Task scheduling and path optimization management method and system of intelligent robot

Abstract

The invention provides a task scheduling and path optimization management method and system for an intelligent robot, and relates to the technical field of data processing, and the method comprises the steps: calculating the mutual influence degree of each task request in the time dimension, obtaining a time coupling value, recognizing and calculating the change trend of the motion direction when the robot executes a task, obtaining a motion trend value, and obtaining a path optimization value; identifying an association state of each reservation task in a time dimension and a motion dimension, identifying an association state which does not meet a preset continuous motion condition, adjusting a task association sequence according to the preset continuous motion condition, and arranging an execution sequence of each reservation task, and enabling the adjacent reservation tasks to meet the continuous motion condition in the time coupling value and the motion trend value, limiting the motion change range of the robot in the task execution process, and ensuring that the robot completes the reservation tasks according to the sequential control data. According to the method, scheduling of multiple tasks in the time dimension can be controlled in a linkage mode, and the motion stability of the robot is ensured.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for task scheduling and path optimization management of intelligent robots. Background Technology

[0002] In existing technologies, community door-to-door garbage collection typically employs a collaborative approach between intelligent robots and user terminals to collect garbage. Users submit information such as garbage type, appointment time, and collection location via mobile devices. Upon receiving this appointment information, the backend system assigns the collection task to the corresponding garbage sorting robot based on the robot's current availability and location. After receiving the task instruction, the robot uses its own location information and pre-stored road map data to generate a path from its current location to the user's appointed location via a path planning algorithm. After completing garbage collection, it independently plans its route to the intelligent garbage collection station, achieving centralized garbage disposal and storage. However, in existing technologies, task scheduling and path planning are typically processed based on a single appointment request as the trigger condition. The path control process mainly relies on real-time calculations based on the robot's current location information, which may lack overall constraints and coordinated control over multiple appointment tasks in the time dimension. For example, when multiple users in the same community initiate door-to-door garbage collection requests within a similar time period, the backend system may sequentially assign multiple tasks to the same robot. However, after the robot completes the collection task for the previous user, the spatial relationship between its travel path direction and the next task location or garbage disposal point may not be incorporated into the unified control model in advance. This could lead to the robot frequently adjusting its travel direction and control strategy during continuous task execution, resulting in a discrete state in the path control process and reducing the stability of the robot's motion control process. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for task scheduling and path optimization management of intelligent robots, aiming to solve the problems mentioned in the background art.

[0004] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: Firstly, a method for task scheduling and path optimization management of intelligent robots, the method comprising: Obtain appointment task data and robot operation data to obtain the basic dataset; Based on the basic dataset, the appointment time information of each appointment task is arranged, segmented, and overlapped. The degree of mutual influence of each task request in the time dimension is calculated to obtain the time coupling value. Based on the basic dataset, the order of position changes for each scheduled task is compared with the robot's direction of movement. The trend of the robot's direction of movement during task execution is identified and calculated to obtain the movement tendency value. Based on the time coupling value and motion tendency value, the association status of each scheduled task in the time dimension and motion dimension is identified to obtain task association data; Based on the task association data, identify the association states that do not meet the preset continuous motion conditions, and adjust the task association order according to the preset continuous motion conditions to obtain combined constraint data. Based on the combined constraint data, the execution order of each scheduled task is arranged so that adjacent scheduled tasks satisfy the continuous motion condition in terms of time coupling value and motion tendency value, thus obtaining the sequence control data; Based on the sequential control data, the range of motion changes of the robot during task execution is limited to ensure that the robot completes the scheduled task in accordance with the sequential control data, thereby obtaining continuous control parameters.

[0005] Furthermore, based on the basic dataset, the appointment time information for each appointment task is arranged, segmented, and overlap identified. The degree of mutual influence between each task request in the time dimension is calculated to obtain the time coupling value, including: Based on the basic dataset, the reservation time information of each reservation task is arranged in chronological order, and the reservation time information is segmented according to the preset time span. At the same time, reservation tasks falling within the same time period are identified and marked as overlapping to obtain time overlap data. Based on the time overlap data, the number of appointment tasks marked as overlapping within the same time period and their appointment time distribution are counted, the degree of task concentration within the time period is calculated, and the segment concentration item is obtained. By calculating the degree of time deviation of each scheduled task within the same time period relative to the time distribution benchmark within that time period, the segment offset term is obtained. By comparing the time distribution of reservation tasks within adjacent time periods, the degree of time continuity of reservation tasks between adjacent time periods is calculated, and the segment continuity item is obtained. By fusing the segment concentration term, segment offset term, and segment continuation term, the mutual influence of each scheduled task in the time dimension is calculated to obtain the time coupling value.

[0006] Furthermore, based on the basic dataset, the sequence of position changes for each scheduled task is compared with the robot's movement direction to identify and calculate the trend of movement direction changes during task execution, obtaining motion tendency values, including: Based on the basic dataset, the reservation location information of each reservation task is arranged in chronological order and paired with the robot's movement direction information at the corresponding time point to obtain motion association data; Based on motion correlation data, the displacement length between adjacent scheduled task positions is calculated to obtain the displacement scale term; the magnitude of the change in motion direction between adjacent tasks is calculated to obtain the direction matching term. Based on the displacement scale term and the direction matching term, the overall motion direction trend of each scheduled task during the execution process is calculated to obtain the direction aggregation term; By fusing the displacement scale term, orientation matching term, and orientation aggregation term, the overall trend of the robot's motion direction changes during the execution of multiple scheduled tasks is calculated, and the motion tendency value is obtained.

[0007] Furthermore, based on the temporal coupling value and motion tendency value, the association status of each scheduled task in the time and motion dimensions is identified, resulting in task association data, including: Based on the time coupling value, the impact of each scheduled task on the time dimension is mapped, and the correspondence between the scheduled task and the time coupling value is established to obtain time mapping data. Based on the motion trend value, the changing trend of each scheduled task in the motion dimension is mapped, and the correspondence between the scheduled task and the motion trend value is established to obtain motion mapping data; Based on time mapping data and motion mapping data, appointment tasks that are correlated in both the time and motion dimensions are identified, resulting in two-dimensional filtering data; Based on the dual-dimensional filtering data, the appointment tasks are sorted in the time dimension and matched in the motion direction of the appointment tasks in the motion dimension to construct a hierarchical structure and obtain hierarchical matching data. Based on the hierarchical matching data, reservation tasks within the same associated hierarchy are uniformly encapsulated to determine the associated status of reservation tasks in both time and motion dimensions, thus obtaining task association data.

[0008] Furthermore, based on the task association data, the associated states that do not meet the preset continuous motion conditions are identified, and the task association order is adjusted according to the preset continuous motion conditions to obtain combined constraint data, including: Based on task association data, identify reservation tasks that do not meet continuity in any dimension, and obtain discontinuity identification data. By comparing discontinuous identification data with preset continuous motion conditions, the violation dimension and occurrence segment are determined, and condition judgment data is obtained. Based on the conditional judgment data, the execution order of the scheduled tasks is rearranged in the time dimension, and the directional relationship between adjacent scheduled tasks is corrected in the motion dimension. The scheduled tasks are formed into a task execution chain under continuous motion conditions to obtain the order adjustment data. Based on the data adjustment sequence, the task execution chain and its corresponding time and motion parameters are uniformly encapsulated to determine the execution sequence and motion continuity of the scheduled tasks, thus obtaining combined constraint data.

[0009] Furthermore, based on the combined constraint data, the execution order of each scheduled task is arranged so that adjacent scheduled tasks satisfy the continuous motion condition in terms of temporal coupling value and motion tendency value, thus obtaining sequence control data, including: Based on the combined constraint data, the constraint parameters of each scheduled task in the time and motion dimensions are extracted to obtain the constraint parameter set. Based on the constraint parameter set, the temporal coupling value and motion tendency value of adjacent scheduled tasks are compared to identify the task combinations that are connected under continuous motion conditions, and continuous matching data is obtained. Based on the continuous matching data, the scheduled tasks that meet the continuous motion condition are sorted according to the coupling relationship in the time dimension, and the directional connection order of adjacent tasks is adjusted in the motion dimension to form a continuous task sequence, thus obtaining the sequential arrangement data. Based on the sequentially arranged data, the continuous task sequence and its corresponding time and motion parameters are uniformly encapsulated to determine the parameter mapping relationship of the robot's task execution order, thus obtaining sequential control data.

[0010] Furthermore, based on the sequential control data, the range of motion changes of the robot during task execution is limited to ensure that the robot completes the scheduled task according to the sequential control data, thus obtaining continuous control parameters, including: Based on the sequential control data, the time and motion parameters corresponding to adjacent scheduled tasks in the continuous task sequence are analyzed, and the motion adjustment requirements of the robot during the task switching process are extracted to obtain motion requirement data. Based on the motion demand data, the motion adjustment content between adjacent scheduled tasks is decomposed, and the motion adjustment content is divided into direction change adjustment and position movement adjustment to obtain motion decomposition data. Based on the motion decomposition data, the range of the robot’s directional change and positional movement during task execution is limited to determine the motion change range that satisfies the sequential control data constraints, thus obtaining the change limit data. Based on the change limit data, the motion change range of each task switching process is uniformly encapsulated to determine the range of motion change of the robot during continuous task execution, and thus obtain continuous control parameters.

[0011] Secondly, a task scheduling and path optimization management system for intelligent robots, the system comprising: The data module is used to acquire appointment task data and robot operation data to obtain the basic dataset; The time module is used to arrange, segment, and identify the overlap of the appointment time information of each appointment task based on the basic dataset, calculate the degree of mutual influence of each task request in the time dimension, and obtain the time coupling value. The motion module is used to compare the position change sequence of each scheduled task with the robot's motion direction based on the basic dataset, identify and calculate the trend of the robot's motion direction when performing tasks, and obtain the motion tendency value. The association module is used to identify the association status of each scheduled task in the time dimension and motion dimension based on the time coupling value and motion tendency value, and obtain task association data; The constraint module is used to identify associated states that do not meet the preset continuous motion conditions based on task association data, and adjust the task association order according to the preset continuous motion conditions to obtain combined constraint data. The sequence module is used to arrange the execution order of each scheduled task according to the combined constraint data, so that adjacent scheduled tasks meet the continuous motion condition in terms of time coupling value and motion tendency value, and obtain the sequence control data. The control module is used to limit the range of motion changes of the robot during task execution based on sequential control data, ensuring that the robot completes the scheduled task according to the sequential control data and obtains continuous control parameters.

[0012] The above-described solution of the present invention has at least the following beneficial effects: This invention expresses the degree of mutual influence of each task request in the time dimension by calculating the time coupling value. It converts whether tasks overlap, are interdependent, or have concurrent interference into unified dimensional data that can be directly used in subsequent algorithm judgments. Subsequently, the time coupling value and motion trend value are used to identify the association status, and the association order is adjusted based on preset continuous motion conditions. Therefore, the time coupling value plays the role of a judgment basis field in the data link, enabling the system to provide consistent time relationship output for different task pairs when processing task sequences. It allows for comparable calculations on the time relationships of any adjacent or candidate adjacent tasks, supports subsequent arrangement to form sequential control data with continuity as the goal, and can use the degree of coupling as a threshold or sorting basis when filtering the association of task sets, avoiding making association conclusions based solely on the single dimension of timestamp difference.

[0013] This invention compares the sequence of position changes for each scheduled task with the robot's movement direction, identifies and calculates the trend of movement direction changes when the robot performs the task, and obtains a motion tendency value. It transforms the scattered task position sequence and robot direction sequence into a unified trend description field, so that spatial changes are no longer based on single displacements or instantaneous directions as decision inputs, but on the trend of change as data objects for subsequent association and sorting. Through comparison and trend recognition, multiple motion changes are compressed into a identifiable trend result, so that subsequent steps can use this trend result as the basis for motion dimension association, and participate in two-dimensional association recognition in parallel with the time coupling value. It performs feature processing on the motion state of the controlled object, and can express the spatial conditions of continuous motion based on the motion tendency value, forming spatial continuity constraint input at the data layer, rather than discovering frequent turning or jumping of path instructions at the control execution layer and needing passive repair.

[0014] This invention identifies the association status of each scheduled task in the time and motion dimensions to obtain task association data. It aggregates the time influence relationship and motion trend relationship in the same data structure to form a unified association structure for subsequent control. When processing tasks, the system no longer needs to maintain time sorting queues and spatial path queues separately, nor does it need to repeatedly align the indexes of the two sets of queues in subsequent stages. Instead, it can directly read the association conclusions of the same task pair in the two dimensions from the task association data. This provides a basis for subsequent identification of association statuses that do not meet the continuous motion condition and adjustment of the order. It transforms the conflict from an implicit conflict into an explicit association status field, so that the continuous motion condition can be directly determined by the association status.

[0015] This invention identifies associated states that do not meet preset continuous motion conditions and adjusts the task association order according to the preset continuous motion conditions to obtain combined constraint data. This combined constraint data is a task execution chain data object with continuity constraint significance. Identifying associated states that do not meet the conditions locates discontinuity problems as specific associated state records. Adjusting the order according to the conditions ensures that the corrected task chain structurally meets the continuous motion conditions, providing input boundaries for subsequent execution order arrangement. If the task chain itself has discontinuous associations, even if the control layer uses a smoothing algorithm, it is difficult to satisfy continuity without changing the task order. The combined constraint data embeds the order correction into the data link, so that continuity is satisfied at the task chain level, and subsequent sequence control data generation only needs to be arranged within the combined constraint range.

[0016] This invention arranges the execution order of each scheduled task by combining constraint data, so that adjacent scheduled tasks satisfy the continuous motion condition in terms of time coupling value and motion tendency value. The continuous motion condition is implemented in the adjacency relationship, so that each pair of adjacent tasks not only has an acceptable coupling relationship in the time dimension, but also an acceptable tendency relationship in the motion dimension, forming an execution sequence with a definite adjacency relationship. The sequence control data not only tells the system the order of tasks, but also implicitly or explicitly carries the satisfaction relationship of each adjacent pair in two dimensions. This allows the subsequent control parameter generation to analyze the motion requirements with adjacent pairs as the basic unit. This means that the control input is no longer an unstructured set of points, but a sequence structure with adjacent edges, which facilitates the generation of switching segment control parameters based on edge relationships. Attached Figure Description

[0017] Figure 1 This is a flowchart of a task scheduling and path optimization management method for intelligent robots provided in an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0019] like Figure 1 As shown, embodiments of the present invention propose a task scheduling and path optimization management method for intelligent robots, the method comprising: Obtain appointment task data and robot operation data to obtain the basic dataset; Based on the basic dataset, the appointment time information of each appointment task is arranged, segmented, and overlapped. The degree of mutual influence of each task request in the time dimension is calculated to obtain the time coupling value. Based on the basic dataset, the order of position changes for each scheduled task is compared with the robot's direction of movement. The trend of the robot's direction of movement during task execution is identified and calculated to obtain the movement tendency value. Based on the time coupling value and motion tendency value, the association status of each scheduled task in the time dimension and motion dimension is identified to obtain task association data; Based on the task association data, identify the association states that do not meet the preset continuous motion conditions, and adjust the task association order according to the preset continuous motion conditions to obtain combined constraint data. Based on the combined constraint data, the execution order of each scheduled task is arranged so that adjacent scheduled tasks satisfy the continuous motion condition in terms of time coupling value and motion tendency value, thus obtaining the sequence control data; Based on the sequential control data, the range of motion changes of the robot during task execution is limited to ensure that the robot completes the scheduled task in accordance with the sequential control data, thereby obtaining continuous control parameters.

[0020] In this embodiment of the invention, reservation task data and robot operation data are acquired to obtain a basic dataset, ensuring that subsequent calculations reference time and motion fields within the same indexing system and maintain a consistent data source. Based on the basic dataset, the reservation time information of each reservation task is arranged, segmented, and overlapped to identify the degree of mutual influence between each task request in the time dimension, resulting in a time coupling value. This ensures that the time mutual influence between tasks has a comparable numerical representation at the data layer. Based on the basic dataset, the position change order of each reservation task is compared with the robot's motion direction to identify and calculate the trend of motion direction change when the robot executes the task, resulting in a motion tendency value. This converts the discrete task positions and the robot's instantaneous motion direction into a unified field describing the motion trend of task switching, enabling subsequent association determination to directly reference the motion trend rather than just the single position difference. Based on the time coupling value and the motion tendency value, the association status of each reservation task in the time and motion dimensions is identified, resulting in task association data. This allows the task set to enter subsequent continuous constraint processing in the form of association status, grouping, and indexing.

[0021] Based on task association data, identify association states that do not meet preset continuous motion conditions, and adjust the task association order according to the preset continuous motion conditions to obtain combined constraint data. Explicitly label association connections that do not meet the continuous motion conditions and correct their order within the same association group, ensuring that subsequent sorting inputs use the connection relationships that meet the continuous motion conditions as boundaries. Based on the combined constraint data, arrange the execution order of each scheduled task so that adjacent scheduled tasks meet the continuous motion conditions in terms of time coupling value and motion tendency value, obtaining sequential control data. Each task switch has directly referable predecessor and successor indices and their corresponding time and motion constraint fields. Based on the sequential control data, limit the range of motion changes of the robot during task execution to ensure that the robot completes the scheduled tasks according to the sequential control data, obtaining continuous control parameters. Convert the motion adjustment amounts required for adjacent task switching into interval constraint data with upper and lower bounds, so that control execution references switching segment constraints in a range-limited, sequential index manner and maintains a consistent mapping with the sequential control data.

[0022] This involves acquiring appointment task data and robot operation data to obtain a basic dataset, which specifically includes: The system reads reservation task data from the reservation system through the task access interface and generates a unique task identifier for each reservation task as a subsequent association index. The reservation task data includes at least reservation time information, reservation location information, and task service requirement information. The reservation time information is represented as a start timestamp and an end timestamp. The reservation location information is represented as position coordinates or position codes consistent with the robot's operating coordinate system. The task service requirement information is represented as task type, estimated service duration, and task priority fields. The system performs time zone unification and time base correction on the reservation task data, converting all timestamps into a standard time series under the same clock source. Abnormal records with start timestamps greater than end timestamps are removed, and records with missing end timestamps are supplemented with end timestamps based on the estimated service duration to obtain the reservation task data.

[0023] The system reads robot operation data from the robot controller and positioning unit through the operation data acquisition interface, and generates a sampling timestamp for each piece of operation data to align with the scheduled task data. The robot operation data includes at least the robot's pose information and motion state information at the sampling time. The pose information includes position coordinates and heading angle, and the motion state information includes linear velocity, angular velocity, acceleration estimate, current task occupancy status, and operation mode identifier. The system performs coordinate system transformation on the robot operation data, uniformly converting the coordinates output by the positioning unit into coordinate expressions consistent with the scheduled position information, and performs resampling processing on the sampling timestamps to form a sequence of operation states with a fixed sampling interval. Velocity and angular velocity records exceeding the physical upper limit in the operation state sequence are truncated or removed to obtain the robot operation data.

[0024] When constructing the basic dataset based on standardized reservation task data and standardized robot operation data, the system first establishes a task table with the task identifier as the primary key, and writes the reservation time information and reservation location information of each task into the task table. Then, using the reservation time information as the retrieval condition, it extracts a subsequence of operation status from the robot operation data, covering the start time stamp to the end time stamp of the task, and binds the subsequence of operation status with the corresponding task identifier to form a task-operation status association record. For the movement preparation segment before the start of the task, the system backtracks by a preset preparation window length from the task start time stamp to extract the operation status sequence and binds it with the task identifier to ensure that there is a referenceable historical state for the movement direction and displacement changes during the subsequent task switching phase. Finally, the system encapsulates the task table, the operation status sequence index table, and the task-operation status association record into the basic dataset.

[0025] The preset continuous motion conditions specifically include: The judgment rule takes adjacent scheduled task pairs as the judgment object and uses time coupling value and motion trend value as the judgment input. The system defines continuous motion condition as a combination of continuous conditions in time dimension and continuous conditions in motion dimension. This combination condition is expressed in the data layer as a set of thresholds and a set of intervals, so that subsequent order adjustment operations can perform deterministic judgment and deterministic rearrangement on the associated state.

[0026] The temporal continuity condition includes constraints on the connectable relationship between adjacent scheduled tasks on the time axis. For any adjacent task pair, the system extracts the end timestamp of the preceding task, the start timestamp of the following task, and the scheduled position information of the two tasks. Based on the linear velocity and acceleration estimates in the robot's running data, the system calculates the estimated travel time from the position of the preceding task to the position of the following task. When the system determines that the difference between the start timestamp of the following task and the end timestamp of the preceding task is greater than or equal to the estimated travel time and less than or equal to the preset waiting limit, the system considers the task pair to meet the temporal continuity condition. This ensures that there is an executable time window during the task switching phase, and that this window will not form an excessively long idle waiting period in the data layer. At the same time, the system compares the temporal coupling value of the task pair with the temporal continuity condition threshold. When the temporal coupling value falls within the preset allowable range, the system maintains the temporal correlation validity of the task pair, so that the temporal coupling value plays a constraining role on the degree of mutual influence of time during the sequence adjustment process.

[0027] The motion dimension continuity condition includes constraints on the spatial motion direction and motion change trend of adjacent scheduled tasks. For any adjacent task pair, the system extracts the scheduled position information of the two tasks to form an adjacent displacement vector, and extracts the heading angle at the end of the preceding task and the heading angle before the start of the subsequent task from the robot's operation data. The system calculates the direction change amplitude and direction change rate during the task switching phase. When the system determines that the direction change amplitude does not exceed the preset upper limit of the turning amplitude and the direction change rate does not exceed the preset upper limit of the angular velocity, the task pair is deemed to meet the direction continuity constraint. The system further compares the motion trend value of the task pair with the motion dimension continuity condition threshold. When the motion trend value falls within the preset allowable range, the task pair is deemed to meet the motion trend continuity constraint. The above constraints enable the system to form an executable motion continuity judgment standard for adjacent task switching at the data layer, so that associated states that do not meet the standard are marked as connection relationships that need to be rearranged during sequence adjustment.

[0028] The system encapsulates the continuous conditions in the time dimension and the continuous conditions in the motion dimension into a single task index, forming a preset continuous motion condition data object. This data object includes time window judgment parameters, a reference field for calculating the estimated movement time, an allowed range for time coupling values, an upper limit for the magnitude of directional changes, an upper limit for angular velocity, and an allowed range for motion trend values. This data object serves as the sole criterion for identifying scheduled tasks that do not meet the continuity requirement and adjusting the task association order. Thus, the order adjustment process performs judgment based on the same data object every time the connection relationship is replaced, ensuring that the adjusted task association order simultaneously meets the continuous motion condition in terms of both time continuity and motion trend. This provides an input foundation with consistent constraint semantics for the subsequent generation of sequence control data and continuous control parameters.

[0029] In a preferred embodiment of the present invention, based on the basic dataset, the appointment time information of each appointment task is arranged, segmented, and overlap identified; the degree of mutual influence of each task request in the time dimension is calculated to obtain a time coupling value, including: Based on the basic dataset, the reservation time information of each reservation task is arranged in chronological order, and the reservation time information is segmented according to the preset time span. At the same time, reservation tasks falling within the same time period are identified and marked as overlapping to obtain time overlap data. Based on the time overlap data, the number of appointment tasks marked as overlapping within the same time period and their appointment time distribution are counted, the degree of task concentration within the time period is calculated, and the segment concentration item is obtained. By calculating the degree of time deviation of each scheduled task within the same time period relative to the time distribution benchmark within that time period, the segment offset term is obtained. By comparing the time distribution of reservation tasks within adjacent time periods, the degree of time continuity of reservation tasks between adjacent time periods is calculated, and the segment continuity item is obtained. By fusing the segment concentration term, segment offset term, and segment continuation term, the mutual influence of each scheduled task in the time dimension is calculated to obtain the time coupling value.

[0030] In this embodiment of the invention, based on the basic dataset, the reservation time information of each reservation task is arranged in chronological order, and the reservation time information is segmented according to a preset time span. Simultaneously, reservation tasks falling within the same time period are identified and marked as overlapping, resulting in time overlap data. This allows the system to directly reference the segment set and overlapping set in subsequent calculations to complete statistics and derivations without repeatedly scanning the full task time. Based on the time overlap data, the number of reservation tasks marked as overlapping within the same time period and their reservation time distribution are statistically analyzed. The degree of task concentration within the time period is calculated to obtain the segment concentration term. Whether tasks within the same time period are squeezed into a few moments and whether there is a large overlap is converted into directly comparable numerical fields. By comparing the reservation time of each reservation task within the same time period with the time distribution benchmark within that time period... Offset values ​​are used to calculate the degree of time deviation of tasks within a segment, resulting in a segment offset term. This distinguishes between two time series patterns: tasks concentrated in the middle of the segment and tasks dispersed to the segment boundary, providing more detailed structural information on the degree of time influence. By comparing the time distribution of scheduled tasks in adjacent time periods, the time continuity of scheduled tasks between adjacent time periods is calculated, resulting in a segment continuity term. This distinguishes between two types of situations: abrupt changes in the time center between segments and a smooth transition between the time centers, avoiding subsequent duplicate calculations of adjacent segment statistics and ensuring that the same data caliber is used throughout the entire control link. The segment concentration term, segment offset term, and segment continuity term are fused to calculate the degree of mutual influence of each scheduled task in the time dimension, resulting in a time coupling value. This avoids inconsistencies in judgment caused by the dispersion of multi-source time features and provides a definite data basis for subsequent continuity condition determination.

[0031] In a preferred embodiment of the present invention, based on a basic dataset, the position change sequence of each scheduled task and the robot's movement direction are compared to identify and calculate the trend of the robot's movement direction during task execution, thereby obtaining a movement tendency value, including: Based on the basic dataset, the reservation location information of each reservation task is arranged in chronological order and paired with the robot's movement direction information at the corresponding time point to obtain motion association data; Based on motion correlation data, the displacement length between adjacent scheduled task positions is calculated to obtain the displacement scale term; the magnitude of the change in motion direction between adjacent tasks is calculated to obtain the direction matching term. Based on the displacement scale term and the direction matching term, the overall motion direction trend of each scheduled task during the execution process is calculated to obtain the direction aggregation term; By fusing the displacement scale term, orientation matching term, and orientation aggregation term, the overall trend of the robot's motion direction changes during the execution of multiple scheduled tasks is calculated, and the motion tendency value is obtained.

[0032] In this embodiment of the invention, based on the basic dataset, the reservation location information of each reservation task is arranged in chronological order and paired with the robot's motion direction information at the corresponding time point to obtain motion association data. This avoids field misalignment, index drift, or duplicate lookups when calculating displacement and direction changes. Based on the motion association data, the displacement length between adjacent reservation task positions is calculated to obtain a displacement scale term. This allows subsequent determination of motion continuity and control parameter constraints to use segment length as a constraint basis, avoiding the control boundary being derived solely from direction changes without displacement scale reference. The magnitude of motion direction change between adjacent tasks is calculated to obtain a direction matching term, providing a basis for subsequently forming continuous motion conditions. Provide necessary posture change constraint inputs to ensure that the posture change boundaries of the control segment have a definite object in the data; calculate the overall motion direction trend of each scheduled task during execution based on the displacement scale term and the direction matching term to obtain the direction aggregation term, quantify the direction consistency of multiple control segments, and enable the subsequently generated motion-side indicators to have cross-segment semantic sources; fuse the displacement scale term, the direction matching term, and the direction aggregation term to calculate the overall trend of motion direction changes of the robot during the execution of multiple scheduled tasks, obtain the motion tendency value, and describe the motion continuity of the control segment sequence, so that the determination of subsequent continuous motion conditions and the order adjustment can establish constraint relationships based on a single motion-side variable.

[0033] In a preferred embodiment of the present invention, based on the time coupling value and the motion migration value, the association status of each scheduled task in the time dimension and the motion dimension is identified to obtain task association data, including: Based on the time coupling value, the impact of each scheduled task on the time dimension is mapped, and the correspondence between the scheduled task and the time coupling value is established to obtain time mapping data. Based on the motion trend value, the changing trend of each scheduled task in the motion dimension is mapped, and the correspondence between the scheduled task and the motion trend value is established to obtain motion mapping data; Based on time mapping data and motion mapping data, appointment tasks that are correlated in both the time and motion dimensions are identified, resulting in two-dimensional filtering data; Based on the dual-dimensional filtering data, the appointment tasks are sorted in the time dimension and matched in the motion direction of the appointment tasks in the motion dimension to construct a hierarchical structure and obtain hierarchical matching data. Based on the hierarchical matching data, reservation tasks within the same associated hierarchy are uniformly encapsulated to determine the associated status of reservation tasks in both time and motion dimensions, thus obtaining task association data.

[0034] In this embodiment of the invention, the impact of each scheduled task on the time dimension is mapped according to the time coupling value, establishing a correspondence between scheduled tasks and the time coupling value, resulting in time mapping data. The time coupling value is transformed from an independent numerical value into a structured mapping object corresponding to the scheduled task, enabling subsequent processing to retrieve time impact relationships at the task granularity and perform consistent time dimension comparisons. Based on the motion trend value, the changing trend of each scheduled task on the motion dimension is mapped, establishing a correspondence between scheduled tasks and the motion trend value, resulting in motion mapping data. The motion trend value is solidified into a mapping structure consistent with the task switching relationship, enabling subsequent processing to simultaneously obtain motion trend data for the same adjacent task pair and perform consistency filtering of the motion dimension. Based on the time mapping data and the motion mapping data, scheduled tasks that are correlated in both the time and motion dimensions are identified, resulting in dual... The system filters data in two dimensions, merging the correlation determination of the time and motion dimensions into a single filtering process. This provides subsequent hierarchical matching with data containing only valid two-dimensional correlations. Based on the two-dimensional filtering data, the scheduled tasks are sorted in the time dimension and matched in the motion dimension to construct a hierarchical correlation structure, resulting in hierarchical matching data. The valid two-dimensional correlations are further organized into a structured relationship network with hierarchical and edge type labels, enabling the subsequent encapsulation process to perform aggregation at the hierarchical level and explicitly distinguish between connectable and conflicting relationships within a layer. Based on the hierarchical matching data, scheduled tasks within the same correlation level are uniformly encapsulated to determine the correlation status of scheduled tasks in both the time and motion dimensions, resulting in task correlation data. This converges the scattered two-dimensional correlations into a hierarchical encapsulation structure, enabling subsequent steps to perform continuity determination and order adjustment based on the correlation package object.

[0035] Specifically, based on time mapping data and motion mapping data, appointment tasks that are correlated in both the time and motion dimensions are identified, resulting in two-dimensional filtering data, which includes: First, using the scheduled task identifier as the association key, time mapping data and motion mapping data are read separately. Fields of the same scheduled task in both types of mapping data are aligned and merged to form a joint task record. This joint task record simultaneously includes the time coupling field, time sequence index field, adjacent task time relationship field, motion trend field, motion adjacency relationship field, and motion trend marker field corresponding to the scheduled task. Based on the time sequence index field, a candidate adjacent task pair set is generated in the joint task record. This set must contain at least one adjacent pair formed by each scheduled task and its subsequent scheduled task. Scheduled tasks with overlapping markers within the same segment are added to form concurrent candidate pairs, ensuring the candidate set covers both time adjacency and time overlap, which may trigger association. For each task pair in the candidate adjacent task pair set, the time coupling field and adjacent task time relationship field are read in the time dimension. The time association determination result is calculated based on preset time association determination conditions. These conditions constrain the associativity of task pairs under time period boundaries, relative offsets within segments, and overlapping states. The time correlation determination result is written to the task pair record with a comparable determination identifier. After obtaining the time correlation determination result, the motion tendency field, motion adjacency field, and motion trend marker field of the task pair are read in the motion dimension, and the motion correlation determination result is calculated according to the preset motion correlation determination conditions. The preset motion correlation determination conditions are used to constrain the correlation of the task pair under the trend of motion direction change and displacement switching relationship, so that the motion correlation determination result is written to the task pair record with a comparable determination identifier. The task pairs that satisfy both the time correlation determination result and the motion correlation determination result are written to the two-dimensional valid correlation set, and the task pairs that do not satisfy either determination result are written to the two-dimensional exclusion set, so that the two-dimensional valid correlation set and the two-dimensional exclusion set form mutually exclusive classifications at the data layer. Consistency sorting is performed on the two-dimensional valid correlation set to eliminate the directional conflict of the same task in multiple correlation edges. The sorting process includes unifying the correlation edge direction to the predecessor task pointing to the successor task according to the time order index field, and merging the repeated correlation edges, so that the same task pair retains only one two-dimensional valid correlation edge with a unique direction, generating two-dimensional filtered data.

[0036] Specifically, based on the dual-dimensional filtering data, the appointment tasks are sorted along the time dimension and matched along the movement direction to construct a hierarchical structure, resulting in hierarchical matching data, which includes: First, a time-ordered task sequence is generated for all scheduled tasks using the time-order index field as the sorting criterion. Each scheduled task has a definite position index within the time-ordered task sequence. Simultaneously, the predecessor and successor sets of each scheduled task are recorded in the two-dimensional effective association set. Based on the time-ordered task sequence, a hierarchical expansion is performed along the time direction. The hierarchical expansion is constrained to start with tasks without predecessors as the hierarchical starting point, classifying tasks into layers. The set containing the hierarchical starting point is marked as the first-level node set, and the set of successor tasks with two-dimensional effective association edges to the first-level node set is marked as the next-level node set. This process iterates to generate at least two levels of hierarchical node sets, ensuring that tasks within the same level are in the same or similar time segment and have associative predecessor relationships. After forming the hierarchical node sets, motion direction matching processing is performed on the two-dimensional effective association edges between the same level and adjacent levels in the motion dimension. Motion direction matching processing includes reading the motion adjacency relationship field. Along with the motion trend label field, the motion direction at the end of the predecessor task and the motion direction at the start of the successor task corresponding to the associated edge are extracted, and the direction consistency relationship identifier between the two is calculated. The direction consistency relationship identifier is written into the hierarchical relationship record as the motion matching label of the associated edge. The motion matching labels are thresholded and sorted. The associated edges that meet the direction matching conditions are marked as hierarchical effective connection edges, and the associated edges that do not meet the direction matching conditions but still maintain temporal correlation are marked as hierarchical constraint edges. This ensures that the hierarchical structure has both effective connection edges that can be used for continuous execution and constraint edges that need to be processed in the subsequent continuous motion condition determination. After the hierarchical structure is generated, hierarchical encapsulation is performed on each hierarchical node set. The hierarchical number, hierarchical node set, hierarchical effective connection edge set, hierarchical constraint edge set, and inter-hierarchical connection endpoints are written into the hierarchical object. The time coupling field and motion trend field corresponding to each associated edge are retained in the hierarchical object to form a two-dimensional summary. All hierarchical objects are uniformly encapsulated in the hierarchical number order of the time-ordered task sequence, and the hierarchical matching data is output.

[0037] In a preferred embodiment of the present invention, based on task association data, association states that do not meet preset continuous motion conditions are identified, and the task association order is adjusted according to the preset continuous motion conditions to obtain combined constraint data, including: Based on task association data, identify reservation tasks that do not meet continuity in any dimension, and obtain discontinuity identification data. By comparing discontinuous identification data with preset continuous motion conditions, the violation dimension and occurrence segment are determined, and condition judgment data is obtained. Based on the conditional judgment data, the execution order of the scheduled tasks is rearranged in the time dimension, and the directional relationship between adjacent scheduled tasks is corrected in the motion dimension. The scheduled tasks are formed into a task execution chain under continuous motion conditions to obtain the order adjustment data. Based on the data adjustment sequence, the task execution chain and its corresponding time and motion parameters are uniformly encapsulated to determine the execution sequence and motion continuity of the scheduled tasks, thus obtaining combined constraint data.

[0038] In this embodiment of the invention, based on task association data, scheduled tasks that do not meet continuity requirements in any dimension are identified, resulting in discontinuity identification data. The continuity violation is then located to a specific pair of adjacent tasks and a specific dimension field, providing a definite input boundary and a verifiable chain of evidence for subsequent condition comparison and order reordering. By comparing the discontinuity identification data with preset continuous motion conditions, the violation dimension and occurrence segment are determined, resulting in condition judgment data. This allows the order adjustment operation to be applied specifically to the determined segment, avoiding additional changes to task pairs that have not triggered the conditions. Based on the condition judgment data, the execution order of the scheduled tasks is reordered along the time dimension. In terms of motion, the directional relationship between adjacent scheduled tasks is corrected, and the scheduled tasks are formed into a task execution chain under continuous motion conditions to obtain sequence adjustment data. At the same time, the consistency processing of temporal adjacency relationship and motion adjacency relationship is completed, providing a set of continuous edges and their constraint fields that can be directly referenced for subsequent encapsulation to form combined constraints. Based on the sequence adjustment data, the task execution chain and its corresponding time and motion parameters are uniformly encapsulated to determine the execution order and motion continuity relationship of the scheduled tasks, and obtain combined constraint data. This ensures that when the subsequent sequence control stage reads the combined constraint data, it can determine the continuous constraint boundary and judgment basis of adjacent task pairs by determining the index.

[0039] Specifically, based on task-related data, scheduled tasks that do not meet the continuity requirement in any dimension are identified, resulting in discontinuity identification data, which includes: First, each associated status record in the task association data is read, and the task identifiers, corresponding time coupling values, corresponding motion tendency values, and association level identifiers of two adjacent scheduled tasks are extracted from the associated status record. Based on the association level identifiers, the task association data is traversed hierarchically, ensuring the traversal order matches the association level structure. Within each association level, scheduled tasks are sorted according to their scheduled time information, thus obtaining a sequence of adjacent task pairs within that association level. For each pair of adjacent tasks in the sequence, the scheduled time information of the two tasks is obtained, and the time interval between the two scheduled times is calculated. Simultaneously, the time coupling value corresponding to the task pair is read and compared with the time coupling value reference level within the same association level to determine the deviation of the task pair in the time dimension, thereby forming a time dimension discrimination record. In parallel, for the same pair of adjacent tasks, the scheduled position information of the two tasks is obtained, and the positional distance between the two scheduled positions is calculated. Simultaneously, the robot's motion direction information at the corresponding times of the two tasks is obtained, and the directional change amplitude between the two motion directions is calculated. The motion tendency value corresponding to the task pair is read and compared with the reference level of motion tendency value within the same associated level to determine the deviation of the task pair in the motion dimension, thus forming a motion dimension discrimination record. After obtaining the time dimension discrimination record and the motion dimension discrimination record, the time interval and time deviation are used as the time continuity discrimination input, and the position distance, direction change amplitude, and motion deviation are used as the motion continuity discrimination input. When the time continuity discrimination input satisfies any of the following conditions: the time interval between adjacent tasks exceeds the continuity discrimination window or the time coupling value deviates abruptly from the reference level, the adjacent task pair is marked as discontinuous in the time dimension. When the motion continuity discrimination input satisfies any of the following conditions: the direction change amplitude of adjacent tasks exceeds the continuous direction discrimination boundary or the motion tendency value deviates in the opposite direction or abruptly from the reference level, the adjacent task pair is marked as discontinuous in the motion dimension. The marked adjacent task pairs are uniformly encapsulated according to their task identifier, associated level identifier, trigger dimension identifier, trigger basis, and original time coupling value and original motion tendency value to obtain discontinuity identification data.

[0040] Specifically, by comparing discontinuous identification data with preset continuous motion conditions, the violation dimension and occurrence segment are determined, and condition judgment data is obtained, which includes: The preset continuous motion conditions include at least a time continuity condition set and a motion continuity condition set. The time continuity condition set limits the maximum allowed time interval between adjacent tasks, the allowable deviation range of the time coupling value, and the segment consistency rule for determining whether adjacent tasks are allowed to cross time segment boundaries. The motion continuity condition set limits the maximum allowed directional change amplitude between adjacent tasks, the maximum allowed positional movement distance between adjacent tasks, and the allowable deviation range of the motion tendency value. Discontinuity identification data is read line by line. For each marked pair of adjacent tasks, its trigger dimension identifier is read and entered into the corresponding dimension's condition set. In the time dimension, the time interval of the adjacent task pair is compared with the maximum allowed time interval in the time continuity condition set, and the time coupling deviation of the adjacent task pair is compared with the allowable deviation range in the time continuity condition set. Simultaneously, the reservation time segment identifier of the adjacent task pair is used to determine whether it violates the segment consistency rule, thereby determining whether the time dimension violates the preset continuity condition. The motion conditions are defined and a time-dimensional violation result is generated. In the motion dimension, the directional change amplitude of the adjacent task pair is compared with the maximum allowed directional change amplitude in the motion continuity condition set, the positional movement distance of the adjacent task pair is compared with the maximum allowed positional movement distance in the motion continuity condition set, and the motion tendency deviation of the adjacent task pair is compared with the allowed deviation range in the motion continuity condition set. This determines whether the motion dimension violates the preset continuous motion conditions and generates a motion dimension violation result. Simultaneously, based on the sorting position of the adjacent task pair within the association hierarchy, the violation occurrence position is determined. Several adjacent scheduled tasks are traced back from this violation occurrence position and several adjacent scheduled tasks are added backward to form an occurrence segment covering the context task set. This occurrence segment can include the preceding and subsequent associations that cause discontinuity. The violation dimension, violation result, triggering condition type, occurrence segment range, corresponding task identifier pair, and triggering basis in the discontinuity identification data are uniformly encapsulated to obtain condition judgment data.

[0041] Specifically, based on the conditional judgment data, the execution order of the scheduled tasks is rearranged in the time dimension, and the directional relationship between adjacent scheduled tasks is corrected in the motion dimension. This forms a task execution chain under continuous motion conditions, resulting in sequence adjustment data, which specifically includes: First, the set of scheduled tasks within the occurrence segment is extracted from the task association data and the basic dataset. The sequence of scheduled tasks outside the occurrence segment is kept unchanged as a boundary sequence to avoid introducing irrelevant changes to task relationships that have not triggered violations. During the time-dimensional rearrangement, the reservation time information and corresponding time coupling value of each scheduled task within the occurrence segment are read. The priority of reservation times is used as the primary sorting criterion, while time coupling-related constraint identifiers are used as secondary sorting criteria. The tasks within the occurrence segment are rearranged so that the time interval between adjacent tasks falls within the range allowed by the preset continuous motion conditions, and deviations in time coupling between adjacent tasks no longer trigger violations. During the motion-dimensional directional relationship correction, based on the adjacent task relationships formed after the time-dimensional rearrangement, the reservation position information and robot motion direction information of adjacent scheduled tasks are obtained pairwise, and the position movement distance and directional change between adjacent tasks are calculated. When adjacent task relationships that do not meet the motion continuity condition occur, directional relationship correction is performed using the adjacent task relationship as a local window. The directional relationship correction includes adjusting the connection order of adjacent tasks within the local window and recalculating the adjusted directional change amplitude and position movement distance until the adjacent task relationships within the local window meet the restrictions on directional change amplitude and position movement distance in the preset continuous motion condition, while ensuring that motion deviation no longer triggers violation results. After completing time rearrangement and directional correction, the adjusted task sequence and boundary sequence within the occurrence segment are concatenated according to the original association hierarchy to obtain a continuous task sequence. The time determination result and motion determination result of each pair of adjacent tasks in the continuous task sequence are written into the adjacent relationship record to form an edge constraint field. The continuous task sequence, edge constraint field, occurrence segment range, trigger condition type, and corresponding task identifier are uniformly encapsulated to obtain sequence adjustment data.

[0042] In a preferred embodiment of the present invention, the execution order of each scheduled task is arranged according to the combined constraint data, so that adjacent scheduled tasks satisfy the continuous motion condition in terms of time coupling value and motion tendency value, thereby obtaining sequence control data, including: Based on the combined constraint data, the constraint parameters of each scheduled task in the time and motion dimensions are extracted to obtain the constraint parameter set. Based on the constraint parameter set, the temporal coupling value and motion tendency value of adjacent scheduled tasks are compared to identify the task combinations that are connected under continuous motion conditions, and continuous matching data is obtained. Based on the continuous matching data, the scheduled tasks that meet the continuous motion condition are sorted according to the coupling relationship in the time dimension, and the directional connection order of adjacent tasks is adjusted in the motion dimension to form a continuous task sequence, thus obtaining the sequential arrangement data. Based on the sequentially arranged data, the continuous task sequence and its corresponding time and motion parameters are uniformly encapsulated to determine the parameter mapping relationship of the robot's task execution order, thus obtaining sequential control data.

[0043] In this embodiment of the invention, constraint parameters of each scheduled task in the time and motion dimensions are extracted based on the combined constraint data to obtain a constraint parameter set, forming a unified input form for adjacent task pairs and constraint parameter fields, supporting the deterministic calculation of subsequent continuous matching and sequential arrangement. Based on the constraint parameter set, the time coupling value and motion tendency value of adjacent scheduled tasks are compared to identify task combinations that are connected under continuous motion conditions, obtaining continuous matching data, avoiding the inclusion of adjacent relationships that do not meet the continuous motion conditions in the candidate order. Based on the continuous matching data, scheduled tasks that meet the continuous motion conditions are sorted according to the coupling relationship in the time dimension, and the directional connection order of adjacent tasks is adjusted in the motion dimension to form a continuous task sequence, obtaining sequential arrangement data, providing directly usable adjacent pair inputs for parameter mapping and control boundary generation in subsequent steps. Based on the sequential arrangement data, the continuous task sequence and its corresponding time and motion parameters are uniformly encapsulated to determine the parameter mapping relationship of the robot's task execution order, obtaining sequential control data, avoiding the need to re-perform association judgment and sorting calculations in the control stage.

[0044] Specifically, based on the combined constraint data, constraint parameters for each scheduled task in the time and motion dimensions are extracted to obtain a constraint parameter set, which includes: First, the task association sequence and continuous motion condition correspondence already formed in the combined constraint data are read. The combined constraint data is parsed according to the task execution chain as the basic unit, so that each task execution chain corresponds to a set of scheduled task records with preceding and following relationships. For each scheduled task record, task identification information and chain order information are extracted, and the preceding task identification information and following task identification information that are adjacent to this task are extracted simultaneously, so that each task forms a preceding, current, and following adjacency structure at the data layer. In the time dimension, the scheduled time information, time period identification information, and time coupling value records used to characterize the adjacency influence relationship corresponding to the task and its adjacent tasks are extracted from the combined constraint data. The time coupling value records are associated with the task identification information to establish an association field, so that the time coupling value can be located to the specific adjacent task pair. In the motion dimension, the scheduled position information, robot motion direction information, or motion trend description fields encapsulated in the combined constraint stage are extracted from the combined constraint data corresponding to the task and its adjacent tasks. Motion trend value records representing adjacent motion relationships are extracted, and association fields are established between these records and task identification information to enable the motion trend values ​​to be located to specific adjacent task pairs. Further, constraint fields for preset continuous motion conditions are extracted from the combined constraint data, and these constraint fields are split into time-dimensional constraint fields and motion-dimensional constraint fields. The time-dimensional constraint fields limit the range of time coupling values ​​that adjacent tasks should meet, and the motion-dimensional constraint fields limit the range of motion trend values ​​that adjacent tasks should meet, allowing subsequent comparison steps to directly reference the same set of threshold fields to complete consistency judgments. Data normalization processing is performed on the extracted time and motion fields, unifying reservation time information to the same time base, reservation location information to the same coordinate expression, and direction or trend fields to the same direction reference system. The numerical ranges of time coupling values ​​and motion trend values ​​are aligned with the numerical ranges of continuous motion condition fields, and the normalized fields are written into the constraint parameter set according to a unified structure.

[0045] Specifically, based on the constraint parameter set, the temporal coupling value and motion tendency value of adjacent scheduled tasks are compared to identify task combinations that are interconnected under continuous motion conditions, thus obtaining continuous matching data, which includes: First, a set of adjacent task pairs is generated based on the in-chain ordinal field and the adjacency relationship field. Each adjacent task pair includes a preceding task identifier, a succeeding task identifier, and time coupling value and motion migration value fields for comparison. For each adjacent task pair, the time coupling value corresponding to the adjacent task pair is read from the adjacency parameter subset, and the judgment range corresponding to the adjacent task pair is read from the time dimension constraint field of the continuous motion condition. A time consistency judgment operation is performed, comparing the time coupling value with the judgment range and generating a time judgment mark, so that the continuity conclusion of the time dimension is solidified into the adjacent task pair record as a structured field. For the same adjacent task pair, the motion migration value corresponding to the adjacent task pair is read from the adjacency parameter subset, and the judgment range corresponding to the adjacent task pair is read from the motion dimension constraint field of the continuous motion condition. A motion consistency judgment operation is performed, comparing the motion migration value with the judgment range and generating a time judgment mark, so that the continuity conclusion of the time dimension is solidified into the adjacent task pair record as a structured field. The range is compared between intervals and a motion determination mark is generated, so that the continuity conclusion of the motion dimension is solidified into the record of the adjacent task pair as a structured field. After completing the two-dimensional determination, the time determination mark and the motion determination mark are jointly intersected to obtain the continuous determination result of whether the adjacent task pair meets the continuous motion condition. The continuous determination result is written into the connection status field of the adjacent task pair record, so that the subsequent sorting process can directly read the connection status without repeated calculation. When there are multiple candidate successor tasks or multiple candidate predecessor tasks for the same task, a candidate connection set is formed based on the connection status field of the adjacent task pair record in the constraint parameter set. The candidate connection set is indexed by the task identifier and stores the connectable successor set and connectable predecessor set that meet the continuous motion condition. The corresponding time coupling value and motion migration value are retained in the set as edge attribute fields. The above candidate connection set is further encapsulated into a continuous matching number.

[0046] Specifically, based on continuous matching data, scheduled tasks that meet the continuous motion condition are sorted according to their coupling relationship in the time dimension, and the directional connection order of adjacent tasks is adjusted in the motion dimension to form a continuous task sequence, resulting in sequentially arranged data, which specifically includes: First, the connectable relationship table in the continuous matching data is read. Connectable relationships that meet the continuous motion condition are used as candidate connecting edges for sequence construction. A time sorting weight field is generated for each candidate connecting edge based on the edge attribute table. The time sorting weight field is determined by the time coupling value corresponding to the connecting edge and the task reservation time information, so that the sequence construction can prioritize connecting edges with coupling relationships that better meet the continuity requirements in the time dimension. The sequence starting task is identified in the candidate task set. The sequence starting task is determined by the task with no connectable predecessor task or the number of connectable predecessor tasks that meets the preset starting condition. The sequence starting task is written into the first index of the continuous task sequence, so that the subsequent iteration construction has a clear starting point. The candidate successor task set is read from the connectable successor set using the current sequence end task as the index. The candidate successor task set is sorted according to the time sorting weight field. The candidate successor with the highest sorting result is selected as the next task and written into the continuous task sequence, thus forming a sequence expansion process that advances according to coupling relationships in the time dimension. After completing the candidate successor sorting in the time dimension, for candidate successor tasks with multiple similar time sorting weights or falling into the same sorting interval, further reading is performed. The motion tendency value field in the edge attribute table is retrieved and a direction connection determination field is generated. The direction connection determination field describes whether the motion dimension connection relationship from the current end task to the candidate successor task is consistent with the continuous motion condition. The direction connection determination field is used as a secondary sorting key to reorder the candidate successor set, so that the direction connection order of adjacent tasks is constrained in the motion dimension. When the direction connection determination field of adjacent tasks in the motion dimension of the constructed local segment does not meet the continuity or conflicts with the subsequent connectable set, a local adjustment operation is performed. The local adjustment operation takes the connectable relationship provided by the continuous matching data as the boundary, and swaps or backs up the writing order of adjacent tasks without destroying the time dimension connection relationship. The sequence interval where the adjustment occurs and the corresponding connection edge attribute reference identifier are recorded in the sequential data, so that the local adjustment result has traceable data basis. When the sequence expansion cannot continue to write successor tasks, a new sequence starting task is selected based on the unwritten task set in the continuous matching data and the above construction process is repeated until the preset coverage condition is met. The resulting one or more continuous task sequences are uniformly packaged into sequential data.

[0047] In a preferred embodiment of the present invention, the range of motion changes of the robot during task execution is limited according to sequential control data to ensure that the robot completes the scheduled task according to the sequential control data, thereby obtaining continuous control parameters, including: Based on the sequential control data, the time and motion parameters corresponding to adjacent scheduled tasks in the continuous task sequence are analyzed, and the motion adjustment requirements of the robot during the task switching process are extracted to obtain motion requirement data. Based on the motion demand data, the motion adjustment content between adjacent scheduled tasks is decomposed, and the motion adjustment content is divided into direction change adjustment and position movement adjustment to obtain motion decomposition data. Based on the motion decomposition data, the range of the robot’s directional change and positional movement during task execution is limited to determine the motion change range that satisfies the sequential control data constraints, thus obtaining the change limit data. Based on the change limit data, the motion change range of each task switching process is uniformly encapsulated to determine the range of motion change of the robot during continuous task execution, and thus obtain continuous control parameters.

[0048] In this embodiment of the invention, based on sequential control data, the time and motion parameters corresponding to adjacent scheduled tasks in a continuous task sequence are analyzed to extract the robot's motion adjustment requirements during task switching, obtaining motion requirement data. The time and motion parameters of adjacent task pairs are transformed into switching segment requirement data that can be directly used for constraint calculation, enabling subsequent processing to use the switching segment as the smallest control unit for range limitation and continuous control parameter generation. Based on the motion requirement data, the motion adjustment content between adjacent scheduled tasks is decomposed, dividing the motion adjustment content into direction change adjustment and position movement adjustment, obtaining motion decomposition data. This avoids incomplete constraint expression caused by overlapping fields of different physical dimensions in the same constraint calculation. Based on the motion decomposition data, the robot... During task execution, the range of directional changes and positional movements is limited to determine the motion change range that satisfies the sequential control data constraints, thus obtaining change constraint data. This ensures that the robot's control input within the switching segment is confined to the executable range boundary. The data layer completes the amplitude-limited expression of continuous motion conditions and forms constraint fields that can be directly referenced by the controller. Based on the change constraint data, the motion change range of each task switching process is uniformly encapsulated to determine the range of motion changes of the robot during continuous task execution, obtaining continuous control parameters. This ensures that the system has searchable change boundaries and time-related fields for any task switching segment during execution, guaranteeing that the continuity constraints of the control input sequence are consistently effective throughout the entire sequence.

[0049] Specifically, based on sequential control data, the time and motion parameters corresponding to adjacent scheduled tasks in a continuous task sequence are analyzed to extract the robot's motion adjustment requirements during task switching, resulting in motion requirement data, which includes: First, the continuous task sequence is parsed into a set of task identifiers arranged in execution order, and adjacent task pairs are determined sequentially based on the task identifier set. For any adjacent task pair, the time parameter record and motion parameter record corresponding to the adjacent task pair are retrieved from the sequence control data. Fields such as task start and end time, task request time window, and allowed switching duration between tasks in the time parameter record are mapped to a unified time base to form a switching time context, so that the switching segment corresponding to the adjacent task pair on the time axis has a clear start boundary and a duration boundary. After establishing the switching time context, the end position field, initial position field, and end orientation field corresponding to the adjacent task pair are further extracted from the motion parameter record. The system generates a segment and an initial orientation field, as well as a speed limit field or motion state field related to task execution. It converts the end position field and the initial position field into position vector expressions in the same coordinate system, and converts the end orientation field and the initial orientation field into direction vector expressions in the same representation space. Based on the position vector expressions, it calculates the displacement difference vector required for task switching segments, and based on the direction vector expressions, it calculates the direction difference metric required for task switching segments. At the same time, it associates and encapsulates the displacement difference vector and the direction difference metric with the switching time context, so that each adjacent task pair generates a corresponding motion requirement record. The motion requirement records of each adjacent task pair are written into the requirement set in the order of the continuous task sequence to form motion requirement data.

[0050] Based on motion demand data, the motion adjustment content between adjacent scheduled tasks is decomposed, dividing the motion adjustment content into direction change adjustment and position movement adjustment, resulting in motion decomposition data, specifically including: The motion demand data set is traversed sequentially according to adjacent task pairs. For any motion demand record, its displacement difference vector field is first read and norm calculation is performed to obtain displacement amplitude data. Simultaneously, normalization processing is performed on the displacement difference vector field to obtain displacement direction data, thus decomposing the original displacement difference into structured position movement adjustment data in amplitude and direction form. After the position movement adjustment data is generated, the direction difference measurement field in the same motion demand record is read and combined with the end orientation field and initial orientation field of the adjacent task pair to obtain the rotation axis expression or equivalent direction change reference expression corresponding to the direction change. This ensures that the direction change adjustment data structure includes direction change amplitude data and direction change reference data. After the generation of the two types of adjustment data is completed, the position movement adjustment data and direction change adjustment data are written into the decomposition record of the adjacent task pair, respectively, while retaining the reference relationship with the adjacent task pair identifier and switching time context, so that the decomposition record can be traced back to the corresponding motion demand record. The decomposition records of each adjacent task pair are sequentially encapsulated to form motion decomposition data.

[0051] Specifically, based on the motion decomposition data, the range of directional changes and positional movements of the robot during task execution is limited to determine the motion change interval that satisfies the sequential control data constraints, thus obtaining the change constraint data, which includes: First, the continuous motion condition field is parsed into a set of constraint parameters for the switching segment. This set of constraint parameters is then matched with the adjacent task pair identifiers in the motion decomposition data to determine the constraint parameter entries corresponding to each adjacent task pair. For any adjacent task pair, the displacement amplitude data in its position movement adjustment data is read first, and then compared with the endpoints of the allowable displacement interval in the constraint parameter entries. If the displacement amplitude data falls within the allowable displacement interval, the allowable displacement interval is directly used as the position change interval for that adjacent task pair. If the displacement amplitude data exceeds the allowable displacement interval, the displacement amplitude data is truncated and mapped based on the endpoints of the allowable displacement interval, and the truncated endpoint interval is used as the position change interval. Simultaneously, the displacement direction data is retained to limit the direction of the displacement change in space. After the position change interval is determined, the direction change amplitude in the direction change adjustment data is read next. The data is processed and the direction change amplitude data is compared with the endpoints of the direction allowable interval in the constraint parameter entries. If the direction change amplitude data falls within the direction allowable interval, the direction allowable interval is used as the direction change interval. If the direction change amplitude data exceeds the direction allowable interval, the direction change amplitude data is truncated and mapped based on the endpoints of the direction allowable interval, and the truncated endpoint interval is used as the direction change interval. At the same time, the direction change reference data is retained to limit the action benchmark of the direction change. After both types of intervals are determined, the position change interval, displacement direction data, direction change interval and direction change reference data are uniformly encapsulated with the adjacent task pair identifier to form the change limit record of the switching segment. The change limit record is subjected to consistency verification to ensure that the lower limit of the interval is not greater than the upper limit of the interval and that the interval fields are complete. The change limit records of all switching segments in the continuous task sequence are aggregated in sequence to form change limit data.

[0052] Embodiments of the present invention also provide a task scheduling and path optimization management system for intelligent robots, the system comprising: The data module is used to acquire appointment task data and robot operation data to obtain the basic dataset; The time module is used to arrange, segment, and identify the overlap of the appointment time information of each appointment task based on the basic dataset, calculate the degree of mutual influence of each task request in the time dimension, and obtain the time coupling value. The motion module is used to compare the position change sequence of each scheduled task with the robot's motion direction based on the basic dataset, identify and calculate the trend of the robot's motion direction when performing tasks, and obtain the motion tendency value. The association module is used to identify the association status of each scheduled task in the time dimension and motion dimension based on the time coupling value and motion tendency value, and obtain task association data; The constraint module is used to identify associated states that do not meet the preset continuous motion conditions based on task association data, and adjust the task association order according to the preset continuous motion conditions to obtain combined constraint data. The sequence module is used to arrange the execution order of each scheduled task according to the combined constraint data, so that adjacent scheduled tasks meet the continuous motion condition in terms of time coupling value and motion tendency value, and obtain the sequence control data. The control module is used to limit the range of motion changes of the robot during task execution based on sequential control data, ensuring that the robot completes the scheduled task according to the sequential control data and obtains continuous control parameters.

[0053] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0054] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0055] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0056] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for task scheduling and path optimization management of intelligent robots, characterized in that, The method includes: Obtain appointment task data and robot operation data to obtain the basic dataset; Based on the basic dataset, the appointment time information of each appointment task is arranged, segmented, and overlapped. The degree of mutual influence of each task request in the time dimension is calculated to obtain the time coupling value. Based on the basic dataset, the order of position changes for each scheduled task is compared with the robot's direction of movement. The trend of the robot's direction of movement during task execution is identified and calculated to obtain the movement tendency value. Based on the time coupling value and motion tendency value, the association status of each scheduled task in the time dimension and motion dimension is identified to obtain task association data; Based on the task association data, identify the association states that do not meet the preset continuous motion conditions, and adjust the task association order according to the preset continuous motion conditions to obtain combined constraint data. Based on the combined constraint data, the execution order of each scheduled task is arranged so that adjacent scheduled tasks satisfy the continuous motion condition in terms of time coupling value and motion tendency value, thus obtaining the sequence control data; Based on the sequential control data, the range of motion changes of the robot during task execution is limited to ensure that the robot completes the scheduled task in accordance with the sequential control data, thereby obtaining continuous control parameters.

2. The task scheduling and path optimization management method for intelligent robots according to claim 1, characterized in that, Based on the basic dataset, the appointment time information of each appointment task is arranged, segmented, and overlap identified. The degree of mutual influence between each task request in the time dimension is calculated to obtain the time coupling value, including: Based on the basic dataset, the reservation time information of each reservation task is arranged in chronological order, and the reservation time information is segmented according to the preset time span. At the same time, reservation tasks falling within the same time period are identified and marked as overlapping to obtain time overlap data. Based on the time overlap data, the number of appointment tasks marked as overlapping within the same time period and their appointment time distribution are counted, the degree of task concentration within the time period is calculated, and the segment concentration item is obtained. By calculating the degree of time deviation of each scheduled task within the same time period relative to the time distribution benchmark within that time period, the segment offset term is obtained. By comparing the time distribution of reservation tasks within adjacent time periods, the degree of time continuity of reservation tasks between adjacent time periods is calculated, and the segment continuity item is obtained. By fusing the segment concentration term, segment offset term, and segment continuation term, the mutual influence of each scheduled task in the time dimension is calculated to obtain the time coupling value.

3. The task scheduling and path optimization management method for intelligent robots according to claim 2, characterized in that, Based on the basic dataset, the sequence of position changes for each scheduled task is compared with the robot's movement direction. The changing trend of the robot's movement direction during task execution is identified and calculated to obtain the motion tendency value, including: Based on the basic dataset, the reservation location information of each reservation task is arranged in chronological order and paired with the robot's movement direction information at the corresponding time point to obtain motion association data; Based on motion correlation data, the displacement length between adjacent scheduled task positions is calculated to obtain the displacement scale term; the magnitude of the change in motion direction between adjacent tasks is calculated to obtain the direction matching term. Based on the displacement scale term and the direction matching term, the overall motion direction trend of each scheduled task during the execution process is calculated to obtain the direction aggregation term; By fusing the displacement scale term, orientation matching term, and orientation aggregation term, the overall trend of the robot's motion direction changes during the execution of multiple scheduled tasks is calculated, and the motion tendency value is obtained.

4. The task scheduling and path optimization management method for intelligent robots according to claim 3, characterized in that, Based on the time coupling value and motion migration value, the association status of each scheduled task in the time and motion dimensions is identified, and task association data is obtained, including: Based on the time coupling value, the impact of each scheduled task on the time dimension is mapped, and the correspondence between the scheduled task and the time coupling value is established to obtain time mapping data. Based on the motion trend value, the changing trend of each scheduled task in the motion dimension is mapped, and the correspondence between the scheduled task and the motion trend value is established to obtain motion mapping data; Based on time mapping data and motion mapping data, appointment tasks that are correlated in both the time and motion dimensions are identified, resulting in two-dimensional filtering data; Based on the dual-dimensional filtering data, the appointment tasks are sorted in the time dimension and matched in the motion direction of the appointment tasks in the motion dimension to construct a hierarchical structure and obtain hierarchical matching data. Based on the hierarchical matching data, reservation tasks within the same associated hierarchy are uniformly encapsulated to determine the associated status of reservation tasks in both time and motion dimensions, thus obtaining task association data.

5. The task scheduling and path optimization management method for intelligent robots according to claim 4, characterized in that, Based on the task association data, identify association states that do not meet the preset continuous motion conditions, and adjust the task association order according to the preset continuous motion conditions to obtain combined constraint data, including: Based on task association data, identify reservation tasks that do not meet continuity in any dimension, and obtain discontinuity identification data. By comparing discontinuous identification data with preset continuous motion conditions, the violation dimension and occurrence segment are determined, and condition judgment data is obtained. Based on the conditional judgment data, the execution order of the scheduled tasks is rearranged in the time dimension, and the directional relationship between adjacent scheduled tasks is corrected in the motion dimension. The scheduled tasks are formed into a task execution chain under continuous motion conditions to obtain the order adjustment data. Based on the data adjustment sequence, the task execution chain and its corresponding time and motion parameters are uniformly encapsulated to determine the execution sequence and motion continuity of the scheduled tasks, thus obtaining combined constraint data.

6. The task scheduling and path optimization management method for intelligent robots according to claim 5, characterized in that, Based on the combined constraint data, the execution order of each scheduled task is arranged so that adjacent scheduled tasks satisfy the continuous motion condition in terms of temporal coupling value and motion tendency value, resulting in sequence control data, including: Based on the combined constraint data, the constraint parameters of each scheduled task in the time and motion dimensions are extracted to obtain the constraint parameter set. Based on the constraint parameter set, the temporal coupling value and motion tendency value of adjacent scheduled tasks are compared to identify the task combinations that are connected under continuous motion conditions, and continuous matching data is obtained. Based on the continuous matching data, the scheduled tasks that meet the continuous motion condition are sorted according to the coupling relationship in the time dimension, and the directional connection order of adjacent tasks is adjusted in the motion dimension to form a continuous task sequence, thus obtaining the sequential arrangement data. Based on the sequentially arranged data, the continuous task sequence and its corresponding time and motion parameters are uniformly encapsulated to determine the parameter mapping relationship of the robot's task execution order, thus obtaining sequential control data.

7. The task scheduling and path optimization management method for intelligent robots according to claim 6, characterized in that, Based on the sequential control data, the range of motion changes of the robot during task execution is limited to ensure that the robot completes the scheduled task according to the sequential control data, resulting in continuous control parameters, including: Based on the sequential control data, the time and motion parameters corresponding to adjacent scheduled tasks in the continuous task sequence are analyzed, and the motion adjustment requirements of the robot during the task switching process are extracted to obtain motion requirement data. Based on the motion demand data, the motion adjustment content between adjacent scheduled tasks is decomposed, and the motion adjustment content is divided into direction change adjustment and position movement adjustment to obtain motion decomposition data. Based on the motion decomposition data, the range of the robot’s directional change and positional movement during task execution is limited to determine the motion change range that satisfies the sequential control data constraints, thus obtaining the change limit data. Based on the change limit data, the motion change range of each task switching process is uniformly encapsulated to determine the range of motion change of the robot during continuous task execution, and thus obtain continuous control parameters.

8. A task scheduling and path optimization management system for intelligent robots, characterized in that, The system is used to perform the method as described in any one of claims 1 to 7, the system comprising: The data module is used to acquire appointment task data and robot operation data to obtain the basic dataset; The time module is used to arrange, segment, and identify the overlap of the appointment time information of each appointment task based on the basic dataset, calculate the degree of mutual influence of each task request in the time dimension, and obtain the time coupling value. The motion module is used to compare the position change sequence of each scheduled task with the robot's motion direction based on the basic dataset, identify and calculate the trend of the robot's motion direction when performing tasks, and obtain the motion tendency value. The association module is used to identify the association status of each scheduled task in the time dimension and motion dimension based on the time coupling value and motion tendency value, and obtain task association data; The constraint module is used to identify associated states that do not meet the preset continuous motion conditions based on task association data, and adjust the task association order according to the preset continuous motion conditions to obtain combined constraint data. The sequence module is used to arrange the execution order of each scheduled task according to the combined constraint data, so that adjacent scheduled tasks meet the continuous motion condition in terms of time coupling value and motion tendency value, and obtain the sequence control data. The control module is used to limit the range of motion changes of the robot during task execution based on sequential control data, ensuring that the robot completes the scheduled task according to the sequential control data and obtains continuous control parameters.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

Images