Method and system for the synchronized control of the motorization of a lifting system for maintenance

Abstract

The application provides a kind of synchronous control method and system of electricization of overhaul lifting system, the real-time running state information of each electric actuator is collected, including running action and relative position relationship between mechanisms and other information, and a multi-dimensional running state atlas is constructed to represent the running correlation characteristics and cooperative action dependency of each mechanism. Then, a pre-trained synchronous control model is called to model the linkage relationship of the atlas and to mine the implicit correlation rules of the running state. Based on the implicit correlation rules, the corresponding synchronous drive adjustment instructions for each electric actuator are generated to realize the adaptive association with the running action information. Finally, the instructions are used to control the cooperative operation of each mechanism, and the running feedback state information is collected to form a linkage control. The application can effectively solve the poor cooperativity problem of traditional overhaul lifting system, improve the stability and efficiency of system operation, and reduce safety hazards.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence, and more specifically, to a synchronous control method and system for the electrification of a maintenance lifting system. Background Technology

[0002] In industrial production and equipment maintenance, maintenance lifting systems play a crucial role, with their operational stability and coordination directly impacting the efficiency and safety of maintenance operations. Traditional maintenance lifting systems often employ mechanical transmission or simple electrical control, lacking an effective collaborative control mechanism between the various actuators. During operation, each electric actuator typically operates independently, making dynamic adjustments based on overall operational needs and real-time status difficult. This leads to issues such as uncoordinated movements and positional deviations, affecting not only the accuracy and efficiency of lifting operations but also potentially causing safety hazards. Existing synchronization control technologies often focus on single-dimensional parameter control, such as simple synchronization of speed or position, failing to fully consider the complex operational correlations and collaborative dependencies between the electric actuators. This makes it impossible to achieve precise and efficient synchronous control of the maintenance lifting system. Therefore, developing a synchronous control method for the electrification of maintenance lifting systems that can comprehensively and deeply uncover the correlation patterns of the operating states of each electric actuator and achieve precise collaborative control is of significant practical importance. Summary of the Invention

[0003] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a synchronous control method for the electrification of a maintenance lifting system, the method comprising:

[0004] Collect real-time operating status information of each electric actuator in the maintenance lifting system. The real-time operating status information includes the operating action information of each electric actuator and the relative positional relationship information between the actuators.

[0005] A multi-dimensional operation status map is constructed based on the real-time operation status information. The multi-dimensional operation status map is used to characterize the operation association characteristics and cooperative action dependencies of each electric actuator.

[0006] The pre-trained synchronous control model is invoked to perform linkage relationship modeling on the multi-dimensional operation state map, and the implicit correlation patterns of the operation states of each electric actuator are explored.

[0007] Based on the implicit correlation rules, synchronous drive adjustment commands are generated for each electric actuator, and the synchronous drive adjustment commands are adapted and correlated with the operating action information of each electric actuator.

[0008] Based on the synchronous drive adjustment command, the electric actuators are controlled to operate in coordination, and the operating feedback status information of each electric actuator is collected to form a linkage control.

[0009] In another aspect, embodiments of the present invention also provide a synchronous control system for electrification of a maintenance lifting system, including a processor and a machine-readable storage medium connected to the processor. The machine-readable storage medium is used to store programs, instructions, or code, and the processor is used to execute the programs, instructions, or code in the machine-readable storage medium to implement the above-described method.

[0010] Based on the above, this invention, by collecting comprehensive real-time operational status information of each electric actuator and constructing a multi-dimensional operational status map, can accurately characterize the complex operational correlations and collaborative action dependencies among the actuators. A pre-trained synchronous control model is invoked to model the linkage relationships within the map, deeply exploring the implicit correlation patterns of the operational status of each electric actuator. This allows control strategies to be formulated based on the overall operational logic of the system, rather than being limited to a single actuator or simple parameters. The synchronous drive adjustment commands generated based on these implicit correlation patterns are highly compatible with the operational action information of each electric actuator, achieving precise collaborative operation among the actuators. Simultaneously, the collection of operational feedback status information forms a linkage control system, enabling real-time adjustments to the control strategy to ensure the system is always in optimal operating condition. This effectively improves the operational stability, coordination, and work efficiency of the maintenance lifting system, reduces safety hazards, and provides reliable technical support for industrial production and equipment maintenance. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the execution flow of the synchronous control method for electrification of the maintenance lifting system provided in an embodiment of the present invention.

[0012] Figure 2 This is a schematic diagram of exemplary hardware and software components of the synchronous control system for the electrification of the maintenance lifting system provided in an embodiment of the present invention. Detailed Implementation

[0013] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a synchronous control method for electrification of a maintenance lifting system according to an embodiment of the present invention. The synchronous control method for electrification of the maintenance lifting system will be described in detail below.

[0014] Step S110: Collect real-time operating status information of each electric actuator of the maintenance lifting system. The real-time operating status information includes the operating action information of each electric actuator and the relative positional relationship information between the actuators.

[0015] In this embodiment, a high-altitude operation and maintenance lifting platform is taken as an example. This lifting platform includes multiple electric actuators, such as an electric lifting arm, electric support legs, and an electric rotary table. When collecting real-time operating status information, data is acquired through sensors installed on each electric actuator. For the electric lifting arm, displacement sensors collect its lifting height, extension length, and other operating motion information, while angle sensors collect its swing angle. For the electric support legs, pressure sensors collect the support pressure, and displacement sensors collect the extension length and support position, among other operating motion information. For the electric rotary table, angular velocity sensors collect the rotation speed, and angle sensors collect the rotation angle, among other operating motion information. Simultaneously, laser rangefinders and infrared positioning sensors installed on the lifting platform collect information on the relative positional relationships between the electric actuators, such as the horizontal distance and vertical height difference between the electric lifting arm and the electric support legs, and the relative angle and distance between the electric rotary table and the electric lifting arm. The data collected by these sensors are aggregated to form real-time operating status information for each electric actuator. The operating action information includes the action type of each electric actuator (such as lifting, telescopic, rotating, supporting, etc.), the speed of action execution, acceleration, and current position parameters. The relative position relationship information between the actuators includes the coordinate position of each electric actuator in the spatial coordinate system, the distance between them, angles, and other relationship parameters.

[0016] Step S120: Construct a multi-dimensional operation status map based on the real-time operation status information. The multi-dimensional operation status map is used to characterize the operation association characteristics and cooperative action dependencies of each electric actuator.

[0017] Step S121: Extract the operation action information of each electric actuator from the real-time operation status information, and divide the action type information and action execution progress information.

[0018] In the high-altitude operation and maintenance lifting platform scenario of this embodiment, the operational action information of each electric actuator is extracted from the collected real-time operating status information. For the electric lifting arm, its action type information may be a lifting action. In this case, the action execution progress information can be determined by the ratio of the current lifting height to the maximum lifting height. For example, if the current lifting height is h and the maximum lifting height is H, then the action execution progress is h / H. If it is a telescopic action, the action execution progress is the ratio of the current telescopic length to the maximum telescopic length. For the electric support leg, the action type information is a support telescopic action, and the action execution progress is the ratio of the current telescopic length to the maximum telescopic length. At the same time, pressure data from pressure sensors is used to help judge the support progress. If the pressure reaches a preset stable pressure value, the support progress is considered to be relatively high. For the electric rotary table, the action type information is a rotation action, and the action execution progress is the ratio of the current rotation angle to the target rotation angle. These action type information and action execution progress information are organized to form a set of action type information and a set of action execution progress information for each electric actuator.

[0019] Step S122: Extract the relative positional relationship information between mechanisms from the real-time operating status information, and extract the spatial positional association information and action timing association information between each electric actuator.

[0020] Next, the relative positional relationship information between mechanisms is extracted from the real-time operating status information. In a lifting platform used for aerial work and maintenance, the spatial positional relationship information includes the coordinate positional relationship of each electric actuator in three-dimensional space. For example, if the coordinates of the electric lifting arm are (x1, y1, z1) and the coordinates of the electric support leg are (x2, y2, z2), then the spatial positional relationship information between them includes the horizontal distance √[(x1-x2)]. 2 +(y1-y2) 2 The vertical height difference is |z1-z2|, etc. The action timing correlation information is determined by analyzing the sequence and time intervals of the actions of each electric actuator. For example, the electric support leg first performs a telescopic support action, and only after the support is stable (determined by a pressure sensor) does the electric lifting arm begin its lifting action. Therefore, there is an action timing correlation between the support action of the electric support leg and the lifting action of the electric lifting arm, with the time interval being the time from when the electric support leg completes its support action to when the electric lifting arm begins its lifting action. Similarly, the rotation action of the electric rotary table may begin only after the electric lifting arm has reached a certain height; there is also an action timing correlation between them. These spatial position correlation information and action timing correlation information are organized to form a set of position and timing correlation information between each electric actuator.

[0021] Step S123: Use the action type information, action execution progress information, spatial location association information and action temporal sequence association information as graph node attributes to construct an initial state graph framework.

[0022] In this embodiment, each electric actuator is used as a node in the graph, and the action type information, action execution progress information, spatial position association information, and action timing association information are used as node attributes. For example, the electric lifting arm is used as a node, and its attributes include action type (lifting or telescopic, rotation, etc., depending on the actual action), action execution progress (the ratio of the current lifting height to the maximum lifting height), spatial position association information (the horizontal distance and vertical height difference with the electric support leg, the relative angle and distance with the electric rotary table, etc.), and action timing association information (the action time interval with the electric support leg, the action sequence with the electric rotary table, etc.). The electric support leg and electric rotary table are also set with node attributes in the same way. Then, based on the connection relationship and association between each electric actuator, an initial state graph framework is constructed, and each node is initially connected according to the actual position relationship and action association relationship to form an initial graph structure containing nodes and node attributes.

[0023] Step S124: Set the association weight between nodes based on the operation association characteristics of each electric actuator. The operation association characteristics are determined by the action coordination and position dependence.

[0024] Step S1241: Statistically analyze the action coordination of each electric actuator during historical operation and record the frequency relationship of different electric actuators performing related actions simultaneously.

[0025] In this embodiment, historical operating data of the aerial work platform for maintenance is retrieved to statistically analyze the coordination of actions among the various electric actuators during historical operations. For example, the number of times the electric lifting arm performs a lifting motion while the electric support leg is performing a supporting motion, and the number of times the electric rotary table rotates while the electric lifting arm is performing a telescopic motion, are counted. These counts are then used to establish the frequency relationships of related actions performed simultaneously by different electric actuators. For instance, in historical operations, the frequency of the electric lifting arm performing a lifting motion after the electric support leg completes its supporting motion is N1 times, and the frequency of the electric rotary table rotating motion while the electric support leg is performing its supporting motion is N2 times, etc. These frequencies reflect the coordination of actions among the various electric actuators.

[0026] Step S1242: Analyze the position dependence of each electric actuator when completing the lifting operation, and record the influence of the position change of a certain electric actuator on the action execution of other electric actuators.

[0027] Taking a high-altitude maintenance lifting platform as an example, this paper analyzes the position dependence of each electric actuator. When the position of the electric support leg changes, such as an increase or decrease in its telescopic length, it affects the adjustment of the lifting height and telescopic length of the electric lifting boom. This is because changes in the support leg position alter the overall stability and center of gravity of the lifting platform. Therefore, the electric lifting boom needs to adjust its movements according to the changes in the support leg position to ensure the platform's balance and operational safety. Similarly, changes in the position of the electric rotary table (such as changes in rotation angle and position) affect the adjustment of the telescopic length and lifting height of the electric lifting boom to ensure that the rotary table can deliver maintenance personnel and equipment to the correct maintenance position. The correlation between these position changes and the actions of other electric actuators is recorded. For example, when the change in the position of the electric support leg is Δx, the change in the lifting height of the electric lifting boom is Δh, and the change in the telescopic length is Δl, etc. These correlations reflect the position dependence.

[0028] Step S1243: Perform association mapping processing on the action coordination situation and the position dependency situation to form the running association feature description result.

[0029] In this embodiment, the frequency relationship of motion coordination and the influence of position dependence are correlated and mapped. For example, based on the motion coordination frequency N1 of the electric support leg and the electric lifting arm, and the influence of the electric support leg position change on the electric lifting arm motion (the relationship between Δx and Δh, Δl), a correlation mapping model is constructed. The frequency of motion coordination is converted into one correlation coefficient, and the influence of position dependence is converted into another correlation coefficient. Then, these two coefficients are fused to form the operational correlation feature description result. For example, the correlation coefficient of motion coordination is K1=N1 / N_total (where N_total is the total number of electric support leg movements), and the correlation coefficient of position dependence is K2=(Δh+Δl) / Δx (this is just a simple fusion method; in actual applications, more complex algorithms can be used depending on the specific situation). Then, K1 and K2 are weighted and fused to obtain the operational correlation feature description result K=α. K1+β K2 (where α and β are weighting coefficients, which can be adjusted according to the actual situation).

[0030] Step S1244: Set the initial benchmark for the association weights between nodes based on the running association feature description results. The degree of association between collaboration and dependence in the running association feature description results corresponds to the setting of the initial benchmark.

[0031] Based on the operational correlation feature description result K, an initial benchmark for the correlation weights between nodes is set. A larger value for K indicates a higher degree of coordination and dependence among the electric actuators, and thus a higher initial benchmark for the correlation weights between nodes. For example, when K is greater than a certain threshold K0, the initial benchmark for the correlation weights between nodes is set to W1; when K is less than or equal to K0, the initial benchmark is set to W2, where W1 > W2. In this way, the degree of coordination and dependence in the operational correlation feature description result is mapped to the setting of the initial benchmark, resulting in a higher initial benchmark for the weights between closely related electric actuator nodes.

[0032] Step S1245: Based on the operational requirements of the lifting system, dynamically adapt and adjust the initial benchmark to ensure that the correlation weights between nodes accurately correspond to the collaborative requirements in actual operations.

[0033] In different operational scenarios for aerial work platforms used for maintenance, such as maintenance on flat ground, on sloping ground, and in confined spaces, the requirements vary, leading to different coordination needs for the various electric actuators. In maintenance on flat ground, the stability of the electric support legs is crucial, while the rotational flexibility of the electric rotary table is relatively less critical. Therefore, in this scenario, it's necessary to adjust the inter-node correlation weights between the electric support legs and the electric lifting arm to more precisely match their coordination requirements. For example, increasing the correlation weight between the electric support legs and the electric lifting arm while decreasing the correlation weight between the electric rotary table and the electric lifting arm. In maintenance on sloping ground, the requirements for the extension and retraction length and position adjustment of the electric support legs are more stringent. Simultaneously, the lifting and extension movements of the electric lifting arm need more frequent adjustments based on changes in the support leg position. Therefore, adjusting the correlation weights between the electric support legs and the electric lifting arm is also necessary to better adapt to the coordination needs of this operational scenario. By analyzing the needs of different work scenarios, the initial baseline is dynamically adapted and adjusted. For example, different adjustment coefficients are set according to the type of work scenario (flat ground, sloping ground, narrow space, etc.) and the difficulty level of the work (simple, medium, complex). The initial baseline is multiplied by the adjustment coefficient to obtain the adjusted inter-node association weights, so that the inter-node association weights accurately correspond to the collaborative needs in actual work.

[0034] Step S1246: Record the adjusted inter-node association weights and their adaptation to the work scenario.

[0035] After dynamically adapting and adjusting the initial baseline, the adjusted inter-node association weights and their adaptation relationship to the work scenario are recorded. For example, in a maintenance work scenario on flat ground, the adjusted association weight between the electric support leg and the electric lifting arm is W11, and the adjusted association weight between the electric support leg and the electric rotary table is W12; in a maintenance work scenario on sloping ground, the adjusted association weight between the electric support leg and the electric lifting arm is W21, and the adjusted association weight between the electric support leg and the electric rotary table is W22, and so on. These adaptation relationships are recorded in detail, including the description of the work scenario, the specific values ​​of the adjusted inter-node association weights (this is just an example; in actual applications, relative proportions or levels can be used), and information such as the corresponding electric actuator nodes.

[0036] Step S125: Based on the node attributes and the association weights between nodes, a multi-dimensional operational status graph containing node hierarchical relationships and associated path information is generated using a graph structured modeling approach.

[0037] In this embodiment, based on the previously determined node attributes (action type information, action execution progress information, spatial location association information, and action timing association information) and the association weights between nodes, a graph neural network-based graph structured modeling approach is used to generate a multi-dimensional operational status graph. First, the nodes of each electric actuator are initialized according to their attributes. Then, the connection strength and connection method between nodes are determined based on the association weights. For nodes with high association weights, stronger connections are established, which can be represented in the graph by thicker lines or higher transparency. Simultaneously, based on the function of each electric actuator and its role in the lifting system, the hierarchical relationship of the nodes is determined. For example, the electric support leg, as a basic support mechanism, is located at the bottom layer of the graph; the electric lifting arm, as the main lifting actuator, is located in the middle layer; and the electric rotary table, as an auxiliary rotary actuator, is located at the top layer. In the process of constructing the graph, it is also necessary to consider the associated path information, that is, the path of action and position association between each electric actuator. For example, the action of the electric support leg affects the action of the electric lifting arm through the overall stability of the lifting platform, and the action of the electric lifting arm affects the action of the electric rotary table through the change of the working position. These associated path information are also incorporated into the construction of the graph to generate a multi-dimensional operation status graph containing node hierarchy and associated path information. This multi-dimensional operation status graph can clearly show the operation association characteristics and collaborative action dependencies of each electric actuator.

[0038] Step S126: Redundant paths are removed from the multi-dimensional operational status graph using graph optimization rules, while core associated paths directly related to collaborative actions are retained.

[0039] In this embodiment, graph optimization rules are formulated. For example, factors such as the length of the associated path, the magnitude of the associated weight, and the frequency of action coordination are used to determine whether a path is redundant. Paths with excessively long associated paths, low associated weights, or infrequent action coordination are identified as redundant and eliminated. For instance, in the multi-dimensional operational status graph, there exists an associated path from the electric support leg through multiple intermediate nodes (such as some auxiliary sensor nodes or unrelated actuator nodes) to the electric lifting arm. This path has a low associated weight and infrequent action coordination, so it is eliminated according to the graph optimization rules. Core associated paths directly related to coordinated actions are retained, such as the associated path from the electric support leg directly to the electric lifting arm, and the associated path from the electric lifting arm directly to the electric rotary table. These paths have high associated weights, high action coordination frequencies, and are directly related to the coordinated actions of each electric actuator. By eliminating redundant paths, the multi-dimensional operational status graph becomes more concise and clear.

[0040] Step S130: Call the pre-trained synchronous control model to perform linkage relationship modeling on the multi-dimensional operating state map, and explore the implicit correlation patterns of the operating states of each electric actuator.

[0041] Step S131: Input the multi-dimensional running state map into the map embedding layer of the synchronization control model, perform joint extraction processing of node features and associated path features, and generate map embedding feature vector.

[0042] In this embodiment, the pre-trained synchronization control model employs a deep learning model, whose graph embedding layer contains multiple convolutional layers and fully connected layers. After the multi-dimensional operational state graph is input into the graph embedding layer, features are first extracted from the nodes and associated paths of the graph through the convolutional layers. The convolutional kernels of the convolutional layers slide across the nodes and paths of the graph, extracting the attribute features of the nodes (such as action type information, action execution progress information, spatial location association information, action temporal association information, etc.) and the features of the associated paths (such as path length, association weight, action coordination frequency, etc.). Then, the extracted features are integrated and transformed through the fully connected layers, and the node features and associated path features are jointly processed to generate a graph embedding feature vector. This graph embedding feature vector is a multi-dimensional vector that contains comprehensive information on the node features and associated path features of each electric actuator, and can comprehensively represent the features of the multi-dimensional operational state graph.

[0043] Step S132: The association mining module of the synchronous control model performs hierarchical association analysis on the embedded feature vector of the map, and analyzes the surface association information of the operating status of each electric actuator layer by layer.

[0044] Step S1321: The embedded feature vector of the graph is grouped according to the node attribute category to form feature subsets with different attribute dimensions.

[0045] In this embodiment, the graph embedding feature vector includes features of different attribute categories, such as action type information, action execution progress information, spatial location association information, and action temporal sequence association information. The graph embedding feature vector is grouped according to these attribute categories to form feature subsets with different attribute dimensions, such as action type feature subset, action execution progress feature subset, spatial location association feature subset, and action temporal sequence association feature subset. For example, all features related to action type are extracted to form the action type feature subset; all features related to action execution progress are extracted to form the action execution progress feature subset, and so on.

[0046] Step S1322: Perform hierarchical division for each feature subset and set hierarchical division criteria.

[0047] For each feature subset, a hierarchical classification standard is defined. Taking the action type feature subset as an example, the hierarchical classification standard can be set according to the complexity of the action and its importance to the lifting system operation. For example, basic actions (such as extension, lifting, and rotation) are set as the first level, combined actions (such as lifting plus rotation, extension plus lifting) are set as the second level, and complex actions (such as compound actions that automatically adjust according to the work scenario) are set as the third level. For the action execution progress feature subset, the hierarchical classification standard can be set according to the completion rate of the action execution, such as 0-30% completion rate as the first level, 30-70% as the second level, and 70-100% as the third level. For the spatial position association feature subset, the hierarchical classification standard can be set according to the distance and angle range of the relative position, such as 0-1 meter relative distance and 0-30 degree angle as the first level, 1-3 meter relative distance and 30-60 degree angle as the second level, and 3-5 meter relative distance and 60-90 degree angle as the third level, etc. For the action temporal correlation feature subset, the hierarchical division criteria can be set according to the time interval of the actions. For example, a time interval of 0-5 seconds is the first level, 5-10 seconds is the second level, 10-20 seconds is the third level, and so on.

[0048] Step S1323: In the first level, extract the motion association features and position association features that directly correspond between different electric actuators.

[0049] In the first level (basic action level) of the action type feature subset, action correlation features directly corresponding to different electric actuators are extracted. For example, the correlation features between the extension / retraction of the electric support leg and the lifting / lowering of the electric lifting arm, and the correlation features between the lifting / lowering of the electric lifting arm and the rotation of the electric rotary table. In the first level (relative distance and small angle level) of the spatial position correlation feature subset, position correlation features directly corresponding to different electric actuators are extracted. For example, the position correlation features between the electric support leg and the electric lifting arm are characterized by a short horizontal distance and a small vertical height difference, and the position correlation features between the electric rotary table and the electric lifting arm are characterized by a small relative angle and a short distance. These directly corresponding action correlation features and position correlation features are extracted to form the first level of correlation feature set.

[0050] Step S1324: In the intermediate level, analyze the derived relationships between basic related features and explore the collaborative adaptation features of different actuators under the same action type.

[0051] In the second level of the motion type feature subset (combined motion level), the derived relationships between basic associated features are analyzed. For example, the composite motion formed by the extension and retraction of the electric support leg and the lifting and lowering motion of the electric lifting arm is analyzed. The relationship between this composite motion and the rotation motion of the electric rotary table is analyzed. The collaborative adaptation features of different actuators (electric lifting arm and electric rotary table) under the same motion type (such as lifting motion) are explored. That is, when the electric lifting arm is raised to a certain height, what angle does the electric rotary table need to rotate to better cooperate with the lifting arm to complete the operation, and the collaborative adaptation relationship of their motion speed and acceleration, etc. In the second level of the spatial position associated feature subset (relative distance and angle appropriate level), the derived relationships between basic position associated features are analyzed. For example, the influence of changes in the horizontal distance and vertical height difference between the electric support leg and the electric lifting arm on the relative angle and distance between the electric rotary table and the electric lifting arm is analyzed. The collaborative adaptation features of different actuators under the same spatial position associated type are explored. That is, how the position change of the electric support leg affects the relative position relationship of the electric lifting arm and the electric rotary table, and the collaborative adaptation relationship of their position adjustment, etc.

[0052] Step S1325: In the last level, integrate the basic association features and derived association relationships to form a comprehensive association feature covering multi-dimensional attributes.

[0053] In the third level (complex action level) of the action type feature subset, basic association features (such as association features of basic actions) and derived association relationships (such as association relationships of combined actions) are integrated to form comprehensive association features covering multiple dimensions such as action type, action execution progress, spatial position association, and action timing association. For example, integrating the association features of the extension and retraction of an electric support leg, the lifting and extension of an electric lifting arm, and the rotation of an electric rotary table, as well as the derived association relationships between them, forms a comprehensive action association feature that covers multiple dimensions such as action type, action execution progress, and action timing. In the third level (level of relatively far distance and large angle) of the spatial position association feature subset, basic position association features (such as position association features of relatively short distance and small angle) and derived position association relationships (such as position association relationships of moderate relative distance and angle) are integrated to form comprehensive association features covering multiple dimensions such as spatial position, relative distance, and angle. These comprehensive association features are summarized to form the comprehensive association feature set of the final level.

[0054] Step S1326: Summarize and organize the association features of the first level, intermediate level and last level to form the surface association information of the operating status of each electric actuator.

[0055] The first-level set of associated features, the intermediate-level set of derived associated relationships, and the final-level set of comprehensive associated features are summarized and organized, and duplicate features and relationships are removed to form surface-level associated information on the operating status of each electric actuator. This surface-level associated information includes direct associated features, derived associated relationships, and comprehensive associated features between each electric actuator, and can characterize the associated status of each electric actuator from multiple dimensions and levels.

[0056] Step S1327: Perform deduplication on the surface association information and retain representative core surface association features.

[0057] In the aggregated and organized surface-level association information, there may be some duplicate association features and relationships, which need to be deduplicated. For example, different levels or different attribute dimensions may have the same or similar association features. By comparing the similarity of these features, duplicate features are removed, and representative core surface-level association features are retained. For instance, in action type association features, there may be multiple similar action coordination association features. By analyzing their action type, action execution progress, spatial location association, and other attributes, the core surface-level association features that best represent the operational status association of each electric actuator are retained.

[0058] Step S133: Perform deep correlation deduction based on surface correlation information to trace the indirect correlation clues between different electric actuators during operation.

[0059] Step S1331: Starting from the core association features in the surface association information, construct an association deduction path framework.

[0060] In this embodiment, a path derivation framework is constructed starting with the core association features in the surface association information, such as the motion coordination association features between the electric support leg and the electric lifting arm (core surface association features). This path derivation framework includes association paths that may extend from the core association features to other electric actuators. For example, starting from the motion coordination association features between the electric support leg and the electric lifting arm, association paths with the electric rotary table and with other auxiliary electric actuators (such as power tool drive mechanisms) are extended. When constructing the path framework, the functional associations and operational process associations between the various electric actuators are considered to ensure the rationality and completeness of the path framework.

[0061] Step S1332: Based on the association mapping relationship recorded in the historical training data of the synchronous control model, supplement the association derivation path framework with potential association nodes.

[0062] During the pre-training process, the synchronous control model accumulated a large amount of historical training data, which recorded the correlation mapping relationships between various electric actuators. Taking the historical training data of a lifting platform for aerial work maintenance as an example, the data recorded the correlation mapping relationship between the position changes of the electric support leg and the movement adjustment of the electric lifting arm, and the correlation mapping relationship between the movement adjustment of the electric lifting arm and the movement changes of the electric rotary table, etc. Based on these correlation mapping relationships, potential correlation nodes are added to the correlation derivation path framework. For example, in the correlation derivation path framework starting from the movement coordination correlation characteristics of the electric support leg and the electric lifting arm, the electric tool drive mechanism is added as a potential correlation node according to the correlation mapping relationship in the historical training data, because in historical operations, the movement coordination of the electric support leg and the electric lifting arm affects the movement execution of the electric tool drive mechanism (such as the start and stop of the electric tool, speed adjustment, etc.).

[0063] Step S1333: Analyze the logical association chain between potential associated nodes and core associated features to determine the order of association transmission.

[0064] The logical connection chain between supplementary potential related nodes (such as the power tool drive mechanism) and the core related features (the motion coordination related features of the electric support leg and the electric lifting arm) is analyzed. For example, the electric support leg performs a support action to stabilize the lifting platform, then the electric lifting arm performs a lifting action to send maintenance personnel and equipment to the designated height. Next, the power tool drive mechanism starts the power tool to perform maintenance work based on the position and action execution of the electric lifting arm. Therefore, the sequence of connection transmission is: electric support leg → electric lifting arm → power tool drive mechanism. By analyzing the logical connection chain, the sequence of connection transmission between each potential related node and the core related feature is determined, so as to clearly trace indirect connection clues.

[0065] Step S1334: Trace the feature transmission process of each node in the logical association chain and extract the changing related features during the feature transmission process.

[0066] During the transmission of logical connections, the feature transfer process at each node is tracked. For example, in the connection chain of electric support leg → electric lifting arm → power tool drive mechanism, the support pressure and extension length of the electric support leg are transmitted to the electric lifting arm, affecting its lifting height, extension length, and swing angle. These features of the electric lifting arm are then transmitted to the power tool drive mechanism, affecting its starting timing, speed, and torque. In this feature transfer process, change-related features are extracted: how changes in the electric support leg's features lead to changes in the electric lifting arm's features, and how changes in the electric lifting arm's features lead to changes in the power tool drive mechanism's features, as well as the relationships between these changes. For example, an increase in the support pressure ΔP of the electric support leg leads to an increase in the lifting height Δh of the electric lifting arm, which in turn leads to an increase in the speed Δv of the power tool drive mechanism. These change-related features are then extracted.

[0067] Step S1335: Determine the association strength between potential related nodes and core related features based on the changing association features, and screen out indirect related nodes whose association strength meets the set requirements.

[0068] Based on the extracted change correlation features, the correlation strength between potential correlation nodes (such as the power tool drive mechanism) and core correlation features (the motion coordination correlation features between the electric support leg and the electric lifting arm) is defined. Correlation strength can be measured by factors such as the amplitude, frequency, and degree of influence of feature changes. For example, if the influence of changes in the support pressure of the electric support leg on the lifting height of the electric lifting arm is M1, and the influence of changes in the lifting height of the electric lifting arm on the rotational speed of the power tool drive mechanism is M2, then the correlation strength between the potential correlation node and the core correlation feature is M = M1 × M2 (this is just a simplified calculation method; more complex algorithms can be used in practical applications). A correlation strength threshold M0 is set, and indirect correlation nodes with a correlation strength M ≥ M0 are selected, i.e., indirect correlation nodes whose correlation strength meets the set requirements.

[0069] Step S1336: Extract the association paths between indirect related nodes and different electric actuators to form indirect association clues.

[0070] For the selected indirectly related nodes (such as power tool drive mechanisms), the association paths between them and different electric actuators (such as electric support legs, electric lifting arms, electric rotary tables, etc.) are extracted. For example, the association path between the power tool drive mechanism and the electric support leg is the supporting action of the electric support leg → the lifting action of the electric lifting arm → the starting action of the power tool drive mechanism; the association path between the power tool drive mechanism and the electric rotary table is the rotating action of the electric rotary table → the extending and retracting action of the electric lifting arm → the speed adjustment action of the power tool drive mechanism, etc. These association paths are extracted to form indirect association clues, which can reflect the relationship and transmission path between the indirectly related nodes and different electric actuators.

[0071] Step S1337: Classify and sort the indirect association clues according to the length of the association path and the strength of the association to form a structured set of indirect association clues.

[0072] The extracted indirect association clues are categorized and sorted according to the length and strength of the association path. Indirect association clues with shorter path lengths and higher association strengths are ranked first, while those with longer path lengths and lower association strengths are ranked last. For example, the direct association path between the power tool drive mechanism and the electric lifting arm (path length 1) has an association strength of M1; the association path between the power tool drive mechanism and the electric support leg (path length 2) has an association strength of M2; and the association path between the power tool drive mechanism and the electric rotary table (path length 2) has an association strength of M3. If M1 > M2 > M3, then the order is: association clues between the power tool drive mechanism and the electric lifting arm, association clues between the power tool drive mechanism and the electric support leg, and association clues between the power tool drive mechanism and the electric rotary table, forming a structured set of indirect association clues.

[0073] Step S134: Integrate and model the surface-level association information with the indirect association clues to form a multi-dimensional association network structure.

[0074] In this embodiment, surface-level correlation information (including core surface-level correlation features and correlation features at each level) is fused with indirect correlation clues (including indirect correlation nodes and correlation paths) for modeling. A graph neural network approach is used, taking the nodes and correlation features from the surface-level correlation information as the foundation, and incorporating the indirect correlation nodes and correlation paths from the indirect correlation clues into the basic model to construct a multi-dimensional correlation network structure. In this network structure, nodes include each electric actuator and indirect correlation nodes, and edges include correlation edges from the surface-level correlation information and correlation edges from the indirect correlation clues. The edge weights are set according to factors such as correlation strength and correlation frequency. Through fusion modeling, the resulting multi-dimensional correlation network structure can comprehensively represent the direct and indirect correlations between various electric actuators, as well as their correlation features and correlation paths.

[0075] Step S135: The feature aggregation process of the multi-dimensional association network structure is performed by the pattern extraction module of the synchronous control model to select association patterns with stability and consistency.

[0076] The pattern extraction module of the synchronous control model comprises multiple fully connected layers and pooling layers. After inputting the multi-dimensional relational network structure into the pattern extraction module, the fully connected layers first extract and transform the node and edge features of the network structure. Then, the pooling layers aggregate these features, using methods such as max pooling and average pooling to aggregate similar relational features and remove noise and redundant features. During the aggregation process, stable and consistent relational patterns are selected—patterns that maintain stable relationships under different operating scenarios and states. For example, after the electric support leg completes its supporting action, the electric lifting arm begins its lifting action; this relational pattern remains stable and consistent under different operating scenarios such as flat ground, sloping ground, and confined spaces. Similarly, after the electric lifting arm reaches a certain lifting height, the electric rotary table adjusts its rotation angle to a specific range; this relational pattern also exhibits stability and consistency.

[0077] Step S136: Generate implicit association rules for the operating status of each electric actuator based on the association pattern. The implicit association rules cover action coordination association rules and position adaptation association rules.

[0078] Based on the selected stable and consistent association patterns, implicit association rules for the operating states of each electric actuator are generated. For motion coordination association rules, the characteristics of each electric actuator in the association patterns, such as motion type, motion execution progress, and motion sequence, are analyzed to summarize their coordination relationships. Examples include the time interval between the completion of the electric support leg's support action and the initiation of the electric lifting arm's lifting action, and the coordination relationship between the lifting speed of the electric lifting arm and the rotation speed of the electric rotary table. For position adaptation association rules, the spatial positional association characteristics of each electric actuator in the association patterns are analyzed to summarize their positional adaptation relationships. Examples include the adaptation relationship between the extension length of the electric support leg and the lifting height of the electric lifting arm, and the adaptation relationship between the rotation angle of the electric rotary table and the extension length of the electric lifting arm. These rules are organized and summarized to form implicit association rules encompassing both motion coordination and position adaptation association rules. These rules reflect the inherent association relationships of the operating states of each electric actuator.

[0079] Step S140: Generate synchronous drive adjustment commands corresponding to each electric actuator according to the implicit correlation rules, and form an adaptation correlation between the synchronous drive adjustment commands and the operating action information of each electric actuator.

[0080] Step S141: Analyze the action coordination correlation rules in the implicit correlation rules, and extract the action coordination requirements of each electric actuator in different operation stages.

[0081] In this embodiment, the action coordination association law in the implicit association law is analyzed. Taking the operation stage of the lifting platform for high-altitude operation and maintenance as an example, the operation process is divided into preparation stage, ascent stage, operation stage, descent stage and recovery stage. During the preparation phase, the motion coordination law requires the electric support legs to perform a supporting action first. Only after the support is stable (as determined by pressure sensors and a preset time), can the electric lifting arm begin its lifting action, while the electric rotary table remains stationary. During the ascent phase, the motion coordination law requires the electric lifting arm to rise and fall at a certain speed. The electric rotary table rotates at a corresponding speed based on the lifting height of the electric lifting arm and the requirements of the working position. The electric support legs adjust their supporting pressure and extension length according to the lifting height of the electric lifting arm and changes in the platform's center of gravity. During the operation phase, the motion coordination law requires the electric lifting arm to maintain a stable height and position. The electric rotary table performs small-range rotations and adjustments according to the maintenance requirements, while the electric support legs maintain stable support. During the descent phase, the motion coordination law requires the electric lifting arm to descend at a certain speed, the electric rotary table to return to its initial position, and the electric support legs adjust their supporting pressure and extension length according to the descent height of the electric lifting arm and changes in the platform's center of gravity. During the retraction phase, the motion coordination law requires the electric lifting arm to return to its initial position, the electric support legs to retract, and the electric rotary table to remain stationary. Extract the action coordination requirements of these different operation stages as the basis for generating synchronous drive adjustment instructions.

[0082] Step S142: Analyze the position adaptation association law in the implicit association law and extract the position correspondence of each electric actuator in the action coordination process.

[0083] This paper analyzes the positional adaptation relationship in the implicit correlation pattern, taking the action coordination process of a lifting platform for high-altitude operation and maintenance as an example. In the preparation phase, the support position of the electric support leg needs to meet the stability requirements of the platform, maintaining a certain horizontal distance and vertical height difference with the initial position of the electric lifting arm. For example, the extension length of the electric support leg reaches L1, the horizontal distance with the electric lifting arm reaches D1, and the vertical height difference is H1. In the lifting phase, when the lifting height of the electric lifting arm is h, the extension length of the electric support leg needs to be adjusted to L2, the horizontal distance with the electric lifting arm adjusted to D2, the vertical height difference adjusted to H2, and the rotation angle of the electric rotary table adjusted to θ1. In the operation phase, the lifting height of the electric lifting arm... The descent height is maintained at h0, the extension length at l0, the extension length of the electric support leg at L3, the horizontal distance between the support leg and the electric lifting arm at D3, the vertical height difference at H3, and the rotation angle of the electric rotary table at θ0. During the descent phase, when the electric lifting arm descends to a height of h1, the extension length of the electric support leg is adjusted to L4, the horizontal distance between the support leg and the lifting arm is adjusted to D4, the vertical height difference is adjusted to H4, and the rotation angle of the electric rotary table is adjusted to θ2. During the retraction phase, the electric lifting arm returns to its initial position, the extension length of the electric support leg returns to L1, the horizontal distance between the support leg and the lifting arm returns to D1, the vertical height difference returns to H1, and the rotation angle of the electric rotary table returns to its initial angle. The positional correspondences during these different coordinated actions are extracted as the basis for generating synchronous drive adjustment commands.

[0084] Step S143: Compare and correlate the action coordination requirements with the operation action information of each electric actuator to find the adaptation differences between the current operation action and the coordination requirements.

[0085] Compare and associate the extracted action coordination requirements with the current operating action information of each electric actuator. For example, during the rising stage, the lifting height of the current electric lifting arm is h_current, the lifting speed is v_current, the rotation angle of the electric rotating table is θ_current, the rotation speed is ω_current, the telescopic length of the electric support leg is L_current, and the support pressure is P_current. Compare with the lifting height h, lifting speed v, rotation angle θ, rotation speed ω, telescopic length L, and support pressure P in the action coordination requirements to find the adaptation differences. If h_current < h, it means that the lifting height of the electric lifting arm is insufficient, and there is a difference in the action amplitude; if v_current > v, it means that the lifting speed of the electric lifting arm is too fast, and there is a difference in the action speed; if θ_current is not within the range of θ1, it means that the rotation angle of the electric rotating table does not meet the requirements, and there is a difference in the action angle; if L_current ≠ L2, it means that the telescopic length of the electric support leg does not meet the requirements, and there is a difference in the action amplitude; if P_current is not within the preset pressure range, it means that the support pressure of the electric support leg does not meet the requirements, and there is a difference in the action pressure, etc. Classify these adaptation differences, such as action timing differences (if the start time of the lifting action of the electric lifting arm is earlier or later than the time required by the action coordination), action amplitude differences (such as differences in lifting height, telescopic length, rotation angle, etc.), action type differences (if the current action type does not match the action type required by the action coordination, although this situation rarely occurs during normal operations and mainly occurs in abnormal situations), etc.

[0086] Step S144: Perform a matching analysis based on the position correspondence relationship and the inter-institution relative position relationship information of each electric actuator to determine the position adaptation deviation.

[0087] The extracted position correspondence is matched and analyzed with the current relative positional information of each electric actuator. For example, during the lifting phase, the current horizontal distance between the electric support leg and the electric lifting arm is D_current, the vertical height difference is H_current, the relative angle between the electric rotary table and the electric lifting arm is θr_current, and the relative distance is dr_current. The horizontal distance D2, vertical height difference H2, relative angle θ1, and relative distance dr1 in the position correspondence are matched to identify positional adaptation deviations. If D_current > D2, it indicates that the horizontal distance between the electric support leg and the electric lifting arm is too large, indicating a spatial positional deviation; if H_current > D2, it indicates that the horizontal distance between the electric support leg and the electric lifting arm is too large, indicating a spatial positional deviation; if H_current > D2, it indicates that the horizontal distance between the electric support leg and the electric lifting arm is too large, indicating a spatial positional deviation. dr1 indicates that the relative distance between the electric rotary table and the electric lifting arm is too large, resulting in a relative distance deviation. These positional adaptation deviations can be categorized as follows: spatial positional deviations (such as deviations in horizontal distance, vertical height difference, and relative angle), relative distance deviations (such as distance deviations between different electric actuators), and position timing deviations (if the time of position change of each electric actuator does not match the time requirements of the position correspondence).

[0088] Step S145: Based on the adaptation differences and positional adaptation deviations, construct synchronous drive adjustment logic and determine the adjustment direction and adjustment correlation.

[0089] Step S1451: Classify the adaptation differences into differences in action timing, action amplitude, and action type.

[0090] The identified adaptation discrepancies are categorized into action timing discrepancies, action amplitude discrepancies, and action type discrepancies. For example, if the lifting action of the electric lifting boom starts earlier than the required action coordination time, it falls under the action timing discrepancy; insufficient lifting height of the electric lifting boom or insufficient extension length of the electric support legs falls under the action amplitude discrepancy; if the current action type of the electric actuator is rotation, while the required action type is lifting, this falls under the action type discrepancy. However, this generally does not occur in normal operation and may mainly occur during malfunctions or misoperations, requiring special handling.

[0091] Step S1452: Classify and analyze the position adaptation deviations to distinguish spatial position deviations, relative distance deviations, and position timing deviations.

[0092] The identified positional adaptation deviations are categorized and analyzed, and classified into spatial positional deviations, relative distance deviations, and positional timing deviations. For example, excessive horizontal distance between the electric support leg and the electric lifting arm, insufficient vertical height difference, and incorrect relative angle between the electric rotary table and the electric lifting arm are considered spatial positional deviations; excessive relative distance between the electric rotary table and the electric lifting arm is considered a relative distance deviation; and discrepancies between the positional change time of each electric actuator and the time requirements of the positional correspondence are considered positional timing deviations.

[0093] Step S1453: For different types of adaptation differences, determine the corresponding adjustment direction based on the motion coordination correlation law, and determine the core direction of motion adjustment.

[0094] For different types of adaptation differences, the corresponding adjustment direction is determined based on the motion coordination correlation rules. For differences in motion timing, if the starting time of the electric lifting arm's lifting motion is earlier than the required time for motion coordination, its starting time needs to be delayed, and the adjustment direction is "delay"; if the starting time is later than the required time, its starting time needs to be advanced, and the adjustment direction is "advance". For differences in motion amplitude, if the lifting height of the electric lifting arm is insufficient, the lifting height needs to be increased, and the adjustment direction is "up"; if the lifting height is too high, the lifting height needs to be decreased, and the adjustment direction is "down". If the extension length of the electric support leg is insufficient, the extension length needs to be increased, and the adjustment direction is "extension"; if the extension length is too long, the extension length needs to be reduced, and the adjustment direction is "shorten". If the rotation angle of the electric turntable does not meet the requirements, the rotation angle needs to be adjusted to reach the required range, and the adjustment direction is "rotation" (clockwise or counterclockwise). For differences in motion speed, if the lifting speed of the electric lifting arm is too fast, the speed needs to be reduced, and the adjustment direction is "deceleration"; if the speed is too slow, the speed needs to be increased, and the adjustment direction is "acceleration". After determining these adjustment directions, the core focus of the motion adjustment is clarified, namely, adjusting the specific motion parameters of each electric actuator to meet the requirements of the motion coordination and correlation law.

[0095] Step S1454: For different types of position adaptation deviations, set the corresponding calibration direction according to the position adaptation correlation law and determine the key dimensions of position calibration.

[0096] For different types of positional adaptation deviations, corresponding calibration directions are set according to the positional adaptation correlation rules. For spatial positional deviations, if the horizontal distance between the electric support leg and the electric lifting arm is too large, the position of the electric support leg needs to be adjusted to reduce the horizontal distance; the calibration direction is to move closer to the electric lifting arm. If the vertical height difference is insufficient, the lifting height of the electric lifting arm or the extension length of the electric support leg needs to be adjusted to meet the requirements; the calibration direction is to adjust the height. For relative distance deviations, if the relative distance between the electric rotary table and the electric lifting arm is too large, the position of the electric rotary table or the electric lifting arm needs to be adjusted to reduce the relative distance; the calibration direction is to move closer. For positional timing deviations, if the position adjustment time of the electric support leg is earlier than the required time, its position adjustment time needs to be delayed; the calibration direction is to delay. If it is later than the required time, its position adjustment time needs to be advanced; the calibration direction is to advance. After determining these calibration directions, the key dimensions of positional calibration are clarified, namely, calibration is performed on the specific position parameters (such as coordinate position, relative distance, relative angle, etc.) of each electric actuator to meet the requirements of the positional adaptation correlation rules.

[0097] Step S1455: Analyze the interaction between adaptation differences and positional adaptation deviations to determine the priority order of adjustments.

[0098] This analysis examines the interrelationships between adaptation differences and positional adaptation deviations. For instance, insufficient extension length of the electric support leg (difference in amplitude of movement) leads to an excessive horizontal distance between it and the electric lifting arm (spatial position deviation). This excessive horizontal distance, in turn, affects the lifting stability of the electric lifting arm, resulting in variations in its lifting speed (difference in movement speed). Therefore, the difference in the amplitude of movement of the electric support leg is the cause of both spatial position deviation and the difference in the movement speed of the electric lifting arm, requiring priority adjustment of the extension length of the electric support leg. Similarly, a deviation in the rotation angle of the electric rotary table (spatial position deviation) results in an unsuitable relative angle between it and the electric lifting arm, affecting the working effect of the power tool. Adjusting the rotation angle of the electric rotary table, in turn, affects the adjustment of the extension length and lifting height of the electric lifting arm (difference in amplitude of movement). By analyzing these interrelationships, a priority order for adjustment is determined: first adjust the electric actuators that have a greater impact on other adaptation differences and positional adaptation deviations, then adjust the affected electric actuators, thereby improving the efficiency and effectiveness of the adjustment.

[0099] Step S1456: Based on the adjustment direction, calibration direction, and adjustment priority order, construct a multi-dimensional synchronous drive adjustment logic framework.

[0100] Based on the defined adjustment direction, calibration direction, and adjustment priority order, a multi-dimensional synchronous drive adjustment logic framework is constructed. This multi-dimensional synchronous drive adjustment logic framework includes multiple dimensions, such as the action adjustment dimension (including the adjustment direction of action timing, action amplitude, action speed, etc.), the position calibration dimension (including the calibration direction of spatial position, relative distance, position timing, etc.), and the adjustment priority dimension (including the adjustment order of each electric actuator). Within the framework, the adjustment direction, calibration direction, and adjustment priority of each electric actuator are clearly defined. For example, the adjustment priority of the electric support leg is 1, the adjustment direction is extension (for cases of insufficient telescopic length), and the calibration direction is moving closer to the electric lifting arm (for cases of excessive horizontal distance); the adjustment priority of the electric lifting arm is 2, the adjustment direction is rising (for cases of insufficient lifting height), and the calibration direction is adjusting height (for cases of insufficient vertical height difference); the adjustment priority of the electric rotary table is 3, the adjustment direction is rotation (for cases of rotation angle deviation), and the calibration direction is moving closer to the electric lifting arm (for cases of excessive relative distance), etc.

[0101] Step S1457: Determine the regulation relationship between each electric actuator in the regulation logic framework, and determine the correspondence between the main regulation mechanism and the cooperative regulation mechanism.

[0102] Within the regulation logic framework, the regulation relationships between each electric actuator are determined. For example, the electric support leg, acting as the primary regulation mechanism, affects the movement of the electric lifting arm and the electric rotary table when its extension length is adjusted. Therefore, the electric lifting arm and the electric rotary table, as co-regulating mechanisms, need to coordinate their adjustments based on the adjustment status of the electric support leg. Similarly, the electric lifting arm, acting as the primary regulation mechanism, affects the rotation angle and position adjustment of the electric rotary table when its lifting height and extension length are adjusted. Therefore, the electric rotary table, as a co-regulating mechanism, needs to coordinate its adjustments based on the adjustment status of the electric lifting arm. The correspondence between primary and co-regulating mechanisms is determined; for example, when the electric support leg is the primary regulation mechanism, the electric lifting arm and the electric rotary table are co-regulating mechanisms; when the electric lifting arm is the primary regulation mechanism, the electric rotary table is a co-regulating mechanism, and so on. This clarifies the regulation roles and relationships of each electric actuator when generating synchronous drive regulation commands.

[0103] Step S1458: Refine the adjustment trigger conditions in the adjustment logic framework to achieve precise matching between the generation and execution timing of adjustment instructions.

[0104] The adjustment trigger conditions in the adjustment logic framework are refined. For example, when the extension length of the electric support leg reaches L2, the lifting action of the electric lifting arm is triggered; when the lifting height of the electric lifting arm reaches h, the rotation action of the electric rotary table is triggered; when the rotation angle of the electric rotary table reaches θ1, the rotation action stops, and so on. By refining these adjustment trigger conditions, the generation and execution timing of adjustment commands are precisely matched, ensuring that the adjustment actions of each electric actuator can be executed in an orderly manner according to operational needs and coordination requirements, avoiding action conflicts and incoordination.

[0105] Step S146: Generate synchronous drive adjustment instructions for each electric actuator according to the synchronous drive adjustment logic. The synchronous drive adjustment instructions include motion adjustment information and position calibration information.

[0106] Based on the constructed synchronous drive adjustment logic framework, synchronous drive adjustment commands are generated for each electric actuator. For the electric support leg, the adjustment command includes motion adjustment information (such as adjusting the telescopic length to L2, adjusting the support pressure to the preset range, delaying or advancing the action start time, etc.) and position calibration information (such as adjusting the horizontal distance to D2, adjusting the vertical height difference to H2, etc.); for the electric lifting arm, the adjustment command includes motion adjustment information (such as adjusting the lifting height to h, adjusting the lifting speed to v, delaying or advancing the action start time, etc.) and position calibration information (such as adjusting the vertical height difference to H2, adjusting the telescopic length to l0, etc.); for the electric rotary table, the adjustment command includes motion adjustment information (such as adjusting the rotation angle to θ1, adjusting the rotation speed to ω, delaying or advancing the action start time, etc.) and position calibration information (such as adjusting the relative angle to θ1, adjusting the relative distance to dr1, etc.). These motion adjustment and position calibration information are integrated into the synchronous drive adjustment command to ensure that the command can accurately guide each electric actuator to perform adjustment actions.

[0107] Step S147: Bind and associate the synchronous drive adjustment command with the corresponding electric actuator's operating action information to achieve accurate matching between the synchronous drive adjustment command and the corresponding electric actuator's operating action information.

[0108] The generated synchronous drive adjustment commands are bound and associated with the corresponding operating motion information of the electric actuators. For example, the synchronous drive adjustment commands for the electric support leg are bound to the current operating motion information of the electric support leg (such as extension length, support pressure, position information, etc.) to ensure that the adjustment commands can be adjusted according to the specific operating state of the electric support leg; the synchronous drive adjustment commands for the electric lifting arm are bound to the current operating motion information of the electric lifting arm (such as lifting height, lifting speed, position information, etc.); and the synchronous drive adjustment commands for the electric rotary table are bound to the current operating motion information of the electric rotary table (such as rotation angle, rotation speed, position information, etc.). Through this binding association, the synchronous drive adjustment commands are precisely matched with the operating motion information of the corresponding electric actuators, ensuring that the adjustment commands are executed correctly and achieving the purpose of coordinated adjustment.

[0109] Step S150: Control each electric actuator to operate in coordination based on the synchronous drive adjustment command, and collect the operation feedback status information of each electric actuator to form a linkage control.

[0110] Step S151: Sort the synchronous drive adjustment commands corresponding to each electric actuator according to the working time sequence to generate the command execution sequence.

[0111] In this embodiment, the synchronous drive adjustment commands corresponding to each electric actuator are sorted according to the work sequence and adjustment priority. For example, the adjustment command for the electric support leg needs to be executed first because it is the main adjustment mechanism, and its adjustment action will affect the adjustment actions of other electric actuators. Therefore, the adjustment command for the electric support leg is ranked first; then the adjustment command for the electric lifting arm is ranked second; and finally the adjustment command for the electric rotary table is ranked third. If there are other adjustment commands for electric actuators, they are also sorted according to the adjustment priority to generate an instruction execution sequence, ensuring that the adjustment actions of each electric actuator can be executed in an orderly manner according to the work sequence and coordination requirements.

[0112] Step S152: According to the instruction execution sequence, send synchronous drive adjustment instructions to each electric actuator one by one to trigger the adjustment action of each electric actuator.

[0113] Following the generated instruction execution sequence, synchronous drive adjustment commands are sent sequentially to each electric actuator via the control system's communication module. For example, an adjustment command is first sent to the electric support leg, controlling its telescopic motor and pressure adjustment device to adjust the telescopic length and support pressure. After the electric support leg's adjustment is completed or reaches a certain progress, an adjustment command is sent to the electric lifting arm, controlling its lifting motor and telescopic motor to adjust the lifting height and telescopic length. Then, an adjustment command is sent to the electric rotary table, controlling its rotary motor to adjust the rotation angle and rotation speed. In this way, the adjustment actions of each electric actuator are triggered, achieving coordinated operation.

[0114] Step S153: During the adjustment process of each electric actuator, real-time operating parameters are continuously collected to form a dynamic operating record.

[0115] During the adjustment process performed by each electric actuator, sensors installed on each actuator continuously collect real-time operating parameters. For the electric support leg, real-time operating parameters such as extension length, support pressure, and position information are collected; for the electric lifting arm, real-time operating parameters such as lifting height, lifting speed, extension length, and position information are collected; for the electric rotary table, real-time operating parameters such as rotation angle, rotation speed, and position information are collected. These real-time operating parameters are recorded in real time to form a dynamic operating record, which can be analyzed and evaluated after the adjustment is completed.

[0116] Step S154: After each electric actuator completes the adjustment action, the operation feedback status information of each electric actuator is collected. The operation feedback status information includes the adjusted operation action information and the adjusted relative position relationship information between the actuators.

[0117] After each electric actuator completes its adjustment, its operational feedback status information is collected. For the electric support leg, operational information such as the adjusted telescopic length, support pressure, and position information are collected, as well as its relative positional relationship with the electric lifting arm and electric rotary table (such as horizontal distance, vertical height difference, and relative angle). For the electric lifting arm, operational information such as the adjusted lifting height, lifting speed, telescopic length, and position information are collected, as well as its relative positional relationship with the electric support leg and electric rotary table. For the electric rotary table, operational information such as the adjusted rotation angle, rotation speed, and position information are collected, as well as its relative positional relationship with the electric support leg and electric lifting arm. This operational feedback status information is then summarized to form the operational feedback status information for each electric actuator.

[0118] Step S155: Compare and correlate the running feedback status information with the expected effect information of the synchronous drive adjustment command to analyze the adjustment execution effect.

[0119] The collected operational feedback status information is compared and correlated with the expected effects of the synchronous drive adjustment commands. For example, the expected effects of the synchronous drive adjustment commands on the electric support leg are: extension length reaching L2, support pressure reaching P2, horizontal distance to the electric lifting arm reaching D2, and vertical height difference reaching H2; on the electric lifting arm, the expected effects are: lifting height reaching h, lifting speed reaching v, horizontal distance to the electric support leg reaching D2, vertical height difference reaching H2, relative angle to the electric rotary table reaching θ1, and relative distance reaching dr1; on the electric rotary table, the expected effects are: rotation angle reaching θ1, rotation speed reaching ω, relative angle to the electric support leg reaching θ2, and relative distance reaching dr2, and relative angle to the electric lifting arm reaching θ1, and relative distance reaching dr1. The actual parameters in the operational feedback status information are compared with these expected effect information to analyze the adjustment execution effect. If the extension length of the electric support leg reaches L2, the support pressure reaches P2, the horizontal distance to the electric lifting arm reaches D2, and the vertical height difference reaches H2, it indicates that the adjustment execution effect of the electric support leg is good. If the lifting height of the electric lifting arm reaches h, the lifting speed reaches v, the horizontal distance to the electric support leg reaches D2, the vertical height difference reaches H2, the relative angle with the electric rotary table reaches θ1, and the relative distance reaches dr1, it indicates that the adjustment execution effect of the electric lifting arm is good. If the rotation angle of the electric rotary table reaches θ1, the rotation speed reaches ω, the relative angle with the electric support leg reaches θ2, and the relative distance reaches dr2, and the relative angle with the electric lifting arm reaches θ1, and the relative distance reaches dr1, it indicates that the adjustment execution effect of the electric rotary table is good. If there is a discrepancy between the actual parameters and the expected results, analyze which electric actuator's adjustment action is malfunctioning and what the cause of the problem is, such as sensor failure, motor failure, or control algorithm issues.

[0120] Step S156: Determine whether to perform secondary adjustment based on the adjustment execution effect. If secondary adjustment is required, regenerate the synchronous drive adjustment instruction based on the running feedback status information.

[0121] Whether secondary adjustment is needed is determined based on the adjustment execution effect. If the adjustment execution effect is good, and the actual parameters of each electric actuator match the expected effect information, it indicates that the adjustment action has met the requirements and secondary adjustment is not necessary. If the adjustment execution effect is poor, and there are discrepancies between the actual parameters and the expected effect information, it indicates that secondary adjustment is required. If secondary adjustment is required, the adaptation differences and position adaptation deviations are re-analyzed based on the operational feedback status information, the synchronous drive adjustment logic is reconstructed, and new synchronous drive adjustment commands are generated to readjust each electric actuator until the adjustment execution effect meets the requirements.

[0122] Step S157: Feed back the operation feedback status information to the update module of the multi-dimensional operation status map, and update the multi-dimensional operation status map in real time.

[0123] For example, in step S1571: extract the adjusted running action information from the running feedback status information, and update the action type information and action execution progress information of the corresponding node in the multi-dimensional running status map.

[0124] The adjusted operational action information is extracted from the operational feedback status information. Examples include: the extension / retraction length, support pressure, action type (extension / retraction), and action execution progress of the electric support leg (extension / retraction length reaches L2, completion rate is L2 / L_max); the lifting height, lifting speed, action type (lifting / retraction), and action execution progress of the electric lifting arm (lifting height reaches h, completion rate is h / H_max); and the rotation angle, rotation speed, action type (rotation), and action execution progress of the electric rotary table (rotation angle reaches θ1, completion rate is θ1 / θ_max). This information is then fed back to the update module of the multi-dimensional operational status graph, updating the action type and action execution progress information of the corresponding nodes, ensuring the graph reflects the latest operational status of each electric actuator.

[0125] Step S1572: Extract the adjusted relative positional relationship information between mechanisms from the operation feedback status information, and update the spatial position association information and action timing association information of the corresponding nodes in the multi-dimensional operation status map.

[0126] Extract the adjusted relative positional relationships between mechanisms from the operational feedback status information, such as the horizontal distance D2 and vertical height difference H2 between the electric support leg and the electric lifting arm, the relative angle θ1 and relative distance dr1 between the electric rotary table and the electric lifting arm, and the relative angle θ2 and relative distance dr2 between the electric support leg and the electric rotary table. Feed this information back to the update module of the multi-dimensional operational status map, updating the spatial position association information and action timing association information of the corresponding nodes, so that the map can reflect the latest relative positional relationships and action timing associations between each electric actuator.

[0127] Step S1573: Based on the updated node attributes, recalculate the association weights between nodes to make the association weights match the adjusted running state.

[0128] Based on the updated node attributes (action type information, action execution progress information, spatial location association information, action timing association information, etc.), the association weights between nodes are recalculated. For example, based on the new action coordination (adjusted action execution status) and position dependence (adjusted relative positional relationship) between the electric support leg and the electric lifting arm, the frequency of action coordination is recalculated, the positional dependence is analyzed, the association coefficient is recalculated, and then the association weights between nodes are reset to adapt the association weights to the adjusted operating state.

[0129] Step S1574: Analyze the completeness of the associated paths in the updated multi-dimensional operational status map and supplement the new associated paths generated by changes in operational status.

[0130] Analyze the completeness of the correlation paths in the updated multi-dimensional operating status map. For example, in the adjusted operating state, new correlation paths may arise between the electric actuators. For instance, the adjustment of the electric support leg might lead to the adjustment of the electric lifting arm, which in turn leads to the adjustment of the electric rotary table. These new correlation paths need to be added to the multi-dimensional operating status map. By analyzing the correlation paths in the map, we can identify the new correlation paths generated by changes in operating state and add them to the map, enabling it to more comprehensively reflect the relationships between the electric actuators.

[0131] Step S1575: Remove redundant association paths that are no longer applicable in the updated multi-dimensional operational status graph and optimize the graph structure.

[0132] Analyze the updated multi-dimensional operational status map to identify redundant and no longer applicable paths. For example, in the adjusted operational status, the association weight of some paths is reduced, the frequency of action coordination decreases, and they can no longer accurately reflect the relationships between the various electric actuators. These paths are redundant and need to be eliminated. By eliminating these redundant paths, the map structure is optimized, making the map simpler and clearer.

[0133] Step S1576: Record the differences in changes before and after the map update, and analyze the correlation between the differences in changes and the synchronous drive adjustment command.

[0134] Record the differences in the graph before and after the update, such as changes in node action type information, action execution progress information, spatial location association information, action timing association information, changes in the association weights between nodes, and changes in association paths. Analyze the relationship between these differences and synchronous drive adjustment commands. For example, the adjustment of the electric support leg by the synchronous drive adjustment command leads to changes in its action type information and action execution progress information, which in turn leads to changes in the spatial location association information and action timing association information with the electric lifting arm, as well as changes in the association weights between nodes and changes in association paths.

[0135] Step S1577: Store the updated multi-dimensional operational status map to the specified data module.

[0136] The updated multi-dimensional operating status map is stored in a designated data module (such as a database or memory cache) so that when performing the next round of synchronous control, the latest multi-dimensional operating status map can be called to perform operations such as real-time operating status information collection, map construction, linkage relationship modeling, synchronous drive adjustment command generation and linkage control, ensuring that the synchronous control method can continuously adapt to the changes in the operating status of each electric actuator and achieve precise synchronous control.

[0137] Furthermore, the training process for the pre-trained synchronization control model is as follows (steps S210-S270):

[0138] Figure 2 Schematic diagrams of exemplary hardware and software components of a synchronous control system 100 for electrifying a maintenance lifting system, which can implement the ideas of this application, are shown in some embodiments of this application. For example, a processor 120 may be used in the synchronous control system 100 for electrifying a maintenance lifting system and for performing the functions described in this application.

[0139] The synchronous control system 100 for electrifying the maintenance lifting system can be a general-purpose server or a special-purpose server; both can be used to implement the synchronous control method for electrifying the maintenance lifting system of this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the load.

[0140] For example, the synchronous control system 100 for electrifying the maintenance lift system may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the synchronous control system 100 for electrifying the maintenance lift system may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of this application can be implemented according to these program instructions. The synchronous control system 100 for electrifying the maintenance lift system also includes an I / O interface 150 between a computer and other input / output devices.

[0141] For ease of explanation, only one processor is described in the synchronous control system 100 for electrifying the maintenance lift system. However, it should be noted that the synchronous control system 100 for electrifying the maintenance lift system in this application may also include multiple processors. Therefore, the steps performed by one processor as described in this application may also be performed jointly or individually by multiple processors. For example, if the processor of the synchronous control system 100 for electrifying the maintenance lift system performs steps A and B, it should be understood that steps A and B may also be performed jointly by two different processors or individually by one processor. For example, the first processor performs step A, the second processor performs step B, or the first processor and the second processor jointly perform steps A and B.

[0142] Furthermore, this embodiment of the invention also provides a readable storage medium, wherein computer-executable instructions are preset in the readable storage medium, and when the processor executes the computer-executable instructions, the above-mentioned synchronous control method for electrification of the maintenance lifting system is implemented.

[0143] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for synchronously controlling the electric drive of a maintenance hoist system, characterized in that The method includes: Collect real-time operating status information of each electric actuator in the maintenance lifting system. The real-time operating status information includes the operating action information of each electric actuator and the relative positional relationship information between the actuators. Based on the real-time operating status information, a multi-dimensional operating status map is constructed. This multi-dimensional operating status map is used to characterize the operational correlation features and cooperative action dependencies of each electric actuator, specifically including: Extract the operation action information of each electric actuator from the real-time operation status information, and divide it into action type information and action execution progress information; Extract the relative positional relationship information between mechanisms from the real-time operating status information, and extract the spatial positional association information and action timing association information between each electric actuator; The action type information, action execution progress information, spatial location association information, and action temporal sequence association information are used as graph node attributes to construct an initial state graph framework. The association weights between nodes are set based on the operational association characteristics of each electric actuator. The operational association characteristics are determined by the action coordination and position dependence. Based on the node attributes and the association weights between nodes, a multi-dimensional operational status graph containing node hierarchical relationships and associated path information is generated using a graph structure modeling approach. The multi-dimensional operational status graph is processed by graph optimization rules to remove redundant paths, while retaining the core associated paths that are directly related to collaborative actions. The pre-trained synchronous control model is invoked to model the linkage relationships in the multi-dimensional operating state map, and the implicit correlation patterns of the operating states of each electric actuator are uncovered, specifically including: The multi-dimensional operational status map is input into the map embedding layer of the synchronization control model, and joint extraction processing of node features and associated path features is performed to generate a map embedding feature vector. The association mining module of the synchronous control model performs hierarchical association analysis on the embedded feature vector of the map, and analyzes the surface association information of the operating status of each electric actuator layer by layer. Deep correlation deduction is performed based on surface correlation information to trace indirect correlation clues between different electric actuators during operation; By fusing surface-level correlation information with indirect correlation clues into a model, a multi-dimensional correlation network structure is formed. The feature aggregation process of the multi-dimensional association network structure is performed by the pattern extraction module of the synchronous control model to filter out association patterns with stability and consistency. Based on the aforementioned association pattern, implicit association rules for the operating status of each electric actuator are generated, including action coordination association rules and position adaptation association rules. Based on the implicit correlation rules, synchronous drive adjustment commands are generated for each electric actuator, and the synchronous drive adjustment commands are adapted and correlated with the operating action information of each electric actuator. Based on the synchronous drive adjustment command, the electric actuators are controlled to operate in coordination, and the operating feedback status information of each electric actuator is collected to form a linkage control.

2. The method of claim 1, wherein the method further comprises: The method of setting inter-node association weights based on the operational association characteristics of each electric actuator, wherein the operational association characteristics are determined through action coordination and position dependence, including: The system analyzes the coordination of actions of each electric actuator during historical operation and records the frequency of related actions performed simultaneously by different electric actuators. Analyze the position dependence of each electric actuator when completing the lifting operation, and record the correlation between the position change of one electric actuator and the action execution of other electric actuators; The action coordination and position dependence are correlated and mapped to form a runtime correlation feature description result. The initial benchmark for the association weights between nodes is set based on the description results of the operational association features. The degree of association between collaboration and dependence in the description results of operational association features corresponds to the setting of the initial benchmark. Based on the operational requirements of the lifting system, the initial benchmark is dynamically adapted and adjusted to ensure that the correlation weights between nodes accurately correspond to the collaborative requirements in actual operations. Record the relationship between the adjusted node association weights and the adaptation to the work scenario.

3. The method of claim 1, wherein, The hierarchical correlation analysis of the embedded feature vectors in the graph, performed by the correlation mining module of the synchronous control model, analyzes the surface correlation information of the operating status of each electric actuator layer by layer, including: The embedded feature vectors of the graph are grouped according to the node attribute categories to form feature subsets with different attribute dimensions; Each feature subset is hierarchically divided, and hierarchical division criteria are set; In the first level, the motion correlation features and position correlation features that directly correspond between different electric actuators are extracted; In the intermediate level, we analyze the derived relationships between basic related features and explore the collaborative adaptation features of different actuators under the same action type. At the lowest level, basic association features and derived association relationships are integrated to form comprehensive association features covering multiple dimensions of attributes; The association features of the first level, the middle level and the last level are summarized and organized to form the surface association information of the operating status of each electric actuator; The surface-level association information is deduplicated, retaining the representative core surface-level association features.

4. The synchronous control method for electrification of the maintenance lifting system according to claim 1, characterized in that, The deep correlation derivation based on surface correlation information, tracing indirect correlation clues between different electric actuators during operation, includes: Starting with the core association features in the surface association information, a framework for association deduction path is constructed; Based on the association mapping relationships recorded in the historical training data of the synchronous control model, potential association nodes are added to the association derivation path framework. Analyze the logical connection chain between potential related nodes and core related features to determine the order of connection transmission; Tracing the feature transmission process of each node in the logical association chain, and extracting the changing related features during the feature transmission process; Based on the changing association features, the association strength between potential association nodes and core association features is defined, and indirect association nodes whose association strength meets the set requirements are selected. Extract the association paths between indirectly related nodes and different electric actuators to form indirect association clues; Indirect connection clues are classified and sorted according to the length of the connection path and the strength of the connection to form a structured set of indirect connection clues.

5. The synchronous control method for electrification of the maintenance lifting system according to claim 1, characterized in that, The step of generating synchronous drive adjustment commands for each electric actuator based on the implicit correlation rule, wherein the synchronous drive adjustment commands are adapted and correlated with the operating action information of each electric actuator, includes: The motion coordination correlation rules in the implicit correlation rules are analyzed to extract the motion coordination requirements of each electric actuator in different operation stages; The positional adaptation correlation rules in the implicit correlation rules are analyzed, and the positional correspondence of each electric actuator in the action coordination process is extracted; The required coordination of actions is compared and correlated with the operational action information of each electric actuator to identify the compatibility differences between the current operational action and the coordination requirements. The positional adaptation deviation is determined by matching and analyzing the positional correspondence with the relative positional relationship information between the various electric actuators. Based on the aforementioned adaptation differences and positional adaptation deviations, synchronous drive adjustment logic is constructed to determine the adjustment direction and adjustment correlation. According to the synchronous drive adjustment logic, synchronous drive adjustment instructions are generated for each electric actuator. The synchronous drive adjustment instructions include motion adjustment information and position calibration information. The synchronous drive adjustment command is bound and associated with the corresponding electric actuator's operating action information to achieve precise matching between the synchronous drive adjustment command and the corresponding electric actuator's operating action information.

6. The synchronous control method for electrification of the maintenance lifting system according to claim 5, characterized in that, Based on the adaptation differences and positional adaptation deviations, a synchronous drive adjustment logic is constructed to determine the adjustment direction and adjustment correlation, including: The adaptation differences are classified into differences in action timing, differences in action amplitude, and differences in action type. The position adaptation deviations are categorized and analyzed to distinguish spatial position deviations, relative distance deviations, and position timing deviations. For different types of adaptation differences, the corresponding adjustment direction is determined by combining the laws of motion coordination and correlation; For different types of position adaptation deviations, corresponding calibration directions are set according to the position adaptation correlation rules, and key dimensions of position calibration are determined. Analyze the interaction between adaptation differences and positional adaptation deviations to determine the priority order of adjustments; Based on the adjustment direction, calibration direction, and adjustment priority order, a multi-dimensional synchronous drive adjustment logic framework is constructed. Within the regulation logic framework, determine the regulation relationships between each electric actuator and the correspondence between the main regulation mechanism and the cooperative regulation mechanism; The adjustment trigger conditions in the adjustment logic framework are refined to achieve precise matching between the generation and execution timing of adjustment instructions.

7. The synchronous control method for electrification of the maintenance lifting system according to claim 1, characterized in that, The method of controlling the coordinated operation of each electric actuator based on the synchronous drive adjustment command, and collecting the operation feedback status information of each electric actuator to form a linkage control, includes: The synchronous drive adjustment commands corresponding to each electric actuator are sorted according to the working time sequence to generate a command execution sequence; According to the instruction execution sequence, synchronous drive adjustment instructions are sent to each electric actuator one by one to trigger the adjustment action of each electric actuator; During the adjustment process of each electric actuator, real-time operating parameters are continuously collected to form a dynamic operating record; After each electric actuator completes its adjustment action, the operation feedback status information of each electric actuator is collected. The operation feedback status information includes the adjusted operation action information and the adjusted relative positional relationship information between the actuators. The operational feedback status information is correlated and compared with the expected effect information of the synchronous drive adjustment command to analyze the adjustment execution effect; Whether to perform secondary adjustment is determined based on the adjustment execution effect. If secondary adjustment is required, the synchronous drive adjustment instruction is regenerated based on the running feedback status information. The operation feedback status information is fed back to the update module of the multi-dimensional operation status map, and the multi-dimensional operation status map is updated in real time.

8. A synchronous control system for electrifying a maintenance lifting system, characterized in that, The synchronous control system for electrification of the maintenance lifting system includes a processor and a memory, the memory and the processor being connected. The memory is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the memory to implement the synchronous control method for electrification of the maintenance lifting system as described in any one of claims 1-7.

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