Automatic contraction hanging basket remote control system

Abstract

The invention relates to the technical field of remote control, in particular to an automatic contraction hanging basket remote control system which comprises a main control unit recognition module, an automatic control module, a remote control terminal module, a data communication optimization module and an action instruction distribution module. In the invention, by combining a hanging basket contraction program with action triggering of a mechanical execution mechanism, stress monitoring of a hanging basket connecting part and action feedback, deep fusion of automatic control and real-time monitoring is realized, dynamic control and an execution path of the hanging basket are optimized, accurate action execution and efficient resource allocation are ensured, and the safety of the hanging basket is improved. The system can perform real-time control according to different working environments, errors and potential safety hazards caused by manual operation are reduced, and through continuous feedback and data updating, the system can automatically adjust a control strategy, enhance adaptability and self-repairing capability, ensure efficient and safe operation of hanging basket operation under different conditions, improve the intelligent and automatic level, and improve the working efficiency. And the overall operation efficiency and safety are obviously improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote control, in particular to an automatic retracting hanging basket remote control system. BACKGROUND

[0002] The technical field of remote control mainly involves monitoring and operating remote devices or systems through communication networks, which is widely used in industrial automation, smart home, unmanned driving, energy management and other fields. The remote control system includes core functions such as signal transmission, device control, monitoring feedback, etc. Its purpose is to realize real-time control and management of devices through networks without direct contact with devices. With the development of technologies such as Internet of Things, 5G communication and artificial intelligence, the application scenarios of remote control systems are continuously expanding, and the intelligent and automated level of the system is gradually improving. Among them, the traditional automatic retracting hanging basket control system is mainly used in construction, especially in high-altitude operations, to improve work efficiency and safety. The traditional hanging basket control system needs to be operated manually or partially automatically, which faces problems such as inconvenient operation, low efficiency and safety hazards.

[0003] The prior art relies on manual or partial automation in the traditional hanging basket control system, which cannot realize real-time feedback and dynamic control remotely, resulting in low operation efficiency, inability to respond to environmental changes and emergencies in time, and high safety risks. In complex environments such as high-altitude operations, the traditional system cannot provide precise control and adaptive adjustment, and is easily affected by insufficient experience of operators, changes in environmental factors, etc., causing equipment failure, work delay or safety accidents. During manual operation, the stress condition of the hanging basket connection part cannot be monitored in real time, which increases the risk of failure when operating in an unstable state. SUMMARY

[0004] The purpose of the present application is to solve the problems existing in the prior art and to provide an automatic retracting hanging basket remote control system.

[0005] In order to achieve the above purpose, the present application adopts the following technical scheme: an automatic retracting hanging basket remote control system comprises: The main control unit recognition module analyzes the hanging basket retraction parameters based on the built-in hanging basket retraction data, triggers the mechanical actuator, monitors the stress of the hanging basket connection part, calibrates the communication component state, collects real-time feedback of the action sequence, and generates a hanging basket retraction instruction sequence; The automatic control module calls the hanging basket retraction instruction sequence, extracts the running logic and operation dependency of the hanging basket components, filters the optimal instruction transmission path according to the communication network type and signal transmission protocol, and establishes a hanging basket dynamic control model; The remote control terminal module calls the hanging basket dynamic control model, matches the action demand and operation requirement of the hanging basket function unit, uses a mechanical transmission device to complete the execution of the corresponding action instruction, and embeds a safety protection mechanism and an abnormality processing strategy to form a hanging basket standardized action execution set; The data communication optimization module calls the hanging basket standardized action execution set, combines the abnormality solution uploaded in real-time operation, performs incremental update on the basic control logic library, extracts high-frequency problems and key operation points, and constructs an intelligent hanging basket remote control strategy knowledge base.

[0006] As a further scheme of the application, the hanging basket contraction instruction sequence comprises instruction number, action execution interval, operation dependency relationship, device state identifier, communication protocol mapping value, the hanging basket dynamic control model comprises function unit number, action logic chain, load balancing parameter, priority label, time floating range, the hanging basket standardized action execution set comprises function unit action content, safety protection mechanism index, abnormality processing strategy list, execution file number, action step sequence, and the intelligent hanging basket remote control strategy knowledge base comprises problem processing scheme library, high-frequency problem label, key operation point index, knowledge base update frequency and term standardization table.

[0007] As a further scheme of the application, the main control unit recognition module comprises: The instruction generation submodule removes invalid instruction records caused by device failure or unplanned shutdown based on the built-in hanging basket contraction data, identifies the average execution time of each component of the hanging basket under the same action, and generates an action reference value; The action interval calculation submodule identifies the standard action interval of the hanging basket component based on the action reference value in combination with the differentiated device running state and environmental factors, and marks the upper and lower limit values of the interval to obtain an action distribution feature data set; The instruction sequence construction submodule calls the action distribution feature data set, identifies the logical relationship and operation dependency between the hanging basket components, formulates an instruction structure and sets an associated weight according to the instruction number and action interval, and establishes a hanging basket contraction instruction sequence.

[0008] As a further scheme of the application, the automatic control module comprises: The action logic extraction submodule calls the hanging basket contraction instruction sequence, extracts the logical chain and operation dependency between the hanging basket components, analyzes the dependency strength and logical sequence, identifies the corresponding time sequence and priority between operations, draws a dependency graph and optimizes the sequence arrangement, and obtains an action logic relationship matrix; The transmission path calculation submodule calculates the load balancing value of the instruction transmission path according to the action logic relationship matrix in combination with the communication network type and signal transmission protocol, verifies the path stability and delay, performs real-time dynamic adjustment, and screens to obtain an optimal instruction transmission path. The dynamic path generation submodule identifies the function unit number and the action logic chain based on the optimal instruction transmission path, determines the transmission priority and the time floating range, dynamically adjusts the path in combination with the real-time network state, and generates a hanging basket dynamic control model.

[0009] As a further scheme of the present application, the remote control terminal module comprises: The action content matching submodule calls the hanging basket dynamic control model, extracts the action content and operation requirements of the hanging basket function unit, analyzes the action complexity and technical dependency, identifies the relationship and cooperation requirements between function units, and generates an action content matching table; The protection mechanism embedding submodule performs standardized processing on the safety protection mechanism through a mechanical transmission device based on the action content matching table, embeds the action content in the matching table, performs safety inspection and risk assessment, and forms a safety protection mechanism index; The action execution generation submodule calls the safety protection mechanism index, combines the abnormal handling strategy list and the action step sequence, generates a standardized action execution file for the corresponding function unit, and marks the execution file number, integrates and verifies the execution file and the step sequence, and forms a hanging basket standardized action execution set.

[0010] As a further scheme of the present application, the data communication optimization module comprises: The abnormal solution aggregation submodule calls the hanging basket standardized action execution set, extracts the abnormal solution uploaded in the real-time operation process, analyzes the applicable scenarios and solution efficiency of the solution, identifies the high-frequency abnormal types or common problems, integrates the problem handling steps and effective strategies, and classifies to form a problem handling scheme library; The high-frequency problem extraction submodule calculates the high-frequency problem weight value based on the problem handling scheme library, analyzes the causes and frequency of the problem, filters the key problems and key operation points, marks and organizes them into tags, and obtains high-frequency problem tags; The knowledge base updating submodule updates the basic control logic library in increments according to the high-frequency problem tags, combines the key operation point index and the term standardization table, records the knowledge base update frequency, and constructs an intelligent hanging basket remote control strategy knowledge base.

[0011] As a further scheme of the present application, the high-frequency abnormal types or common problems refer to when the abnormal types or common problems occur more than fifteen times of the preset frequency threshold within thirty consecutive natural days, they are identified as high-frequency abnormal types or common problems; The high-frequency problem weight value is determined by the problem occurrence frequency, the association strength of the key operation point and the problem, and the solution efficiency factor, and is obtained by calculating through a self-matching multi-level fuzzy evaluation algorithm.

[0012] As a further scheme of the present application, the system further comprises an action instruction distribution module: The action instruction distribution module calls the intelligent hanging basket remote control strategy knowledge base, automatically distributes the action instruction file of the corresponding functional unit according to the operator login account and the functional unit number, marks the file version number and the update time, and outputs the remote hanging basket instruction distribution list; The remote hanging basket instruction distribution list comprises a functional unit number, an action instruction file number, a file version number, an update time stamp, and a distribution path identifier.

[0013] As a further scheme of the present application, the action instruction distribution module comprises: The file version management submodule calls the intelligent hanging basket remote control strategy knowledge base, identifies the version number and the update time of the action instruction file, compares the current version and the original version, selects the current version file, and generates a file version mapping table; The distribution path generation submodule identifies the distribution path of the corresponding functional unit based on the file version mapping table, in combination with the operator login account and the functional unit number, records the path identifier and the update time stamp, and generates the remote hanging basket instruction distribution list.

[0014] Compared with the prior art, the present application has the following advantages and positive effects: In the present application, by combining the hanging basket contraction program with the action triggering of the mechanical execution mechanism, the force monitoring and action feedback of the hanging basket connection part, the deep integration of automatic control and real-time monitoring is realized, the dynamic control and execution path of the hanging basket are optimized, and therefore more accurate action execution and more efficient resource allocation are ensured. Through intelligent scheduling and transmission path optimization, real-time control for different working environments is realized, errors and safety hazards caused by human operation are reduced, and through continuous feedback and data updating, the control strategy can also be automatically adjusted, the adaptability and self-repairing ability of the system are enhanced, the hanging basket operation can maintain high efficiency and safety under various conditions, the intelligent and automatic level is improved, and the overall operation efficiency and safety are effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The system flowchart of the present application; Figure 2 The process flowchart of the main control unit identification module in the present application; Figure 3 The process flowchart of the automatic control module in the present application; Figure 4 The process flowchart of the remote control terminal module in the present application; Figure 5 The process flowchart of the data communication optimization module in the present application; Figure 6 Flow chart of action instruction distribution module in the present application. DETAILED DESCRIPTION

[0016] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0017] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0018] Please refer to Figure 1 An automatic retracting hanging basket remote control system comprises: The main control unit identification module analyzes the hanging basket retraction parameters based on the built-in hanging basket retraction data, triggers the mechanical actuator action, monitors the force on the hanging basket connection part, calibrates the communication component state, collects real-time feedback of the action sequence, and generates a hanging basket retraction instruction sequence; The automatic control module calls the hanging basket retraction instruction sequence, extracts the operation logic and operation dependency of the hanging basket components, filters the optimal instruction transmission path according to the communication network type and signal transmission protocol, and establishes a hanging basket dynamic control model; The remote control terminal module calls the hanging basket dynamic control model, matches the action demand and operation requirement of the hanging basket functional unit, uses the mechanical transmission device to complete the execution of the corresponding action instruction, and embeds the safety protection mechanism and abnormal handling strategy to form a hanging basket standardized action execution set; The data communication optimization module calls the hanging basket standardized action execution set, combines the abnormal solution uploaded during real-time operation to incrementally update the basic control logic library, extracts high-frequency problems and key operation points, and constructs an intelligent hanging basket remote control strategy knowledge base; The action instruction distribution module calls the intelligent hanging basket remote control strategy knowledge base, automatically distributes the action instruction file of the corresponding functional unit according to the operator login account and functional unit number, marks the file version number and update time, and outputs the remote hanging basket instruction distribution list.

[0019] The hanging basket contraction instruction sequence comprises instruction number, action execution interval, operation dependency relationship, device state identification, communication protocol mapping value, the hanging basket dynamic control model comprises function unit number, action logic chain, load balancing parameter, priority label, time floating range, the hanging basket standardized action execution set comprises function unit action content, safety protection mechanism index, abnormal treatment strategy list, execution file number, action step sequence, the intelligent hanging basket remote control strategy knowledge base comprises problem treatment scheme library, high-frequency problem label, key operation point index, knowledge base update frequency, term standardization table, and the remote hanging basket instruction distribution list comprises function unit number, action instruction file number, file version number, update time stamp, distribution path identification.

[0020] Please refer to Figure 2 The master control unit identification module comprises: The instruction generation submodule removes invalid instruction records caused by device failure or unscheduled downtime based on the built-in hanging basket contraction data, identifies the average execution time of each component of the hanging basket under the same action, and generates an action reference value; Based on the built-in hanging basket contraction data, the atomic actions of each component in the hanging basket contraction operation and their instruction numbers are clearly defined. For example, the instruction number of "release safety brake" is BAK001, the instruction number of "motor 1 starts to go up 10 cm" is MOT001_UP10, and the instruction number of "limit switch 1 touches" is LIM001_TRG. The module reads all instruction execution records from historical operation data and filters out invalid records. First, check the device fault code of the record. Non-zero fault code (such as 0x0001 motor overload) is judged as invalid. At the same time, check the unscheduled downtime flag. If it is TRUE, the record is considered invalid, and the abnormal deviation of the instruction execution time is evaluated. For example, the historical average execution time of MOT001_UP10 is 5.0 seconds, the standard deviation is 0.2 seconds, and the execution time deviation threshold is set to 3 times the standard deviation of the average execution time, i.e. [4.4 seconds, 5.6 seconds]. Execution time exceeding this range is identified as invalid record. This 3 times standard deviation threshold is based on the normal distribution theory, covering 99.7% of the normal fluctuation range, avoiding accidental fluctuation misjudgment. After screening, only valid instruction records under normal operation state are retained. The valid records are processed. For all valid execution records under the same instruction number, the execution time is accumulated and averaged to calculate the average execution time of the action. For example, if there are 2000 valid records of MOT001_UP10, the total execution time is 10000 seconds, and the average execution time is 5.0 seconds. This average execution time is the action reference value of the action, representing the performance of the component under ideal stable working conditions, providing a reference for subsequent standard action interval setting. A data set of action reference values of the same action of each component is generated, such as the reference value of MOT001_UP10 being 5.0 seconds, the reference value of BAK001 being 0.1 second, and the reference value of LIM001_TRG being 0.05 second.

[0021] The action interval calculation sub-module identifies the standard action interval of the hanging basket component based on the action reference value, in combination with the differentiated equipment operating state and environmental factors, and marks the upper and lower limit values of the interval to obtain the action distribution feature data set; Based on the action reference value data set, such as the reference value 5.0 seconds of MOT001_UP10, in combination with the equipment operating state and environmental factors, the standard action interval of the hanging basket component is identified, the equipment operating state parameters are defined, and the equipment health state is evaluated by monitoring the motor current, vibration frequency, temperature and other data. The motor current is less than 8A and the vibration frequency is less than 0.3G as "healthy state"; the motor current is between 8A and 10A or the vibration frequency is between 0.3G and 0.5G as "slight wear state"; the motor current is higher than 10A or the vibration frequency is higher than 0.5G as "high load / wear state". At the same time, considering the environmental factors, the environmental wind speed is obtained through the wind speed sensor, the wind speed is less than 5 meters / second as "low wind speed"; 5 meters / second to 10 meters / second as "medium wind speed"; higher than 10 meters / second as "high wind speed". The environmental temperature is 0℃ to 30℃ as "normal temperature"; lower than 0℃ as "low temperature"; higher than 30℃ as "high temperature". For different state and factor combinations, the action reference value is corrected. For example, in the "healthy state", "low wind speed", "normal temperature", the standard action interval of MOT001_UP10 is set to [4.9 seconds, 5.1 seconds], that is, ±2% of the reference value 5.0 seconds, indicating that the execution time fluctuation range is extremely small under the optimal condition, when the equipment is in "slight wear state" and "medium wind speed", "normal temperature", the module adjusts the reference value according to the preset correction coefficient. The correction coefficient is obtained by a large amount of experimental data regression analysis, for example, the equipment wear correction coefficient is +0.05 seconds, and the medium wind speed correction coefficient is +0.03 seconds. At this time, the reference value of MOT001_UP10 is corrected to 5.0+0.05+0.03=5.08 seconds, and its standard action interval is adjusted to [5.08 seconds-0.1 seconds, 5.08 seconds+0.1 seconds], that is, [4.98 seconds, 5.18 seconds], wherein 0.1 seconds is recalculated according to the reference value after correction ±2% tolerance. Each standard action interval contains its corresponding equipment operating state, environmental factor and action reference value, and all the marked interval data converge to form the action distribution feature data set.

[0022] The instruction sequence construction sub-module calls the action distribution feature data set, identifies the logical relationship and operation dependency between the hanging basket components, formulates the instruction structure and sets the associated weight according to the instruction number and the action interval, and establishes the hanging basket contraction instruction sequence; The action distribution feature dataset is called, which contains the standard action interval of each component of the hanging basket under different operating conditions and environmental factors. For example, the interval of instruction MOT001_UP10 under the condition of "healthy / low wind speed / normal temperature" is [4.9 seconds, 5.1 seconds]. First, the logical relationship and operational dependency between the hanging basket components are identified. By analyzing the workflow diagram and safety protocol of the built-in contraction program, the order, parallelism and precondition of the action are identified. For example, BAK001 (release safety brake) must be completed before MOT001_UP10 (start motor 1 up 10 cm), which is a prerequisite dependency. According to the instruction number and the action interval in the action distribution feature dataset, the instruction structure is formulated, and the associated weight is set for each instruction structure. The associated weight reflects the closeness of operational dependency and the influence on the criticality of the contraction process. The safety level and performance requirements are referred to when setting. If instruction A is a mandatory safety prerequisite for instruction B, the associated weight is set to 1.0, indicating strong dependency. For example, the associated weight between BAK001 and MOT001_UP10 is set to 1.0. If instructions C and D are parallel and have little effect on the overall time, the associated weight is set to 0.3. For example, the associated weight between MOT001_UP10 and MOT002_UP10 is set to 0.5, because both of them determine the start time of the next step. 0.5 balances their independence and influence on the overall timing. The associated weight is calculated as follows: first, the dependency between components is sorted, including prerequisite, mutual exclusion and parallel dependency. According to the dependency type and the safety or performance criticality in the hanging basket contraction process, the initial weight is assigned (for example, safety dependency 0.9, performance dependency 0.7, and synchronous dependency 0.5). The expert system combines historical fault data to fine-tune the weight. If a certain dependency relationship has caused serious failure or long downtime, the weight is adjusted upward by 0.1 to 0.2, and vice versa. Finally, the instruction number, standard action interval, logical relationship between instructions and associated weight are integrated to form the hanging basket contraction instruction sequence.

[0023] Please refer to Figure 3 , the automatic control module comprises: The action logic extraction submodule calls the hanging basket contraction instruction sequence, extracts the logical chain and operational dependency between the hanging basket components, analyzes the dependency strength and logical order, identifies the corresponding timing and priority between operations, draws a dependency graph and optimizes the sequence arrangement, and obtains the action logic relationship matrix. The sequence of basket contraction calling instructions is called, which lists the number, action interval, dependency relationship and associated weight of each instruction in detail. By traversing each instruction and its dependency field in the instruction sequence, all types of dependency relationships such as preposition, parallel and mutual exclusion are identified. For example, it is identified that BAK001 is a direct preposition dependency of MOT001_UP10 and MOT002_UP10. Then the dependency strength and logical order are analyzed. The dependency strength is directly derived from the associated weight set in the instruction sequence. For example, the associated weight between MOT001_UP10 and BAK001 is 1.0, indicating that its dependency strength is the highest. The logical order is determined by constructing a directed graph. In the graph, the node represents the instruction, the edge represents the dependency relationship, and the weight of the edge is the associated weight, which identifies the corresponding timing and priority between operations. For example, the completion time of BAK001 is defined as T_BAK001_end, and the start time T_MOT001_start of MOT001_UP10 must satisfy T_MOT001_start >= T_BAK0001_end. For parallel instructions in the same time window, the priority is determined by its safety criticality. For example, the priority of the safety brake instruction is higher than that of the general motor control instruction. The dependency relationship between all instructions is represented by constructing a weighted directed acyclic graph, in which the weight of the edge reflects the associated strength. The critical path algorithm is used to analyze the dependency graph, identify the critical path, and optimize the sequence arrangement on this basis. For example, if MOT001_UP10 and MOT002_UP10 are found to be parallel, and the execution time is about 5.0 seconds, the module will ensure that the scheduler can start both instructions at the same time, thereby shortening the total contraction time. The optimized sequence arrangement ensures that the basket contraction process time is minimized under the premise of meeting all dependency relationships and safety constraints. The extracted logical relationship, dependency strength, relative timing, priority and optimized sequence arrangement are integrated to form an action logic relationship matrix. Each row and each column of the matrix represents an instruction, and the elements in the matrix record the detailed logical relationship between the instructions.

[0024] The transmission path calculation submodule calculates the load balancing value of the instruction transmission path according to the action logic relationship matrix, combines the communication network type and the signal transmission protocol, and uses the formula: ; to verify the path stability and delay, make real-time dynamic adjustment, and select the optimal instruction transmission path. Based on the action logic relationship matrix, which details the dependency order, relative timing, and priority of operations between basket components, for example, matrix element M[MOT001_UP10][BAK001] indicates that MOT001_UP10 can only begin after BAK001 is completed. Path calculation is performed by combining the communication network type and signal transmission protocol. For example, industrial Ethernet (such as the Profinet protocol) or wireless 5G network can be selected for command transmission. Each network type has unique bandwidth, latency, and reliability characteristics. The module uses a formula... Calculate the load balancing value of the command transmission path, where, This represents the load balancing value of the command transmission path. This value measures the efficiency and stability of command transmission on a given path. The smaller the value, the more balanced the path load and the more stable the operation. Indicates the first The first in the path The utilization rate of a link is the ratio of the actual data traffic carried by the link within a specific time period to its maximum capacity. For example, if a 100Mbps link actually transmits 50Mbps of data, its utilization rate is 0.5. Indicates the first The first in the path The stability factor of a link reflects its ability to maintain stable transmission under the current communication environment. The stability factor is quantified as a value between 0 and 1, where 1 represents optimal stability (e.g., zero packet loss rate, extremely low latency) and 0 represents complete instability. This stability factor is calculated based on comprehensive performance indicators such as real-time monitoring of packet loss rate, jitter, and bit error rate. For example, when the packet loss rate is below 0.1% and the jitter is less than 1ms, When the packet loss rate is higher than 5% and the jitter is greater than 10ms, the value is set to 0.9. Set to 0.3. Indicates the first The first in the path The packet forwarding rate of a link, measured in Mbps, represents the amount of data that the link can forward per second. Representing a path Total number of links in the middle, summation symbol Indicates the path The calculation results of all links in the process are summed. The logical approach of this formula is to calculate link utilization. With stability factor The absolute difference between them is used to assess the non-ideal state of the link. Deviation When the value is too large, whether it is too high (indicating that the link is overloaded and unstable) or too low (indicating that the link resources are not fully utilized), it will increase the absolute difference, thereby affecting the overall load balancing value , and then multiplying the absolute difference by the packet forwarding rate of the link , so that the link with a high forwarding rate has a greater weight in the overall load balancing calculation, ensuring that the non-ideal state of the key link can be more significantly reflected, and then summing the weighted differences of all links and dividing by the total weighted forwarding rate to obtain a comprehensive, weighted average load balancing value ; The advantage of this formula is that not only does it focus on the utilization rate of the link, but also introduces a stability factor as a key parameter for measuring the health status of the link, and uses the forwarding rate for weighting, making the evaluation of load balancing more comprehensive and accurate, and effectively identifying and avoiding potential transmission risks caused by link instability or improper allocation of resources to key links. The load balancing value is compared with the preset load balancing threshold, which is set to 0.2 based on analysis of historical data and system performance requirements, and when the value is below the threshold, the path is considered to be load balanced and stable, and the end-to-end delay and packet loss rate of the path are also verified, for example, only paths with a delay below 20 ms and a packet loss rate below 0.5% are selected, and if the value of a certain path is too high, or the delay and packet loss rate do not meet the requirements, real-time dynamic adjustment is performed, such as recalculating the weight or trying alternative links, until the optimal instruction transmission path is selected, which ensures the timeliness and reliability of instruction transmission; Table 1: Transmission path link parameter table: ; As shown in Table 1, the utilization rate, stability factor, and forwarding rate of each link in the two transmission paths are listed for calculating the load balancing value L. For path 1, the total number of links : ; ; ; For path 2, the total number of links : ; ; ; The calculation results show that the load balancing value of path 1 is , while the load balancing value of path 2 is . Comparing the two values with the preset load balancing threshold of 0.2, the The value below 0.2 indicates that the load is balanced and stable, and the value of path 2 is The value above 0.2 indicates that the load is not balanced or stable to some extent, so path 1 will be selected as the current optimal instruction transmission path. The result shows that path 1 can provide more efficient and stable instruction transmission service under the current network condition, ensuring the reliable delivery of the hanging basket control instruction.

[0025] The dynamic path generation submodule identifies the function unit number and action logic chain based on the optimal instruction transmission path, determines the transmission priority and time floating range, dynamically adjusts the path based on the real-time network state, and generates a hanging basket dynamic control model; Based on the optimal instruction transmission path, for example, path 1 with low load balancing value, first identify the function unit number and action logic chain, bind each instruction number in the instruction sequence with the specific function unit of the hanging basket (such as motor controller MCT01), and associate it with the action logic chain output by the action logic extraction submodule (such as BAK001 must be before MOT001_UP10), and determine the exact position of each instruction in the control flow, then determine the transmission priority and time floating range, the transmission priority is set according to the importance of the instruction in the action logic chain and its impact on the safe operation of the hanging basket, for example, the transmission priority of the safety brake related instruction BAK001 is set to the highest (such as the value 10), the time floating range represents the tolerable transmission delay without affecting the overall action timing and safety. For example, for MOT001_UP10, it must be executed before BAK001 is completed. If the standard execution time of BAK001 is 0.1 seconds, the maximum allowed transmission delay is 50 milliseconds, then its time floating range is set to [0 milliseconds, 50 milliseconds]. Dynamic adjustment of the path based on real-time network state, continuously monitor the actual network performance indicators of the current optimal instruction transmission path, including real-time bandwidth utilization, end-to-end delay and packet loss rate, if the monitoring data shows that the current path performance is declining, for example, the delay exceeds the upper limit of 50 milliseconds, or the packet loss rate exceeds the preset threshold of 1%, the backup path evaluation program will be started immediately, the load balancing value of the backup path will be recalculated, and the new optimal path will be selected according to the new network state. For example, if path 1 performance deteriorates, it will quickly switch to suboptimal path 2 to ensure the continuity and reliability of instruction transmission, and all information, including function unit and logic chain binding relationship, instruction transmission priority, allowed time floating range, and current active optimal instruction transmission path and real-time adjustment strategy, will be integrated to generate a hanging basket dynamic control model.

[0026] Please refer to Figure 4 The remote control terminal module comprises: The action content matching submodule calls the hanging basket dynamic control model, extracts the action content and operation requirements of the hanging basket functional units, analyzes the action complexity and technical dependency, identifies the relationship and coordination requirements between functional units, and generates an action content matching table; The hanging basket dynamic control model is called, which includes the instruction information, logic chain, priority, and optimal instruction transmission path of each functional unit of the hanging basket. For example, the model records the execution parameters of instruction MOT001_UP10 (motor 1 starts uplink 10 cm). First, the action content and operation requirements of the hanging basket functional units are extracted. For example, the action content of motor controller MCT01 includes "start uplink", "stop", "accelerate", "decelerate", etc., and the operation requirements cover parameters such as "maximum speed 1200 rpm", "maximum torque 50 Nm", "positioning accuracy ±1 mm", etc. Then, the action complexity and technical dependency are analyzed. The analysis of action complexity is based on the number of components involved, the degree of coordination, and the required computing resources. For example, a single sensor reading instruction is rated as "low complexity", and a multi-motor synchronous driving instruction is rated as "high complexity". The technical dependency analysis identifies the prerequisites or external inputs required to complete the action. For example, the technical dependency of MOT001_UP10 is the completion signal of BAK001 (release safety brake). Then, the relationship and coordination requirements between functional units are identified. For example, MCT01 and MCT02 (motor 2 controller) must maintain synchronization of speed and displacement when executing the overall uplink or downlink instruction of the hanging basket, and their coordination requirement is "position deviation ≤2 mm", which requires real-time data exchange and coordinated control between them. The extracted action content, operation requirements, complexity evaluation, technical dependency, and relationship and coordination requirements between functional units are integrated to generate an action content matching table, which provides detailed and executable instruction instructions for the remote control terminal.

[0027] The protection mechanism embedding submodule standardizes the safety protection mechanism through mechanical transmission devices based on the action content matching table, embeds the action content in the matching table, performs safety checks and risk assessments, and forms a safety protection mechanism index. Based on the action content matching table, the table defines the action content, operation requirements and coordination relationship of each functional unit of the hanging basket in detail, and the safety protection mechanism is standardized by mechanical transmission device. For example, for the mechanical limit switch, emergency brake and anti-inclination mechanism of the hanging basket, parameterization setting and interface standardization are carried out to ensure that they can be accurately recognized and operated. For example, the trigger signal of the upper limit switch is defined as LS_UP_TRG, and its trigger condition is set as "activated when the hanging basket reaches 10mm above the highest preset position". The standardized safety protection mechanism is embedded in each action content in the action content matching table. For example, in the instruction of MOT001_UP10 (motor 1 up 10 cm), "LS_UP_TRG not activated" is embedded as the pre-execution safety condition, and "if LS_UP_TRG is activated, emergency stop is triggered immediately" is embedded as the runtime safety strategy. Then, safety check and risk assessment are performed. The safety check refers to checking whether all embedded safety conditions are met before the instruction is executed. For example, before executing the up instruction, check whether all brake components have been released and no limit switch has been touched. Risk assessment is to quantify the failure mode and its impact on safety by failure mode and effects analysis (FMEA) method. For example, if MCT01 fails during the execution of MOT001_UP10, resulting in overspeed, its risk value is evaluated as 9 (high), and the evaluation result is used to adjust the safety strategy. Finally, all embedded safety protection mechanisms, their corresponding action conditions, runtime strategies and risk levels are integrated to form a safety protection mechanism index.

[0028] The action execution generation submodule calls the safety protection mechanism index, combines the abnormal handling strategy list and the action step sequence to generate the standardized action execution file of the corresponding functional unit, marks the execution file number, integrates and verifies the execution file and the step sequence, and forms the hanging basket standardized action execution set; The safety protection mechanism index is called, which lists in detail the safety conditions, runtime policies and risk levels related to each action. For example, for the instruction MOT001_UP10, the index indicates that its preconditions are that LS_UP_TRG is not activated, and the runtime policy is to immediately emergency stop if LS_UP_TRG is activated. The corresponding standardized action execution file is generated in combination with the abnormal handling strategy list and the action step sequence, and the abnormal handling strategy list defines specific measures to be taken when facing various abnormal situations. For example, the strategy for motor overload exception is "try to reduce speed and retry 3 times, and if it still fails, issue an alarm and shut down", and the action step sequence breaks down each high-level action into a series of precise low-level instructions, such as "release brake BAK001", "start motor 1 up MCT001_UP10" and other atomic instructions and their timing. Then a corresponding standardized action execution file is generated for each functional unit, which is a structured data package containing instruction numbers, parameters, safety conditions, exception handling logic and execution order, for example, the file generated for motor controller MCT01 will contain all the execution details of MOT001_UP10 instruction, including its start parameters, current and speed monitoring thresholds during operation, and specific handling procedures if overload occurs. A unique execution file number is marked for each generated execution file, such as EXF_MOT001_UP10_V1.0, and then the execution file and step sequence are integrated and verified. Integration is to logically associate the execution files of all functional units according to the action step sequence to ensure the coherence of the overall operation. For example, ensure that the start instruction of MCT01 will not be issued before the brake release instruction of SBA01. Verification is to check whether the execution file meets all safety constraints, logical dependencies and performance requirements through static code analysis and simulation, for example, simulate the execution of MOT001_UP10 in a simulation environment while checking whether LS_UP_TRG is triggered at the wrong time to ensure the robustness of the program, and finally form a standardized action execution set for the hanging basket.

[0029] Please refer to Figure 5 The data communication optimization module comprises: The exception solution aggregation submodule calls the standardized action execution set for the hanging basket, extracts the exception solutions uploaded in real-time operation, analyzes the applicable scenarios and solution efficiency of the solutions, identifies high-frequency appearing exception types or common problems, integrates problem handling steps and effective strategies, and classifies to form a problem handling solution library; A high-frequency appearing exception type or common problem refers to an exception type or common problem that, when it appears, its cumulative occurrence frequency exceeds a preset frequency threshold of fifteen times within thirty consecutive natural days. The standardized action execution set of the hanging basket is called, which contains a standardized instruction package for all actions of the hanging basket, such as EXF_MOT001_UP10_V1.0. Abnormal solutions uploaded during real-time operation are continuously extracted. When an abnormality occurs during the execution of the standardized action of the hanging basket, the type, time, context environment and final solution of the abnormality are recorded. The solution can be an operation step of manual intervention or an automatic triggering recovery program. Then, the applicable scenarios and solution efficiency of the solution are analyzed. The analysis of the applicable scenarios includes judging whether the solution is only effective for a specific component, environmental condition or instruction sequence. For example, if the solution for "motor overload" is only successful in a low-temperature environment, the applicable scenario is limited to "low temperature", and the solution efficiency is evaluated as "the time required from the occurrence of the abnormality to the solution of the problem" and "the downtime of the equipment caused by the solution". For example, a solution can solve the "sensor data drift" problem within 5 minutes and does not cause downtime, so its solution efficiency is rated as high. Identify high-frequency abnormal types or common problems. The criteria for identifying high-frequency abnormal types or common problems are: when the abnormal type or common problem occurs more than a preset frequency threshold of 15 times within 30 consecutive natural days, for example, if the "motor 1 startup failure" type of abnormality has occurred 18 times from January 1 to January 30, the abnormality is identified as a high-frequency abnormality. Then integrate the problem handling steps and effective strategies, compare multiple solutions for the same or similar abnormality, and extract the most common, most effective and shortest downtime handling steps and practical strategies. For example, for the "communication interruption" problem, if "switching to standby network" is more effective and faster than "restarting communication", the former is integrated as the preferred strategy, and finally classified to form a problem handling solution library.

[0030] The high-frequency problem extraction submodule calculates the high-frequency problems and key operation points based on the problem handling solution library, using the formula: ; Calculate the weight value of the high-frequency problem, analyze the cause and frequency of the problem, filter the key problems and key operation points, mark and organize them into tags, and obtain the high-frequency problem tags; The high-frequency problem weight value is determined by the problem occurrence frequency, the association strength of the key operation point and the problem, and the solution efficiency factor, and is calculated by a self-matching multi-level fuzzy evaluation algorithm; Based on the problem processing scheme library, which contains information such as various exception types, processing steps, and solution efficiency, for example, the library records the solution strategy of the "motor overload" problem and its 0.8 solution efficiency, first, the high-frequency problems and key operation points are counted, the identification of high-frequency problems directly quotes the definition mentioned above, that is, the number of occurrences within thirty consecutive days is more than fifteen times, and the key operation points refer to the instruction steps that are identified as high correlation weight or involve multi-component cooperation in the hanging basket contraction instruction sequence, for example, BAK001 (release safety brake) or multi-motor synchronous start instruction, the formula Calculate the high-frequency problem weight value, where, represents the high-frequency problem weight value, which measures the comprehensive importance of a specific high-frequency problem in the system, the larger the value, the more important the problem, the more it needs to be considered in the knowledge base update, represents the frequency of the th problem, for example, the "motor overload" problem occurs 20 times in the statistical period, represents the correlation strength of the th problem and the key operation point, which is a value between 0 and 1, 1 means that the problem occurs directly or seriously affects the key operation point, 0 means no correlation, this correlation strength is determined by analyzing the instruction context and action logic relationship matrix when the problem occurs, for example, if the "motor overload" problem often occurs during the execution of MOT001_UP10 (motor 1 up 10 cm), and MOT001_UP10 is a key synchronous operation point, then its A_k value can be set to 0.9, represents the efficiency of solving the problem, which is a value between 0 and 1, 1 means that the problem can be solved very quickly and successfully, 0 means that the solution efficiency is very low, this efficiency value comes from the solution efficiency evaluation recorded in the problem processing scheme library, for example, 0.8 means that the problem can be solved quickly and effectively, represents the total number of problems, the summation symbol indicates that all problems identified as high-frequency problems and requiring weight evaluation are added up; the logical idea of this formula is to evaluate the importance of the problem by multiplying the frequency of the problem, its correlation strength with the key operation point, and the solution efficiency; The benefit of this formula is that not only does it identify frequently occurring and key operation problems, but it also distinguishes those important problems that have efficient solutions, thus enabling the knowledge base update to focus more on key problems that have low solution efficiency or need further optimization, the module then analyzes the causes and frequency of the problem, for example, for the frequently occurring "motor overload" problem, it analyzes its historical data and finds that it mainly occurs at the beginning of the hanging basket start and is related to excessive load or insufficient lubrication, the module updates the knowledge base based on the weight value Screening key problems and key operation points, setting a high-frequency problem weight threshold, for example, 0.5, only selecting problems with a value higher than 0.5 as key problems, and marking and sorting them into labels, for example, "Motor 1_Start overload_high frequency high weight_lubrication deficiency", and finally obtaining a high-frequency problem label list, for example, ["Motor 1_Start overload_high frequency high weight_lubrication deficiency", "Sensor 2_Drift_high frequency medium weight_environment temperature fluctuation"], which accurately points out the operation problems in the current system that need to be paid attention to and have improvement potential; Table 2: High-frequency problem parameter table: ; As shown in Table 2, the occurrence frequency, association strength with key operation points, and solution efficiency of three high-frequency problems are listed for calculating the high-frequency problem weight value W; Total number of problems ; Calculation items of problem 1: ; Calculation items of problem 2: ; Calculation items of problem 3: ; ; ; The calculation result shows that the high-frequency problem weight value Compare this value with the preset high-frequency problem weight threshold 0.5, and the value 7.05 is significantly higher than 0.5, indicating that the problem set has high comprehensive importance in system operation. The result shows that the high-frequency problem is the most important and optimized problem point in the current system, which has a significant impact on the running efficiency and stability of the hanging basket, and its solution efficiency is the key direction of knowledge base updating and strategy optimization. Through this weight value, the knowledge base updating submodule can prioritize problems with high weight values, ensuring the continuous improvement of the system's intelligent level.

[0031] The knowledge base updating submodule performs incremental updating on the basic control logic library according to the high-frequency problem label, combines the key operation point index and the term standardization table, records the knowledge base updating frequency, and constructs the intelligent hanging basket remote control strategy knowledge base; According to the high-frequency problem label, such as ["Motor 1 Start-up Overload High Frequency High Weight Insufficient Lubrication", "Sensor 2 Drift High Frequency Medium Weight Environmental Temperature Fluctuation"]. Combined with the key operation point index, which is a list containing all the key instructions of the hanging basket and its related components, such as MOT001_UP10 is identified as a key operation point, and the term standardization table, which ensures the naming and definition of all components, instructions, exceptions and solutions remain consistent throughout the system, such as standardizing "overload" as OVERLOAD. Incremental updating of the basic control logic library means that the existing library will not be completely replaced, but according to the problem causes and effective solutions revealed in the high-frequency problem label, specific control rules and logic will be modified or added. For example, for the label "Motor 1 Start-up Overload High Frequency High Weight Insufficient Lubrication", the start-up check logic of MOT001_UP10 instruction in the basic control logic library will be modified, "check motor lubricating oil level" and "preheat motor 1 in low temperature environment" will be added as preconditions, or the start-up current curve will be adjusted to avoid overload risk. If the problem revealed by the label is new and there is no existing logic to cover it, new control logic will be added, and the knowledge base update frequency will be recorded. For example, every time an incremental update is made, the update time, update content and update personnel / system will be recorded, and the number of updates in a natural month will be regularly counted, such as this month's knowledge base update frequency is 3 times. Through incremental updating and frequency recording, the intelligent hanging basket remote control strategy knowledge base is finally constructed.

[0032] Please refer to Figure 6 , the action instruction distribution module includes: The file version management submodule calls the intelligent hanging basket remote control strategy knowledge base, identifies the version number and update time of the action instruction file, compares the current version with the original version, selects the current version file, and generates a file version mapping table; The intelligent basket remote control strategy knowledge base is called, which contains the control logic and strategy of all basket operation instructions. For example, for the high-level instruction of "basket up", the knowledge base stores three versions of action instruction files, V1.0, V1.1 and V2.0. First, the version number and update time of the action instruction file are identified. For each action instruction file in the knowledge base, the version number (e.g. V2.0) and the last update time (e.g. 09-2010:30:00) recorded in its metadata are read, and then the current version is compared with the original version. For example, for the "basket up" instruction, the three versions V1.0 (updated on 03-15), V1.1 (updated on 08-01) and V2.0 (updated on 09-20) are compared, and the current version file is selected by comparing the update time stamp. The file corresponding to the latest time stamp is determined as the latest version. For example, in the above example of "basket up" instruction, V2.0 is selected as the latest version because its update time 09-2010:30:00 is the latest. At the same time, the incremental logic of the version number is also considered, that is, V2.0 is newer than V1.1. The selected results are sorted to generate a file version mapping table, which clearly indicates the specific file version that should be used for each logical control instruction at present, for example, the table entry is "logical instruction: basket up, latest version file: / path / to / RetractBasket_V2.0.bin".

[0033] The distribution path generation submodule identifies the distribution path of the corresponding functional unit based on the file version mapping table, combines the operator login account and the functional unit number, records the path identifier and the update time stamp, and generates a remote basket instruction distribution list; Based on the file version mapping table, which specifies the latest file version of each logical control instruction, for example, the "basket up" logical instruction corresponds to the file / path / to / RetractBasket_V2.0.bin. Combined with the operator login account and the function unit number, the instruction distribution path is determined. The operator login account is used to verify the operator's authority and is bound to a specific basket or function unit, for example, the account operator_A is authorized to operate the function unit of basket 01. The function unit number accurately specifies the target receiving end of the instruction, for example, MCT01 (Motor Controller 1) or SBA01 (Safety Brake Actuator 1), and then identifies the distribution path of the corresponding function unit. The determination of this path integrates the dynamic path generation sub-optimal instruction transmission path information. For example, if operator_A sends the "basket up" instruction to MCT01 of basket 01, it will query the file version mapping table to obtain RetractBasket_V2.0.bin. Then, according to the physical network address of MCT01 and the optimal transmission path determined in the dynamic control model (for example, through the industrial Ethernet Path_1), the complete instruction distribution path is constructed, for example, IP: 192.168.1.10 / Port: 502 / Protocol: Profinet / Path: Path_1. At the same time of determining the distribution path, the path identifier and the update timestamp are recorded. The path identifier is a unique string used to identify the specific transmission channel currently in use, for example, HB01_MCT01_Profinet_Path1_09221035, and the update timestamp records the time when the path is selected and used, for example, 09-2210:35:00 UTC. Through the above process, the remote basket instruction distribution list is finally generated. This list is a list containing all the instructions to be distributed, and each record includes the instruction file path, the target function unit number, the operator account, the distribution path identifier, the distribution priority, and the generation timestamp, etc.

[0034] The above is only a preferred embodiment of the present application, and does not limit the form of the present application in other ways. Any skilled person in the art can use the above disclosed technical content to make changes or modifications as equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made in accordance with the technical essence of the present application to the above embodiments, without departing from the technical solution content of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A remote control system for an automatic retractable hanging basket, characterized in that, The system includes: The main control unit identification module analyzes the hanging basket retraction parameters based on the built-in hanging basket retraction data, triggers the mechanical actuator action, monitors the force on the hanging basket connection part, calibrates the status of the communication component, collects the action sequence for real-time feedback, and generates the hanging basket retraction command sequence. The automatic control module calls the hanging basket retraction command sequence, extracts the operating logic and operational dependencies of the hanging basket components, selects the optimal command transmission path based on the communication network type and signal transmission protocol, and establishes a dynamic control model for the hanging basket. The remote control terminal module calls the hanging basket dynamic control model, matches the action requirements and operation requirements of the hanging basket functional unit, uses the mechanical transmission device to complete the execution of the corresponding action commands, and embeds safety protection mechanisms and abnormal handling strategies to form a standardized action execution set for the hanging basket. The data communication optimization module calls the standardized action execution set of the hanging basket, combines it with the abnormal solutions uploaded during real-time operation, incrementally updates the basic control logic library, extracts high-frequency problems and key operation points, and constructs a knowledge base for intelligent hanging basket remote control strategies.

2. The automatic retractable hanging basket remote control system according to claim 1, characterized in that, The basket retraction command sequence includes command number, action execution range, operation dependency, equipment status identifier, and communication protocol mapping value. The basket dynamic control model includes functional unit number, action logic chain, load balancing parameters, priority label, and time fluctuation range. The basket standardized action execution set includes functional unit action content, safety protection mechanism index, anomaly handling strategy list, execution file number, and action step sequence. The intelligent basket remote control strategy knowledge base includes problem handling solution library, high-frequency problem label, key operation point index, knowledge base update frequency, and terminology standardization table.

3. The automatic retractable hanging basket remote control system according to claim 1, characterized in that, The main control unit identification module includes: The instruction generation submodule, based on the built-in hanging basket retraction data, removes invalid instruction records caused by equipment failure or unplanned downtime, identifies the average execution time of each component of the hanging basket under the same action, and generates action baseline values. The action range calculation submodule identifies the standard action range of the hanging basket component based on the action benchmark value, combined with the differentiated equipment operating status and environmental factors, and marks the upper and lower limits of the range to obtain the action distribution feature dataset. The instruction sequence construction submodule calls the action distribution feature dataset to identify the logical relationships and operational dependencies between the hanging basket components, formulates the instruction structure and sets the association weights based on the instruction number and action range, and establishes the hanging basket contraction instruction sequence.

4. The automatic retractable hanging basket remote control system according to claim 3, characterized in that, The automatic control module includes: The action logic extraction submodule calls the basket retraction instruction sequence, extracts the logic chain and operation dependency between basket components, analyzes the dependency strength and logical order, identifies the corresponding timing and priority between operations, draws a dependency graph and optimizes the sequence arrangement, and obtains the action logic relationship matrix. The transmission path calculation submodule calculates the load balancing value of the instruction transmission path based on the action logic relationship matrix, combined with the communication network type and signal transmission protocol, verifies the path stability and latency, performs real-time dynamic adjustments, and selects the optimal instruction transmission path. The dynamic path generation submodule identifies the functional unit number and action logic chain based on the optimal instruction transmission path, determines the transmission priority and time fluctuation range, and dynamically adjusts the path in combination with the real-time network status to generate a hanging basket dynamic control model.

5. The automatic retractable hanging basket remote control system according to claim 4, characterized in that, The remote control terminal module includes: The action content matching submodule calls the hanging basket dynamic control model, extracts the action content and operation requirements of the hanging basket functional units, analyzes the action complexity and technical dependence, identifies the relationship and coordination requirements between functional units, and generates an action content matching table. The protection mechanism embedding submodule, based on the action content matching table, standardizes the safety protection mechanism through a mechanical transmission device, embeds the action content in the matching table, performs safety checks and risk assessments, and forms a safety protection mechanism index. The action execution generation submodule calls the security protection mechanism index, combines the exception handling strategy list and action step sequence to generate standardized action execution files for the corresponding functional units, marks the execution file number, integrates and verifies the execution files and step sequence, and forms a standardized action execution set for the hanging basket.

6. The automatic retractable hanging basket remote control system according to claim 5, characterized in that, The data communication optimization module includes: The abnormal solution collection submodule calls the standardized action execution set of the hanging basket, extracts the abnormal solutions uploaded during real-time operation, analyzes the applicable scenarios and resolution efficiency of the solutions, identifies frequently occurring abnormal types or common problems, integrates problem handling steps and effective strategies, and classifies them into a problem handling solution library. The high-frequency problem extraction submodule, based on the problem handling solution library, counts high-frequency problems and key operation points, calculates the weight value of high-frequency problems, analyzes the causes and frequency of problems, filters key problems and key operation points, marks and organizes them into tags, and obtains high-frequency problem tags. The knowledge base update submodule incrementally updates the basic control logic library based on the high-frequency question tags, combined with the key operation point index and terminology standardization table, records the knowledge base update frequency, and constructs a knowledge base for intelligent hanging basket remote control strategies.

7. The automatic retractable hanging basket remote control system according to claim 6, characterized in that, The term "high-frequency abnormal type or common problem" refers to an abnormal type or common problem that is identified as a high-frequency abnormal type or common problem when its cumulative occurrence exceeds a preset frequency threshold of fifteen times within thirty consecutive natural days. The weight value of the high-frequency problem is determined by the frequency of problem occurrence, the correlation strength between key operation points and the problem, and the efficiency of problem resolution, and is calculated by a self-matching multi-level fuzzy evaluation algorithm.

8. The automatic retractable hanging basket remote control system according to claim 1, characterized in that, The system also includes an action instruction distribution module: The action instruction distribution module calls the intelligent hanging basket remote control strategy knowledge base, automatically assigns the corresponding action instruction file of the functional unit according to the operator's login account and functional unit number, marks the file version number and update time, and outputs the remote hanging basket instruction distribution list. The remote hanging basket instruction distribution list includes the functional unit number, action instruction file number, file version number, update timestamp, and distribution path identifier.

9. The automatic retractable hanging basket remote control system according to claim 8, characterized in that, The action instruction distribution module includes: The file version management submodule calls the intelligent hanging basket remote control strategy knowledge base, identifies the version number and update time of the action instruction file, compares the current version with the original version, selects the current version file, and generates a file version mapping table. The distribution path generation submodule, based on the file version mapping table and combined with the operator's login account and functional unit number, identifies the distribution path of the corresponding functional unit, records the path identifier and update timestamp, and generates a remote hanging basket instruction distribution list.

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