised learning process, and the goal is to let the model learn to identify anomaly situations from historical data and generate appropriate electrical protection schemes. Input data: The input of the model is processed electrical data (such as current, voltage, temperature, etc.). These data are usually time series data, which can be data of a single sampling point or window data of historical sampling (i.e., electrical data over a period of time). Output labels: Anomaly label (0: normal, 1: anomaly). Scheme label (protection scheme generated according to the type of anomaly). Then, the labeled historical electrical data is used for training. The model determines whether an anomaly exists by learning the relationships between different electrical parameters. Common algorithms include classification algorithms (such as support vector machine SVM, decision tree, random forest, neural network, etc.). During the training process, the model learns to identify the patterns of electrical anomalies from the input data, and the training process is supervised learning. Once the model determines an anomaly (label is 1), it needs to output the corresponding electrical protection scheme (scheme label) according to the type of anomaly. For example, if the model detects that the current exceeds the standard, the scheme label may be "power-off protection", and if it detects that the temperature is too high, it may be "alarm prompt". The training of the scheme output also adopts supervised learning to learn how to match appropriate protection schemes from anomaly data. Common algorithms can be multi-classification algorithms (such as decision tree, random forest, etc.). During the training process, it is necessary to regularly evaluate the performance of the model: Accuracy: The ratio of the model correctly classifying anomaly and normal data. Recall rate: The ratio of the model being able to correctly identify anomalies. F1 score: An indicator that combines accuracy and recall rate. False positive rate and false negative rate: The frequencies of false positives and false negatives generated by the model. By evaluating these indicators, the hyperparameters of the model (such as learning rate, depth of the tree, etc.) can be optimized and the quality of the training data can be improved.

[0058] Suggestion Output Module:

[0059] The goal of this module is to generate relevant equipment operation suggestions when no abnormalities are detected. The content of the suggestions usually includes equipment optimization, preventive maintenance, load adjustment, etc.

[0060] Training Data: Historical Electrical Data (Normal State): Only use the electrical data during normal operation, including current, voltage, temperature, etc. Suggestion Labels: For each normal data sample, define appropriate suggestion labels. Possible suggestion labels include: Continue to Operate: The equipment is in normal working condition and can continue to operate. Optimization Operation Suggestions: Such as adjusting the load, adjusting operation parameters, performing periodic maintenance, etc. Safety Monitoring: It is recommended to continue monitoring the equipment operation, especially in high-load or critical mission situations.

[0061] Training Process:

[0062] Similar to the anomaly module, the data is processed such as standardized and denoised. Then extract relevant features from the electrical data during normal operation. When training the model, the goal is to predict appropriate operation suggestions based on historical normal data. The model can use classification methods (such as random forest, neural network) to learn the equipment operation suggestions under different conditions. The trained model will generate current suggestions based on real-time data. If the equipment is in a normal state, the model will put forward corresponding optimization or monitoring suggestions according to factors such as the load, temperature, and voltage of the equipment. Evaluate the accuracy of the model and the rationality of the generated suggestions. Use historical data for verification to ensure the adaptability and reliability of the model in different environments.

[0063] Then integrate these two modules into a complete model:

[0064] Input Layer: Obtain real-time electrical data (such as voltage, current, temperature, etc.) from sensors.

[0065] Anomaly Determination Layer: First, determine whether there are anomalies in the data through the anomaly determination and solution output module.

[0066] Solution Output Layer: If the data is determined to be abnormal, then the anomaly determination and solution output module determines the protection solution next; if the data is normal, skip the solution output and reach the suggestion output module.

[0067] S105, if a recovery instruction from the remote control platform is received, control the hoisting equipment to continue working according to the recovery instruction.

[0068] The recovery instruction can be an instruction sent by the remote control platform based on suggestions or diagnostic results after receiving the electrical anomaly report of the equipment, used to indicate whether the protection measures can be lifted and the equipment can be restored to normal operation.

[0069] When the remote control platform issues a recovery command, the control system or monitoring platform of the equipment will receive the recovery command from the remote control platform through the network. The command content may include restoring power supply, removing fault isolation, adjusting the operation mode, etc. After receiving the recovery command, the system will first verify whether the recovery conditions are met, such as: whether the electrical abnormality is completely eliminated (for example, the overcurrent has been restored to a safe range, the equipment temperature has returned to normal, etc.). Whether all protection measures have been completely lifted and whether the safety of the equipment is guaranteed. If the recovery conditions are not met (for example, the equipment is still in a faulty state), the system may prompt the remote control platform and suggest delaying the recovery. If the conditions are met, the system will control the behavior of the lifting equipment by executing the command. The equipment task may have been set in advance, including the task path, operation steps, parameter settings and other information. After recovery, the system will obtain the status of the current task, determine whether the task execution is interrupted, and obtain the interruption point. If the task has been partially completed, the system will check the task progress and continue to execute from the interruption point. If the task has not yet started, the device will start the task from the beginning. After ensuring that the equipment is restored to normal, the device will continue to execute the operation according to the path, steps, and parameters set in the task. For example: Path execution: The equipment will continue to work according to the original path to ensure that the task is not affected by the recovery process. The equipment will continue according to the predetermined steps of the task, such as lifting, transportation, positioning, etc., to ensure the continuity of operations.

[0070] In an embodiment of the present application, real-time electrical data of the hoisting equipment working in a live environment is obtained, and the real-time electrical data is input into a preset abnormality detection model to determine whether there is an electrical abnormality; if there is an electrical abnormality, the electrical isolation protection scheme is determined by the preset abnormality detection model, and the hoisting equipment is electrically isolated according to the electrical isolation protection scheme; the real-time electrical data and the electrical isolation protection scheme are sent to the remote control platform, and the real-time electrical data is updated when the preset update time interval is reached, and the updated real-time electrical data is input into the preset abnormality detection model to determine whether there is still an electrical abnormality; if there is no electrical abnormality, the current suggestion is determined by the preset abnormality detection model, and the current suggestion is sent to the remote control platform; if a recovery instruction is received from the remote control platform, the hoisting equipment is controlled to continue working according to the recovery instruction. Through the above-mentioned remote control method for hoisting equipment in a live environment, electrical abnormalities can be quickly discovered and isolation protection can be automatically performed, reducing the spread of faults and equipment damage. The remote control platform ensures that the operator can monitor the equipment status in real time and intervene, automatically generate recovery suggestions or protection measures, and minimize downtime. It not only improves equipment safety and operational efficiency, but also reduces the need for manual intervention and improves the level of intelligence in management and maintenance.

[0071] Based on the above technical solution, optionally, after sending the current suggestion to the remote control platform, the method further includes:

[0072] Obtaining the current path, current position, and target position of the hoisting equipment, as well as first environmental data and electrical load information of each area, inputting the current path, current position, target position, first environmental data, and electrical load information of each area into a preset path planning model to determine whether the path needs to be replanned;

[0073] If the path needs to be replanned, the target path is output through the preset path planning model;

[0074] Accordingly, the current suggestion is sent to the remote control platform, including:

[0075] Sending the current suggestion and the target path to the remote control platform;

[0076] Correspondingly, if a recovery instruction is received from the remote control platform, the hoisting equipment is controlled to continue working according to the recovery instruction, including:

[0077] If a recovery instruction is received from the remote control platform, the hoisting equipment is controlled to continue working according to the recovery instruction and the target path.

[0078] In this solution, the current path may be the path or trajectory that the lifting equipment is executing, indicating the line or planned trajectory between the current position of the equipment and the target position.

[0079] The current position may be the actual position of the lifting equipment, which is usually provided by a positioning system of the equipment (such as GPS, IMU, etc.).

[0080] The target location can be the intended or planned destination of the lifting equipment, or the final target location of the equipment.

[0081] The first environmental data may refer to all dynamic and static information related to the working environment of the lifting equipment, which affects the path planning and safe operation of the equipment. The first environmental data includes: Obstacle data: static and dynamic obstacles in the working area, such as buildings, other equipment, cargo stacking, etc. Path planning needs to avoid these obstacles. Personnel location data: The location of the staff in the work area. Path planning needs to avoid areas where the equipment is close to the staff to ensure safety. Other equipment status data: The working status (whether it is running) and location of other equipment (such as lifting equipment, transport vehicles, etc.) in the working area to avoid collisions or mutual interference between equipment. Terrain data: The flatness of the ground, slope type, soil conditions and other conditions that affect the movement of equipment. Path planning needs to consider these factors to ensure that the equipment can pass smoothly.

[0082] The electrical load information of each area can be the load data of the electrical system within the equipment working area, including information such as current, voltage, and power in each area. The electrical load affects the power supply, load capacity, and safety of the equipment, and may affect the path planning strategy.

[0083] The preset path planning model can be an algorithm model used to automatically calculate and optimize the path of the hoisting equipment from the current position to the target position. This model is based on the working environment data and electrical load information of the equipment, and comprehensively considers factors such as obstacles, personnel, other equipment, terrain, and electrical load to generate the optimal path. The preset path planning model usually includes: Path optimization algorithms: such as the A* algorithm, Dijkstra algorithm, etc., used to find the shortest and safest path. Obstacle detection and avoidance mechanism: Dynamically adjust the path according to real-time environmental data to avoid obstacles and dangerous areas. Dynamic adjustment: Real-time update the path planning according to the current position of the equipment and environmental changes.

[0084] The target path can be the calculation result output by the path planning model, which refers to the optimal path of the hoisting equipment from the current position to the target position. This path takes into account factors such as the current state of the equipment, the working environment, and the electrical load of each area to ensure that the equipment can reach the target position under the premise of maximizing safety and efficiency.

[0085] Before starting work, the device usually receives task information, which includes the current path, the tasks the device will perform next, and the target location. When it is necessary to obtain the current path and target location, they can be directly called from the device locally. The current location can be obtained in real time through a positioning system (such as GPS, RTLS, sensors, etc.). The first environmental data can be obtained through devices such as sensors, radars, video surveillance, and LiDAR. The electrical load information of each area can be collected in real time through devices such as installed sensors (such as current and voltage sensors), smart meters, SCADA systems, or on-site management systems. Then, the current path, current location, target location, first environmental data, and electrical load information of the lifting device are input into a preset path planning model. The model will calculate and judge based on this information: Whether there are obstacles that need to be avoided or the path needs to be re-planned. Whether there are new electrical load restrictions or safety requirements that require adjusting the path. Whether the environment has changed (such as new obstacles, personnel changes, etc.) and affects the existing path. If the path needs to be re-planned, the path planning model will re-calculate based on the new input data (such as new obstacles, updated electrical load information, etc.) and output a new target path. The new path will comprehensively consider the safety, efficiency, and operation requirements of the device to ensure that the device can complete the task smoothly. Then, the current suggestions and the target path are sent to the remote control platform through wireless communication technology. By receiving the recovery instructions from the remote control platform, the system parses the instruction content, obtains the current device status and the target path, adjusts the device operation, and ensures the normal operation of the device through real-time monitoring. The recovery instructions can instruct the device to continue to execute the task according to the target path, ensuring that the device resumes its normal working state and continues to complete the task.

[0086] The training process of the preset path planning model includes:

[0087] Collect historical data: Equipment path data: Collect the historical paths of the equipment when performing tasks, including the starting position, target position, key positions on the path, and task status (e.g., start, end, failure, etc.) of each path. Real-time equipment information: Current position: The current position of the equipment during operation. Target position: The final destination or target of the equipment task. Current path: The path that the equipment is currently executing or the planned path. Environmental data (first environmental data): Obstacle data: Include static or dynamic obstacles around the equipment, such as buildings, goods, etc. Terrain data: Such as ground type (sand, hard ground, slope, etc.), which affects the path selection. Personnel position: The position of personnel within the working area to avoid conflicts between path planning and personnel. Other equipment status: The position information of the equipment and other objects (such as lifting equipment, transport vehicles, etc.) to avoid collisions or interference between equipment. Electrical load information: The electrical load status of each area, which affects the path selection. For example, avoid the equipment from entering high-load areas to prevent overload or electrical faults. Then extract features from the above data for model use. Possible features include: Path features: The current position, target position, and task status of the equipment. Environmental features: Obstacles, terrain, personnel, other equipment positions, etc. Electrical load features: The electrical load conditions of each area, possible overload areas. Equipment features: Equipment type, load status, speed limit, etc. Then label the tags of the path planning results in the historical data for training the model. Path planning tags: According to the task completion situation and environmental conditions, label the following types of tags: Valid path tag: Valid path (1): The current path is not affected by any problems such as obstacles, interference, or abnormal electrical load, and can continue to be executed. Invalid path (0): There are some problems with the current path and the path needs to be re-planned. Invalid paths usually occur for the following reasons: The path is blocked by obstacles or the equipment collides with obstacles. The electrical load is too large, and the current path will cause the equipment to enter an overload area, which may lead to equipment failure or safety hazards. Dynamic environmental changes, such as the movement of personnel or other equipment, make the current path unavailable. Re-plan path tag: Re-plan path (1): When the equipment path is invalid, a new path needs to be generated according to the current environmental information. This usually occurs when the path encounters unexpected obstacles (such as obstacles, personnel, etc.), or the equipment enters an unsafe electrical load area. Do not re-plan path (0): When the path is valid, the equipment can continue to execute the current path without re-planning. Path generation tag (new path): Path generation tag: When the model determines that the path is invalid, the tag should include the generated new target path, or the re-adjusted path. This path should take into account the following factors: Avoidance of obstacles. Avoidance of electrical load areas to ensure that the equipment does not enter high-load areas. Avoiding collisions or affecting other equipment and personnel. Optimizing the path within the safe area to avoid increasing unnecessary time or energy consumption.

[0088] Labeling during the training process: Labeling for normal paths (valid paths): For valid paths in historical data, label them as "1", indicating that there are no problems with this path during execution and it can be continued to be used. The corresponding label is that there is no need to re-plan the path (0), indicating that this path can be directly executed. Labeling for invalid paths (paths that need to be re-planned): For invalid paths in historical data, label them as "0", indicating that there are problems with this path and it needs to be re-planned. The corresponding label is that a re-planning of the path is required (1), and the label of the new path is output. The label of the new path can be generated based on environmental data and device status to ensure avoidance of interference from obstacles, overloaded areas, personnel, etc.

[0089] The training steps are as follows:

[0090] Input data: Includes the current path of the device, current location, target location, obstacle information, electrical load information, etc. Output labels: The goal of the model is to output two main labels: whether a re-planning of the path is required (1: required, 0: not required). When a re-planning of the path is required, the new target path is output. This path may need to be dynamically calculated according to the current environment through algorithms (such as A*, Dijkstra, reinforcement learning, etc.). Construction of the training set and validation set: Extract samples of valid paths and invalid paths from historical data. Label the validity of the paths, and generate new paths based on the invalid paths to ensure that the model can learn how to adjust the path in different environments. Model optimization: Use models suitable for path planning (such as reinforcement learning, Deep Q-Network (DQN), etc.) for training. During the training process, the model continuously optimizes its ability to generate new paths according to different inputs (path, obstacles, electrical load, etc.). Online learning: The trained path planning model can continuously learn through real-time data and adjust the path planning strategy to cope with a dynamically changing environment (such as changes in electrical load, movement of obstacles, etc.).

[0091] In this solution, by obtaining the device path and electrical load information in real time, it is possible to evaluate in real time whether there are interference factors or obstacles, and adjust the path in a timely manner, thereby avoiding unnecessary stagnation or detours. This can improve the working efficiency of the device and shorten the operation time. During the operation of the device, some unexpected situations may occur, such as the appearance of obstacles and changes in environmental conditions. By adjusting the path in real time through the model, the device can flexibly respond to environmental changes to ensure that the task can be successfully completed without being affected by external interference.

[0092] Based on the above technical solution, optionally, after controlling the lifting device to continue working according to the recovery instruction and the target path, the method further includes:

[0093] If it is recognized that there are changes in the first environmental data and / or the electrical load information of each area, re-acquire the first environmental data, the electrical load information of each area, and the current position of the lifting device, and input the re-acquired first environmental data, the re-acquired electrical load information of each area, the re-acquired current position, the target path, and the target position into a preset path planning model to determine whether a path needs to be re-planned;

[0094] If a path needs to be re-planned, update the target path through the preset path planning model and send the target path to the remote control platform;

[0095] If a path replacement instruction transmitted by the remote control platform is received, control the lifting device to continue working according to the updated target path.

[0096] In this solution, the path replacement instruction can be a command sent by the remote control platform to the lifting device, aiming to instruct the device to make adjustments and executions according to the new or updated target path.

[0097] If it is recognized that the first environmental data and / or the electrical load information of each area have changed, the system will re-acquire the latest first environmental data, the electrical load information of each area, and the current position of the lifting device, and input these updated data together with the target path and the target position into a preset path planning model. The model will judge whether a path needs to be re-planned according to these input data. If the path needs to be adjusted, the system will update the target path and send the new path to the remote control platform; when a path replacement instruction transmitted by the remote control platform is received, the system will control the lifting device to continue to execute tasks according to the updated target path.

[0098] In this solution, by adjusting the path in real time according to changes in environmental data (such as electrical load, obstacles, etc.), it is ensured that the lifting device can avoid possible dangers or obstacles, and reduce the interference or accident risks brought by environmental factors.

[0099] Figure 2 It is a schematic flow chart of a method for remotely controlling a lifting device in a live environment provided for an embodiment of the present disclosure. As Figure 2 shown, the method includes:

[0100] S201, obtain the load force data, equipment mass data, natural frequency data, vibration frequency quantity, vibration amplitude of each vibration frequency, and natural frequency of each vibration frequency of the lifting device, and obtain the second environmental data and the vibration time, and determine the damping coefficient according to the load force data and the second environmental data.

[0101] Load force data can refer to the external load or load data that the lifting equipment is subjected to during operation. This includes the weight and load of the lifting equipment when lifting objects, as well as any external forces that may be encountered during movement. This data helps analyze the load capacity and working status of the equipment, and helps design appropriate protective measures and optimize equipment operating conditions.

[0102] Equipment mass data can refer to the mass (or mass distribution) of the lifting equipment itself, including the weight, mass center, inertia, etc. of the lifting equipment. These data are key factors affecting vibration characteristics and stability, and can help analyze the dynamic behavior of the equipment when subjected to force.

[0103] Natural frequency can refer to the frequency at which a system vibrates naturally without external excitation. Every object or system has a natural frequency, which depends on the mass, stiffness and other characteristics of the object. Natural frequency data is used to analyze the resonance phenomenon that may occur in the lifting equipment during work, and prevent the equipment from working at the resonant frequency, which may cause equipment damage or failure.

[0104] The number of vibration frequencies can refer to the number of different vibration frequencies observed during the operation of the device. Each vibration mode has a specific frequency, and multiple vibration modes of the device can exist at the same time and vibrate at different frequencies. Monitoring the number of vibration frequencies can help detect the operating status of the device and determine whether there is abnormal vibration.

[0105] Vibration amplitude can refer to the vibration amplitude of the equipment at each vibration frequency, indicating the intensity of the vibration. A larger vibration amplitude may indicate that some parts of the equipment are abnormal or structurally unstable, which may cause damage or failure of the equipment. Therefore, monitoring the vibration amplitude is crucial for analyzing the health of the equipment.

[0106] The natural frequency can be the frequency of vibration generated by the equipment in each vibration mode. Different vibration modes will produce different frequencies. For example, the structure, hook, load and other factors of the equipment will produce different frequencies. By monitoring each vibration frequency, it can be determined whether the equipment has resonance or other abnormal conditions under specific conditions.

[0107] The second environmental data may refer to environmental factor data other than the equipment itself, which may include: ambient temperature, humidity, air pressure, etc. Physical phenomena such as noise, vibration, air flow, etc. in the environment. Working conditions in the operating area, such as the operation of other equipment, the location of operators, etc. These environmental data will affect the working state of the lifting equipment, and may interact with factors such as vibration and load to affect the safety and stability of the equipment.

[0108] The vibration time can refer to the duration for which the lifting equipment undergoes vibration under certain conditions. The vibration time is an important indicator for judging whether the vibration belongs to a short-term anomaly or a long-term persistent problem. Long-term or frequent vibration may lead to equipment fatigue or damage.

[0109] The damping coefficient can refer to the ability of a material or system to absorb vibration energy during vibration. Damping can reduce the amplitude of vibration, reduce resonance phenomena, and prevent excessive vibration from causing equipment damage. The damping coefficient is usually determined by the design of the equipment, material properties, and working environment. In vibration analysis, a higher damping coefficient indicates that the equipment can better absorb energy and suppress vibration; a lower damping coefficient may cause the equipment to resonate under external excitation, increasing the risk of equipment damage.

[0110] Load sensors (such as strain gauges, force sensors, etc.) can be installed at key parts of the lifting equipment (such as hooks, boom, etc.). These sensors can monitor the load force borne on the lifting equipment in real time. Equipment mass data is usually provided by the equipment manufacturer and can be obtained by referring to the technical manual or specification of the equipment. The natural frequency is usually obtained through modal analysis, which is a vibration test method used to measure the response of the equipment at different frequencies. The equipment can be vibration-tested by an exciter (such as an impact hammer or vibration excitation device), the vibration responses at different frequencies are recorded, and then the natural frequency is calculated by analyzing the response data. Specialized vibration analyzers or data acquisition devices can monitor the vibration frequency and amplitude of the equipment in real time and obtain the vibration amplitude corresponding to each frequency through spectrum analysis. In this way, the number of vibration frequencies, vibration amplitudes, and the natural frequencies of each vibration frequency can be determined. Environmental sensors such as temperature, humidity, pressure sensors, as well as wind speed, air flow, etc. are installed to monitor the changes in the operating environment in real time. The vibration time when the equipment vibrates during operation is recorded using vibration sensors, and the vibration event can be time-stamped through a data acquisition system to obtain the duration of vibration. The load force data, environmental data, etc. can be used as inputs, and through regression analysis or machine learning algorithms (such as support vector machines, neural networks, etc.), the damping coefficient can be predicted and calculated. Through learning a large amount of historical data, the model can predict the appropriate damping coefficient based on the input real-time data.

[0111] S202, calculate the displacement data of the lifting equipment according to the load force data, equipment mass data, natural frequency data, vibration amplitudes of each vibration frequency, natural frequencies of each vibration frequency, damping coefficient, vibration time, and a preset displacement calculation formula.

[0112] Displacement data can be a physical quantity that describes the degree of movement of a hoisting device during vibration or under the action of an external load. It usually represents the displacement of a certain part of the device relative to its initial position, usually in millimeters (mm) or meters (m). Specifically, displacement data can reflect the displacement amplitude of the device under conditions such as vibration, oscillation, and load changes.

[0113] The load force data, device mass data, natural frequency data, vibration amplitudes of each vibration frequency, natural frequencies of each vibration frequency, damping coefficient, and vibration time can be substituted into a preset displacement calculation formula to calculate the displacement data of the hoisting device.

[0114] Based on the above technical solution, optionally, the preset displacement calculation formula is:

[0115]

[0116] where x(t) is the displacement data; F load is the load force data; m is the device mass data; δ(t) is the damping coefficient; t is the vibration time; w is the natural frequency; α is a preset vibration adjustment coefficient; N is the number of vibration frequencies; A i is the vibration amplitude of each vibration frequency; w i is the natural frequency of each vibration frequency.

[0117] In this solution, the adjustment coefficient α is used to balance the vibration effects of multiple frequencies. The preset of this coefficient depends on the design requirements, vibration characteristics, and vibration control requirements of the device. First, analyze the vibration characteristics of the hoisting device, including the vibration intensity of different frequency components and their effects on the device performance. Different frequency components may correspond to the vibration modes of the device under different working conditions. The α coefficient helps to adjust the contributions of these frequency components to the total vibration, enabling the system to treat the effects of different frequencies more reasonably. Different vibration modes of the device (such as natural frequencies or resonance frequencies) will be excited under different working conditions. To avoid interference between vibrations of different frequencies, the setting of α will be preset according to the natural frequency of the device, external excitation, and frequency response. Engineers or vibration experts of the device can determine the optimal α coefficient through experiments or simulation calculations. Through modeling and experimental data analysis, α can be adjusted according to different usage scenarios. For example, in actual hoisting operations, if certain specific frequencies have a greater impact on the device operation, the adjustment coefficient of these frequencies may be increased, or in a multi-frequency system, the value of α is adjusted to make the system response stable. The effects of vibrations of different frequencies on the device performance can be measured through experiments, and α is adjusted so that the response of the device meets the expected control requirements during actual use.

[0118] Based on the above technical solution, optionally, after calculating the displacement data of the hoisting device, the method further includes:

[0119] If the displacement data does not exceed a preset displacement threshold, after each preset monitoring time interval, update the load force data, vibration time, and second environmental data, and update the damping coefficient according to the updated load force data and second environmental data;

[0120] According to the updated load force data, updated vibration time, equipment mass data, natural frequency data, vibration amplitudes of each vibration frequency, natural frequencies of each vibration frequency, damping coefficient, and a preset displacement calculation formula, update the displacement data of the hoisting device, and re-determine whether it exceeds the preset displacement threshold after each update of the displacement data. If it exceeds the preset displacement threshold, generate an alarm message and send the alarm message to the remote control platform.

[0121] In this solution, the preset monitoring time interval may refer to the time interval during the operation of the device when the system automatically updates relevant data (such as load force data, vibration time, environmental data, etc.) and performs displacement calculations.

[0122] During the operation of the hoisting device, if the displacement data does not exceed the preset displacement threshold, then every time the preset monitoring time interval is reached, the system will automatically update the load force data, vibration time, and second environmental data, and recalculate the damping coefficient based on the updated load force data and environmental data. Then, according to the updated data (including load force, vibration time, equipment mass, natural frequency, vibration amplitude, vibration frequency, damping coefficient, etc.) and the preset displacement calculation formula, update the displacement data of the hoisting device. After each update, the system will re-check whether the displacement data exceeds the preset threshold. If it does, an alarm message will be generated and sent to the remote control platform to take necessary operation and maintenance measures in a timely manner to ensure the safe operation of the device.

[0123] In this solution, by regularly updating the load force data, vibration time, environmental data, etc., the status of the hoisting device can be continuously monitored to ensure that the device is always within the safe range during operation. If the displacement data of the device exceeds the preset threshold, the system can generate an alarm in a timely manner and send it to the remote control platform to quickly respond to potential safety hazards and avoid equipment damage or safety accidents.

[0124] S203. If the displacement data exceeds the preset displacement threshold, obtain the serial number information of the hoisting device, generate an alarm message according to the serial number information and the displacement data, and send the alarm message to the remote control platform.

[0125] The preset displacement threshold can be a value used to determine whether the vibration or displacement of the hoisting equipment exceeds the safe range. This threshold is usually set based on the design standards of the equipment, safety codes, and requirements of the actual working environment. When the displacement data of the hoisting equipment exceeds this threshold, it indicates that the equipment may have abnormal vibration or faults, and an alarm needs to be triggered.

[0126] The serial number information can be an identifier used to uniquely identify the hoisting equipment. It can be the equipment's serial number, ID, registration number, etc., ensuring that each equipment has a unique identification. The serial number information is very important for managing and tracking the equipment status.

[0127] The alarm information can be a notification sent to the remote control platform when the equipment status is abnormal, usually including the abnormal status of the equipment, fault description, time of the abnormality occurrence, and possible impacts. The alarm information can help the operator take corresponding measures in a timely manner.

[0128] The displacement data can be compared with the preset displacement threshold. If the displacement data is greater than the preset threshold, it is determined that the equipment exceeds the safe working range. Once it is determined that the equipment displacement exceeds the threshold, the system will search for the unique identifier of the equipment (equipment serial number information). This information can be obtained from the equipment's database or system to ensure that the specific equipment with the abnormality can be accurately identified. According to the equipment serial number and abnormal displacement data, the system will automatically generate alarm information. The alarm information usually includes the following content: Equipment serial number: Identifies which equipment has an abnormality. Current displacement data: Informs the specific data of the displacement exceeding the preset threshold. Through the network or other communication methods, the alarm information is sent to the remote control platform. The operator of the remote control platform will receive the alarm notification and can check the equipment or take necessary maintenance measures according to the alarm content.

[0129] In this embodiment, by real-time monitoring of multiple data indicators such as the vibration, load force, and equipment mass of the hoisting equipment, the system can timely detect the abnormal status of the equipment and judge whether it exceeds the safe working range by calculating the displacement data. This can detect potential faults or abnormalities in advance, avoid serious problems with the equipment, and thus reduce the risk of faults occurring.

[0130] Based on the above technical solution, optionally, after sending the alarm information to the remote control platform, the method further includes:

[0131] Performing vibration suppression intervention on the hoisting equipment according to the preset vibration suppression strategy until the staff checks the hoisting equipment.

[0132] In this solution, the preset vibration suppression strategy can refer to the control measures for equipment vibration set in advance according to the working characteristics, vibration analysis and safety standards of the equipment to reduce the negative impacts brought by vibration during the operation of the lifting equipment (such as equipment damage, reduced accuracy or operation safety problems). These strategies usually consider the following aspects: Damping coefficient adjustment: Dynamically adjust the damping coefficient of the equipment according to the changes in vibration frequency and amplitude to reduce the propagation of vibration. Vibration reduction device activation: Activate mechanical or electrical vibration reduction devices, such as shock absorbers, vibration isolation pads, active vibration control devices, etc., to absorb or reduce the vibration generated by the equipment. Frequency matching control: Avoid the equipment operating within the resonance frequency range by adjusting the operating parameters of the equipment (such as load, speed, etc.), so as to reduce the vibration caused by resonance. Vibration monitoring and adjustment: Real-time monitor the vibration frequency and amplitude, and adjust the control strategy to ensure that the vibration remains within the safe range. Stop or decelerate operation: When the vibration of the equipment exceeds the safe range, restrict the equipment from continuing to work, or reduce the vibration intensity by decelerating, etc., until the problem is solved.

[0133] When the vibration of the equipment exceeds the safety threshold, the corresponding intervention measures can be automatically activated according to the preset vibration suppression strategy. These measures may include: Adjusting the damping coefficient to reduce vibration. Activating vibration reduction devices, such as active vibration reduction systems, vibration isolation pads, etc. Adjusting the speed of the equipment to avoid the equipment operating at the resonance frequency. In extreme cases, temporarily stop or decelerate the operation to reduce the vibration intensity. After implementing the vibration suppression intervention, the system will continue to monitor the vibration data. If the vibration data still exceeds the safety threshold, the system will continuously execute the intervention strategy until the vibration returns to the safe level. Once the vibration suppression measures are activated and the equipment status has not improved, the system will issue an alarm to notify the staff for inspection or intervention to ensure that the staff is aware of the equipment status. Finally, the vibration problem will be inspected on-site by the staff, and necessary repairs or adjustments will be made. After the staff inspection, the vibration suppression strategy may be further adjusted according to the equipment status to ensure the stability and safety of the equipment.

[0134] In this solution, through real-time monitoring and automatic intervention, it is possible to effectively reduce equipment failures or safety hazards caused by excessive vibration. Avoid damage to the equipment caused by excessive vibration or injury to the operator, and enhance the safety of the equipment.

[0135] Figure 3 It is a schematic block diagram of a remote control system for a lifting equipment in a live working environment provided for an embodiment of the present disclosure. It is characterized in that the system includes:

[0136] An abnormal determination module 301, configured to obtain real-time electrical data of the lifting equipment operating in a live working environment, input the real-time electrical data into a preset abnormal detection model, and determine whether there is an electrical abnormality;

[0137] An electrical isolation module 302, configured to, if there is an electrical anomaly, determine an electrical isolation protection scheme through a preset anomaly detection model, and perform electrical isolation on the hoisting equipment according to the electrical isolation protection scheme;

[0138] A continuous determination module 303, configured to send real-time electrical data and the electrical isolation protection scheme to a remote control platform, update the real-time electrical data at a preset update time interval, input the updated real-time electrical data into the preset anomaly detection model, and determine whether there is still an electrical anomaly;

[0139] A suggestion generation module 304, configured to, if there is no electrical anomaly, determine a current suggestion through the preset anomaly detection model, and send the current suggestion to the remote control platform;

[0140] A device control module 305, configured to, if a recovery instruction from the remote control platform is received, control the hoisting equipment to continue working according to the recovery instruction.

[0141] Figure 4 FIG. shows a schematic block diagram of an electronic device 400 that can be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, for example, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0142] The electronic device 400 includes a computing unit 401, which can execute various appropriate actions and processes according to a computer program stored in the ROM 402 or a computer program loaded from the storage unit 408 into the RAM 404. In the RAM 404, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 404 are connected to each other through a bus 404. The I / O interface 405 is also connected to the bus 404.

[0143] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disc, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0144] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 executes the various methods and processes described above, such as the remote control method for hoisting equipment in a live electrical environment. For example, in some embodiments, the remote control method for hoisting equipment in a live electrical environment can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 404 and executed by the computing unit 401, one or more steps of the remote control method for hoisting equipment in a live electrical environment described above can be executed. Alternatively, in other embodiments, the computing unit 401 can be configured to execute the remote control method for hoisting equipment in a live electrical environment by any other suitable means (such as, by means of firmware).

[0145] The various embodiments of the systems and technologies described above in this article can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, the programmable processor can be a dedicated or general-purpose programmable processor, can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0146] The program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, a special purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0147] In the context of the present disclosure, a machine-readable medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0148] In order to provide interaction with a user, the systems and techniques described herein may be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0149] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.

[0150] A computer system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, a server of a distributed system, or a server incorporating blockchain.

[0151] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0152] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A remote control method for hoisting equipment in an electric environment, characterized in that: The method comprises: Acquire real-time electrical data of the hoisting equipment working in a live environment, and input the real-time electrical data into a preset anomaly detection model to determine whether there is an electrical anomaly; If there is an electrical anomaly, an electrical isolation protection scheme is determined through a preset anomaly detection model, and the hoisting equipment is electrically isolated according to the electrical isolation protection scheme; Sending real-time electrical data and electrical isolation protection solutions to the remote control platform, updating the real-time electrical data when a preset update time interval is reached, and inputting the updated real-time electrical data into a preset anomaly detection model to determine whether electrical anomalies still exist; If there is no electrical anomaly, a current suggestion is determined by a preset anomaly detection model, and the current suggestion is sent to the remote control platform; If a recovery instruction is received from the remote control platform, the lifting equipment is controlled to continue working according to the recovery instruction.

2. The method according to claim 1, characterized in that in, After inputting the updated real-time electrical data into a preset anomaly detection model to determine whether the electrical anomaly still exists, the method further includes: If there is an electrical anomaly, the electrical isolation protection scheme is re-determined through a preset anomaly detection model, and the hoisting equipment continues to be electrically isolated according to the electrical isolation protection scheme; The real-time electrical data is updated every time a preset update time interval is reached, and the updated real-time electrical data is input into a preset anomaly detection model to determine whether the electrical anomaly still exists, and if the electrical anomaly exists, the electrical isolation protection scheme is re-determined, and the lifting equipment is continuously electrically isolated according to the electrical isolation protection scheme until there is no electrical anomaly, and the current recommendation is determined by the preset anomaly detection model, and the current recommendation is sent to the remote control platform.

3. The method according to claim 1, characterized in that: in, After sending the current suggestion to the remote control platform, the method further includes: Obtaining the current path, current position, and target position of the hoisting equipment, as well as first environmental data and electrical load information of each area, inputting the current path, current position, target position, first environmental data, and electrical load information of each area into a preset path planning model to determine whether the path needs to be replanned; If the path needs to be replanned, the target path is output through the preset path planning model; Accordingly, the current suggestion is sent to the remote control platform, including: Sending the current suggestion and the target path to the remote control platform; Correspondingly, if a recovery instruction is received from the remote control platform, the hoisting equipment is controlled to continue working according to the recovery instruction, including: If a recovery instruction is received from the remote control platform, the hoisting equipment is controlled to continue working according to the recovery instruction and the target path.

4. The method according to claim 3, characterized in that: in, After controlling the hoisting equipment to continue working according to the recovery instruction and the target path, the method further includes: If it is identified that the first environment data and / or the electrical load information of each area have changed, the first environment data, the electrical load information of each area and the current position of the lifting equipment are re-acquired, and the re-acquired first environment data, the re-acquired electrical load information of each area, the re-acquired current position, the target path and the target position are input into a preset path planning model to determine whether the path needs to be re-planned; If the path needs to be replanned, the target path is updated through the preset path planning model and the target path is sent to the remote control platform; If a path change instruction transmitted by the remote control platform is received, the hoisting equipment is controlled to continue working according to the updated target path.

5. The method according to claim 1, characterized in that in, After controlling the hoisting equipment to continue working according to the recovery instruction, the method further includes: Obtaining load force data, equipment mass data, natural frequency data, number of vibration frequencies, vibration amplitude of each vibration frequency, and natural frequency of each vibration frequency of the lifting equipment, and obtaining second environmental data and vibration time, and determining a damping coefficient according to the load force data and the second environmental data; Calculate the displacement data of the hoisting equipment according to the load force data, equipment mass data, natural frequency data, vibration amplitude of each vibration frequency, natural frequency of each vibration frequency, damping coefficient, vibration time and a preset displacement calculation formula; If the displacement data exceeds a preset displacement threshold, the serial number information of the hoisting equipment is obtained, an alarm message is generated according to the serial number information and the displacement data, and the alarm message is sent to the remote control platform.

6. The method according to claim 5, characterized in that in, The preset displacement calculation formula is: Where x(t) is the displacement data; F load is the load force data; m is the equipment mass data; δ(t) is the damping coefficient; t is the vibration time; w is the natural frequency; α is the preset vibration adjustment coefficient; N is the number of vibration frequencies; A i is the vibration amplitude of each vibration frequency; w i is the natural frequency of each vibration frequency.

7. The method according to claim 5, characterized in that in, After calculating the displacement data of the lifting equipment, the method further includes: If the displacement data does not exceed the preset displacement threshold, the load force data, the vibration time and the second environment data are updated after each preset monitoring time interval, and the damping coefficient is updated according to the updated load force data and the second environment data; The displacement data of the lifting equipment is updated according to the updated load force data, the updated vibration time, the equipment mass data, the natural frequency data, the vibration amplitude of each vibration frequency, the natural frequency of each vibration frequency, the damping coefficient and the preset displacement calculation formula, and after each update of the displacement data, it is re-determined whether it exceeds the preset displacement threshold. If it exceeds the preset displacement threshold, an alarm message is generated and sent to the remote control platform.

8. The method according to claim 5, characterized in that in, After sending the alarm information to the remote control platform, the method further includes: Vibration suppression intervention is performed on the hoisting equipment according to the preset vibration suppression strategy until the staff inspects the hoisting equipment.

9. A remote control system for hoisting equipment in an electric environment, used to execute the method according to any one of claims 1 to 8, characterized in that: The system comprises: The abnormality determination module is used to obtain real-time electrical data of the hoisting equipment working in a live environment, input the real-time electrical data into a preset abnormality detection model, and determine whether there is an electrical abnormality; An electrical isolation module is used to determine an electrical isolation protection scheme through a preset anomaly detection model if an electrical anomaly exists, and to electrically isolate the hoisting equipment according to the electrical isolation protection scheme; A continuous determination module is used to send real-time electrical data and electrical isolation protection solutions to the remote control platform, and update the real-time electrical data when a preset update time interval is reached, and input the updated real-time electrical data into a preset anomaly detection model to determine whether the electrical anomaly still exists; A suggestion generation module, for determining a current suggestion through a preset anomaly detection model if no electrical anomaly exists, and sending the current suggestion to the remote control platform; The equipment control module is used to control the hoisting equipment to continue working according to the recovery instruction if a recovery instruction is received from the remote control platform.

10. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 8.

Images