A mobile lifting operation platform management system based on intelligent scheduling

Abstract

The present invention discloses a mobile lifting operation platform management system based on intelligent scheduling, specifically related to the field of intelligent resource scheduling. The dense area marking module identifies the job aggregation areas within the scheduling period as the key points for scheduling control. The scheduling adaptability evaluation module combines the platform path conflicts and task waiting time to evaluate the scheduling adaptability of the platform. The fast-paced scheduling domain construction module marks the load imbalance areas as the rhythm compression state according to the task addition and completion ratios. The access judgment module screens the qualified platforms that can enter the compression domain based on the platform performance. The fast-paced scheduling module performs task reallocation, path refinement, and staggered scheduling on the qualified platforms. The non-fast-paced scheduling module guides the task transfer of unqualified platforms to avoid operating in high-density areas. By constructing a closed-loop control chain of identification-evaluation-scheduling, the influence of the task intensive compression effect is further avoided, and the stability and operation efficiency of the overall scheduling are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent resource scheduling. More specifically, the present invention relates to a mobile lifting operation platform management system based on intelligent scheduling. Background Art

[0002] In modern regional logistics centers, to meet the requirements of multi-layer picking, packing, and delivery operations, mobile unmanned operation lifting platforms are generally deployed to replace manual high-position operations and space scheduling. These platforms have a certain ability to move autonomously along paths and can perform lifting operations between multiple vertical shelf layers, and are widely used in multi-task concurrent environments such as daily distribution warehouses and transfer centers. However, during some peak operation periods, such as the short-term order concentration period before shuttle departure, tasks tend to highly aggregate within specific unit areas, and the scheduling system then dispatches multiple mobile lifting platforms to the same area. In this process, problems such as excessive platform aggregation, overlapping operation trajectories, limited lifting, and blocked paths are likely to occur, resulting in some platforms being stranded or failing in the task area.

[0003] Traditional scheduling methods only perform task scheduling based on path costs and idle states, lacking consideration of the adaptability of different lifting operation platforms to intensive operations, and not deeply setting refined scheduling strategies for intensive operation areas. Therefore, the impact of such "task intensive compression effects" cannot be avoided, leading to unbalanced resource scheduling and local path-level conflicts, affecting the stability and operation efficiency of the overall scheduling system. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a mobile lifting operation platform management system based on intelligent scheduling to solve the problems proposed in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A mobile lifting operation platform management system based on intelligent scheduling, including an intensive area marking module, a scheduling adaptability evaluation module, a fast-paced scheduling domain construction module, an access judgment module, a fast-paced scheduling module, and a non-fast-paced scheduling module;

[0007] The intensive area marking module extracts the high-frequency operation aggregation area clusters of each scheduling period and marks the intensive operation areas;

[0008] The scheduling adaptability evaluation module calculates the scheduling adaptability index of the lifting operation platform according to the intersection conflict ratio between the moving path of the lifting operation platform and the intensive operation area, and the task waiting duration;

[0009] Based on the task throughput balance state of the job-intensive area within the current scheduling period, the fast-paced scheduling domain construction module marks the job-intensive areas in the non-equilibrium state as the rhythm compression state and constructs a local fast-paced scheduling domain.

[0010] The access judgment module performs scheduling access judgment on the lifting operation platform based on the task execution record and scheduling adaptability index of the lifting operation platform, restricts unqualified lifting operation platforms from entering the fast-paced scheduling domain, and simultaneously makes access markings for qualified lifting operation platforms.

[0011] In the fast-paced scheduling domain, the fast-paced scheduling module performs dynamic misalignment scheduling on the admitted lifting operation platforms by enabling task reallocation, path refresh compression, and yielding order arrangement.

[0012] The non-fast-paced scheduling module transfers the task objectives of the unqualified lifting operation platforms restricted from entering the fast-paced scheduling domain.

[0013] In a preferred embodiment, the intensive area marking module extracts the high-frequency job aggregation area clusters of each scheduling period. The specific steps for marking the job-intensive areas include:

[0014] Real-time collect the position coordinates of all task objectives within each scheduling period to generate a standard two-dimensional plane task point set.

[0015] Project the task point set onto the job area map and perform grid division, and construct a task density sequence based on the task frequency.

[0016] Use the density peak clustering algorithm to perform clustering operations on the task density sequence, extract the local density centers, and identify the high-frequency job aggregation area clusters in the grid cells, which are marked as job-intensive areas.

[0017] In a preferred embodiment, the scheduling adaptability evaluation module calculates the scheduling adaptability index of the lifting operation platform according to the intersection conflict ratio between the moving path of the lifting operation platform and the job-intensive area, and the task waiting duration. The specific steps include:

[0018] Collect the set of path segments of all lifting operation platform tasks in the previous scheduling period, and the path segments are represented by the moving trajectory line between the starting position and the task objective position of the lifting operation platform in the period.

[0019] Retrieve the job-intensive areas identified in the previous scheduling period, and count the number of intersections of each lifting operation platform path segment in the corresponding job-intensive area.

[0020] Obtain the path conflict ratio of the lifting operation platform by calculating the comprehensive ratio of the path conflict frequency to the number of intersections.

[0021] Obtain the total duration of task start waiting of the lifting operation platform in the previous scheduling cycle as the response delay metric item;

[0022] Perform normalized weighted operation on the path conflict ratio and the response delay metric, and calculate the scheduling adaptability index of each lifting operation platform.

[0023] In a preferred embodiment, the fast-paced scheduling domain construction module marks the job-intensive areas in the non-equilibrium state as the rhythm compression state based on the task throughput balance state of the job-intensive areas in the current scheduling cycle. The construction of the local fast-paced scheduling domain specifically includes:

[0024] Based on the coordinates of the job-intensive areas in the current scheduling cycle, establish an independent task rhythm monitoring sequence for each job-intensive area;

[0025] Calculate the ratio of the number of newly added tasks to the number of completed tasks in the job-intensive area per unit time in the rhythm monitoring sequence as the throughput balance coefficient. When the throughput balance coefficient is greater than 1, it is determined that the tasks in the job-intensive area are in the non-equilibrium state;

[0026] When the task throughput in the job-intensive area is in the non-equilibrium state, mark the corresponding area as the rhythm compression area, and merge the rhythm compression areas based on the job logic association between the rhythm compression areas to form a local fast-paced scheduling domain.

[0027] In a preferred embodiment, the judgment method of the job logic association between the rhythm compression areas is to construct a job flow trajectory through the change sequence of the task target areas in the historical scheduling cycle, and for the rhythm compression areas on the same job flow trajectory, it is determined that there is a logic association between them.

[0028] In a preferred embodiment, the access judgment module performs scheduling access judgment on the lifting operation platform according to the task execution record and the scheduling adaptability index of the lifting operation platform, restricts unqualified lifting operation platforms from entering the fast-paced scheduling domain, and at the same time performs access marking on qualified lifting operation platforms, specifically including:

[0029] Obtain the current task completion progress of each lifting operation platform in the current cycle, and calculate the remaining time required to complete the task;

[0030] Take the remaining time required to complete the task and the scheduling adaptability index as inputs, construct an access scoring function for the lifting operation platform and generate an access score matrix;

[0031] Set the access score threshold for the fast-paced scheduling domain, and based on the threshold comparison, screen out unqualified lifting operation platforms with scores not meeting the standard, prohibit them from participating in the task scheduling of the corresponding fast-paced scheduling domain, and at the same time perform fast-paced scheduling domain access marking on the lifting operation platforms with scores meeting the standard.

[0032] In a preferred embodiment, the fast-paced scheduling module in the fast-paced scheduling domain enables task reallocation, path refresh compression, and yielding order arrangement to perform dynamic dislocation scheduling on the admitted lifting platforms, which specifically includes:

[0033] Within the local fast-paced scheduling domain, recycle all unqualified lifting platforms that have not executed the job task sequence in the current fast-paced scheduling domain, and round-robin allocate the unexecuted job task sequence to the admitted lifting platforms according to the principle of proximity;

[0034] Perform micro-interval sliding window processing on the paths of each admitted lifting platform to generate a compressed path refresh sequence for refined time-sharing path control;

[0035] Identify all path intersection nodes of the lifting platforms within each local fast-paced scheduling domain, and construct a yielding order graph based on task urgency to coordinate the scheduling rhythm, where the task urgency is determined in sequence based on the waiting duration of the task;

[0036] Perform time-axis rearrangement on the path refresh sequence and the yielding order graph to form a multi-lifting platform task time sequence exclusive matrix, and allocate non-overlapping execution windows;

[0037] Dynamically issue task scheduling instructions according to the exclusive matrix to perform relative dislocation scheduling of the start and end times of multi-lifting platform tasks within the same local fast-paced scheduling domain.

[0038] In a preferred embodiment, the non-fast-paced scheduling module performs task target transfer on the unqualified lifting platforms restricted from entering the fast-paced scheduling domain, which specifically includes:

[0039] Collect the task sequences that fail to pass the admission judgment of the local fast-paced scheduling domain, and record the original task target positions;

[0040] Screen the candidate target points in the non-operation intensive areas among the original task target positions;

[0041] Calculate the path costs of candidate target point task transfers for each unqualified lifting platform within the current scheduling cycle to form a task target substitution scoring table;

[0042] Select the optimal transfer target point based on the task target substitution scoring table, direct the task target to the optimal transfer target point, and establish a new scheduling path candidate line;

[0043] Based on the new scheduling path candidate line, guide the corresponding lifting platform to enter the non-operation intensive area for operation.

[0044] In a preferred embodiment, when calculating the path cost for candidate target point task transfer of each unqualified mobile lifting platform, it is necessary to mark the path through-lines covered by all local fast-paced scheduling domains as path breaks.

[0045] Technical effects and advantages of a mobile lifting platform management system based on intelligent scheduling according to the present invention:

[0046] The system first identifies high-frequency task aggregation areas through a dense area marking module, and evaluates the current scheduling adaptability of the platform by combining the platform's historical task execution records and path intersection situations. Subsequently, the scheduling system determines whether the area rhythm is imbalanced based on task throughput and resource response status, automatically marks the unbalanced area as a rhythm compression state, and constructs a local fast-paced scheduling domain. On this basis, an access judgment mechanism is used to screen the adaptable platforms, and task reallocation, path misalignment refresh, and dynamic yield adjustment are implemented for them in the fast-paced scheduling module. At the same time, the inadaptable platforms are guided to the conventional area for scheduling.

[0047] The entire process not only improves the resource coordination efficiency in the task-intensive area, but also realizes the structural diversion of scheduling behavior in the spatial and temporal dimensions, effectively enhancing the system's control and recovery capabilities for scheduling emergencies. Brief Description of the Drawings

[0048] Figure 1 It is a schematic structural diagram of a mobile lifting platform management system based on intelligent scheduling according to the present invention. Detailed Embodiment

[0049] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0050] Embodiment 1

[0051] Figure 1 A mobile lifting platform management system based on intelligent scheduling according to the present invention is given, including a dense area marking module, a scheduling adaptability evaluation module, a fast-paced scheduling domain construction module, an access judgment module, a fast-paced scheduling module, and a non-fast-paced scheduling module;

[0052] The dense area marking module extracts the high-frequency operation aggregation area clusters of each scheduling cycle and marks the operation-intensive areas;

[0053] The scheduling adaptability evaluation module calculates the scheduling adaptability index of the lifting operation platform according to the intersection conflict ratio between the movement path of the lifting operation platform and the operation intensive area, as well as the task waiting duration.

[0054] The fast-paced scheduling domain construction module marks the operation intensive areas in the non-equilibrium state as the rhythm compression state based on the task throughput balance state of the operation intensive areas within the current scheduling period, and constructs a local fast-paced scheduling domain.

[0055] The admission judgment module performs scheduling admission judgment on the lifting operation platform based on the task execution record and the scheduling adaptability index of the lifting operation platform, restricts the unqualified lifting operation platform from entering the fast-paced scheduling domain, and at the same time makes an admission mark for the qualified lifting operation platform.

[0056] The fast-paced scheduling module performs dynamic staggered scheduling on the admitted lifting operation platforms in the fast-paced scheduling domain by enabling task reallocation, path refresh compression, and yield order arrangement.

[0057] The non-fast-paced scheduling module transfers the task objectives of the unqualified lifting operation platforms restricted from entering the fast-paced scheduling domain.

[0058] The intensive area marking module extracts the high-frequency operation aggregation area clusters of each scheduling period and marks the operation intensive areas.

[0059] Set the scheduling period time window (set based on the historical task scheduling period length, for example, 12h). When each cycle node is reached, automatically retrieve the target coordinate information of all current unfinished tasks, including the two-dimensional plane coordinate representation of the task target point in the operation area. All coordinate points are uniformly transformed into the standard map coordinate system set by the system and constructed into a standard two-dimensional task point set.

[0060] Perform regular grid division on the entire operation area map. The division principle determines the cell spacing according to the regional geometric boundary and resolution requirements to form a two-dimensional grid matrix. For each task in the task point set, determine the grid cell it belongs to, and count the frequency (i.e., the number of tasks) of the task target points in each grid within a unit scheduling period to construct a task density matrix.

[0061] Extract the local task density abnormal aggregation areas in space. The system linearly expands the density matrix to generate a task density sequence , each represents the density value of a grid. At this time, each grid cell is both a sample point, and its feature is the local density value. The system uses this as the input of the density peaks clustering algorithm (Density Peaks Clustering, DPC).

[0062] Before executing the DPC algorithm, the system first assigns a Calculate its local density value The minimum distance from the density center The local density By measuring The density similarity of the surrounding points is obtained, while Is the distance to the point with the smallest Euclidean distance among all points with higher density than Based on the joint decision value (the maximum value of the weighted comprehensive operation) of And Select several density center points as the clustering cluster centers

[0063] After performing clustering label assignment on all density points, all grid units within the same clustering cluster are recombined into a set of operation-intensive area units, and each set represents a high-frequency task distribution area cluster. This area cluster structure is encoded as a spatial mask area and marked on the task space map as the operation-intensive area

[0064] The scheduling adaptability evaluation module calculates the scheduling adaptability index of the lifting operation platform according to the intersection conflict ratio between the movement path of the lifting operation platform and the operation-intensive area, as well as the task waiting duration

[0065] The above-mentioned (including the following) mobile lifting operation platform generally refers to an intelligent device with autonomous movement ability and integrated lifting structure, which is used to complete vertical-horizontal composite tasks such as handling, loading and unloading, picking, etc. within a limited operation area, including but not limited to intelligent lifting devices such as forklift arm composite handling vehicles, scissor lift mobile platforms, and automatic lifting picking robots that have the ability of autonomous navigation and path execution and can simultaneously realize multi-point task path planning and operation instruction response

[0066] Automatically retrieve the historical task trajectory information of all lifting operation platforms in the previous scheduling cycle before the start of each scheduling cycle. Each task executed by each platform in the previous cycle is defined by two position points, that is, the starting position of the periodic task and the platform path segment between the task target points, which is expressed as the set of all moving trajectory lines completed by each platform in one cycle

[0067] Synchronously read all the data of the identified operation-intensive areas in the previous cycle, and the operation-intensive areas are all stored in the form of spatial masks. Map each platform path segment to these operation-intensive areas one by one, and count the intersection situations of each path segment in each operation-intensive area, including the length interval of the path segment passing through the intensive area, the number of covered cells, and the number of repeated overlaps, etc. For each platform, finally count the number of intersection segments of all its path segments in all intensive areas, and record whether there are any interference behaviors such as intersection, blockage, or delay with other platform paths in the intersection

[0068] After the path interference event statistics are completed, calculate the path conflict ratio for each platform. The path conflict ratio represents the ratio between the conflict frequency in the path intersection and the total number of intersections of the platform, reflecting the scheduling controllability and path safety level of the platform in the high-density area. As the core factor for measuring path stability, it directly participates in the scheduling adaptability evaluation.

[0069] While extracting the conflict ratio, cumulatively count the task start delay situation of each platform in the previous scheduling cycle. This delay time is defined as the duration from when the task is sent to the platform to when the task starts to be executed. Automatically sum up the delay times of all tasks and use it as a quantitative indicator of the overall response timeliness of the platform. In the scenario of this solution, this task delay time is determined by the lifting platform movement speed, machine startup duration, and platform lifting speed, and is used to characterize the poor fast startup and fast operation capabilities of the lifting operation platform.

[0070] Normalize the two indicators of the path conflict ratio and response delay time for each platform to eliminate the deviation effects in dimensions such as the platform operation cycle and the number of tasks. The two normalized dimensions are introduced into the scheduling adaptability model and weighted fusion is performed according to the set weights to output the scheduling adaptability index of each platform.

[0071] The fast-paced scheduling domain construction module, based on the task throughput balance state in the job-intensive area during the current scheduling cycle, marks the job-intensive areas in the non-equilibrium state as the rhythm compression state and constructs a local fast-paced scheduling domain.

[0072] Call the spatial region coordinate data of the job-intensive areas calibrated in the current cycle, and establish an independent task rhythm monitoring sequence for each job-intensive area. This monitoring sequence takes the cycle time window as the unit and continuously records the dynamic changes of the task status in this area. Statistically calculate two key values in each time period: one is the new addition rate of the task target quantity in this area, and the other is the number of tasks marked as "completed" in this area. Construct the task throughput balance coefficient of this area with the ratio of the two to describe the dynamic load relationship between task generation and task completion.

[0073] During the rhythm judgment process, set the ideal state of throughput balance as the new addition and completion quantities being roughly equivalent (the ratio is close to 1). If a certain area shows that the task new addition speed is significantly greater than the completion speed in multiple consecutive time periods, that is, its throughput balance coefficient is greater than 1, determine that this area is in the scheduling non-equilibrium state and mark this area as the "rhythm compression area". This mark represents that the task accumulation trend in this area needs to be controlled by means of increasing the scheduling frequency and admission control.

[0074] After the rhythm compression areas are formed, analyze whether there is a correlation in scheduling behavior between the compressed areas. Specifically:

[0075] Retrieve the change trajectories of all task target areas within the historical scheduling period, including records such as target area replacement, transfer, and interruption and readjustment during the scheduling process of tasks, to form a task spatial flow trajectory library. Conduct spatial sequence analysis on these trajectories, extract the migration chains between multiple job-intensive areas of tasks, and establish the behavioral path relationships between job areas.

[0076] For any two rhythm compression areas, if there are a large number of tasks in the historical trajectory that have been adjusted from the target area of one party to the other, or the same task group frequently appears between the two areas, then they are marked as within the same job flow trajectory, and at the same time, such areas are determined to have a logical association in scheduling behavior. Based on this behavioral association relationship, perform regional-level merging processing on several rhythm compression areas to construct a local fast-rhythm scheduling domain that includes multiple logically related compression areas. This scheduling domain will serve as the spatial boundary unit for subsequent high-frequency scheduling strategies, effective cycle compression control, and platform resource access constraints.

[0077] The access judgment module performs scheduling access judgment on the lifting operation platform based on the task execution records and scheduling adaptability indicators of the lifting operation platform, restricts unqualified lifting operation platforms from entering the fast-rhythm scheduling domain, and at the same time makes access markings for qualified lifting operation platforms.

[0078] At the beginning of each scheduling period, first obtain the task progress information of all lifting operation platforms that have entered the task execution state within the current period. This task progress information includes dimensions such as the proportion of the completed path of the platform, the execution status of the task instruction, and whether it has entered the job area. According to the task target distance, the remaining path length, and the current task advancement speed, estimate the remaining time required for each platform to complete the current task. This time value directly reflects the time for the platform to reach the next idle state and serves as one of the reference criteria for judging whether the platform has the ability to undertake high-rhythm tasks (without interrupting the current job progress).

[0079] Retrieve the scheduling adaptability index calculated for each platform before this period. This index has comprehensively considered the platform path conflict frequency and response delay indicators and represents the resource adaptability ability of the platform under the current job density state. Use the remaining time for the platform to complete the task and the scheduling adaptability index as input items, and construct a scheduling access scoring function through a preset normalization and weighting mechanism. This scoring function calculates a current access score for each platform, reflecting the comprehensive performance of its scheduling performance under the current rhythm pressure.

[0080] After the scoring function is executed, a fast-paced scheduling domain platform score matrix is generated. The matrix records the admission score values of all lifting platforms corresponding to the current fast-paced scheduling domain. Set the minimum admission score threshold for this scheduling domain. This threshold is comprehensively derived and set based on the task density level, rhythm compression degree, and historical completion stability level of the scheduling domain, serving as the performance baseline for the scheduling behavior in this area.

[0081] Taking the performance baseline as the benchmark, compare and judge all platform scores in the score matrix. Any lifting operation platform with a score lower than the current admission threshold is marked as an unqualified platform by the system, and its scheduling right for tasks within this fast-paced scheduling domain is excluded during the scheduling process. For lifting platforms that meet or exceed the threshold, the system performs admission marking, allowing them to participate in the task allocation and path planning within the scheduling domain, and incorporating their behavior sequences into subsequent rhythm conflict handling and path misalignment algorithms.

[0082] In the fast-paced scheduling domain, the fast-paced scheduling module enables dynamic misalignment scheduling of the admitted lifting operation platforms through task reallocation, path refresh compression, and yielding order arrangement.

[0083] Traverse all lifting operation platforms within the current local fast-paced scheduling domain, and perform task recovery operations on platforms that have been determined to be ineligible for admission. Specifically, recover the task sequences that have not started execution, and reconfirm the target location and start scheduling window of each task. After completing the task extraction, based on the dual-factor principle of the job target distance and the current path delay of the platform, and following the strategy of nearest first and as balanced as possible, reallocate the task sequences to the admitted platforms within the current cycle to ensure scheduling coverage integrity and alleviate scheduling resource skew. The strategy of nearest first and as balanced as possible is as follows:

[0084] First, calculate the spatial distance between the target location of each task to be allocated and the current locations of all currently admitted platforms, and establish a nearest-sorted platform list for each task. Based on the sorted list, adopt a round-robin scheduling mechanism, and rotate to select the most forward available platform as the carrier in the order of task appearance. If a platform has reached its task carrying limit within the current scheduling cycle, automatically skip this platform and allocate the task to the next platform in the sorting.

[0085] This strategy controls the priority allocation of each task to relatively closer admitted platforms, and at the same time controls the allocation order through round-robin, so as to achieve load balancing of platform task loads in terms of space and task volume.

[0086] After the task reallocation is completed, the paths of each platform that has been admitted to the platform are refined. The path refinement is processed in a sliding window manner. The original path is divided into continuous but overlapping micro-interval segments to form a compressed path refresh sequence. Specifically, a spatial sliding window with a fixed length is set, and the window is slid along the moving direction of the platform on the original path to extract the path segment information covered by each sliding window. Each path segment is assigned a clear segment number, estimated time consumption, execution order, and scheduling control label as an independent execution unit. If a path segment crosses an operation-intensive area or a rhythm compression boundary, a conflict identification label and a priority mark are injected into it. This sequence will serve as the basic scheduling granularity unit in the path planning module, supporting subsequent higher-frequency path updates and execution timing adjustments, and serving as the structural basis for yield control.

[0087] Perform a spatial analysis on the path intersection nodes of all platforms within the local fast-paced scheduling domain to identify the location points where path conflicts may occur between platforms. At the intersection nodes, extract the waiting duration of the current scheduling tasks of each platform as the main measure of task urgency. Based on the task urgency, construct a yield order diagram to describe the passing priorities of different platforms in the conflict window at each intersection point.

[0088] Cross-rearrange the compressed path refresh sequence and the yield order diagram, and overall adjust the time axis of the path segments to form a task-level time mutual exclusion matrix. This matrix arranges the start and end windows of tasks according to the platform numbers to ensure that there will be no overlap of the paths of two platforms passing through the same conflict point within the same time period. Through the mutual exclusion matrix, the tasks of each platform are misaligned in the time dimension to construct a micro-structural separation of the scheduling rhythm.

[0089] The non-fast-paced scheduling module specifically transfers the task objectives of the unqualified lifting operation platforms restricted from entering the fast-paced scheduling domain.

[0090] Identify the lifting operation platforms that have not passed the admission judgment of the local fast-paced scheduling domain during the current scheduling cycle, and collect all the pending tasks carried by these platforms, as well as the discarded tasks of the lifting operation platforms that have entered the admission judgment of the local fast-paced scheduling domain. For each task, extract its original target position (coordinate position on the two-dimensional plane task point set) and perform spatial coordinate registration.

[0091] According to the operation-intensive area mask layer during the current scheduling cycle, perform a regional judgment on the original target position of the task, and screen out the candidate target points located in the non-operation-intensive area. Such target points have relatively lower path conflict probability and resource interference risk, and can be used as target transfer candidates for unqualified platform task diversion.

[0092] For each platform that does not have access qualifications, calculate the path cost for all reachable candidate target points. The path cost consists of factors such as the path length from the starting position to each candidate target point, the expected congested section, and the task response time. In the path cost calculation model, all path segments within the currently activated local fast-paced scheduling domain in the current cycle are marked as path breaks. Any scheduling route passing through these path segments will be forced by the system to be marked as an infeasible path, and its cost value will be set to an invalid state and excluded from the candidate path set.

[0093] After excluding all broken paths, construct a task target alternative scoring table based on path length, time cost, and platform reachability. The higher the score value of each target point in the scoring table, the better its suitability as a new task target point. According to the score ranking, select the optimal transfer target point for each task, officially point the task target to this location, and at the same time establish a new feasible scheduling path from the current position of the platform to the new target point as a new path candidate line. After the path candidate line is generated, the module automatically updates the scheduling plan, excludes the unqualified lifting platform from the original scheduling domain task, and guides it to enter the non-operation-intensive area to execute the task according to the new path line.

[0094] Among them, by applying distributed lock control to the program instructions of the task scheduling plan, it is ensured that a single task scheduling cannot be acquired by two or more lifting platforms at the same time. When one of them obtains a new task scheduling plan, the remaining lifting platforms that have not obtained the task scheduling plan automatically re-look for the second-best candidate line in the candidate path set and re-plan the scheduling task.

[0095] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters and threshold selection in the formulas are set by technicians in this field according to the actual situation.

[0096] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0097] Those of ordinary skill in the art will realize that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0098] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0099] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or modules can be in electrical, mechanical, or other forms.

[0100] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical module, and it may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0101] In addition, in each embodiment of this application, each functional module can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0102] If the described function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0103] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0104] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A mobile lifting operation platform management system based on intelligent scheduling, characterized in that, It includes a dense area annotation module, a scheduling adaptability evaluation module, a fast-paced scheduling domain construction module, an admission judgment module, a fast-paced scheduling module, and a non-fast-paced scheduling module; The dense area annotation module extracts the high-frequency job aggregation area clusters of each scheduling cycle and marks the job dense areas; The scheduling adaptability evaluation module calculates the scheduling adaptability index of the lifting operation platform according to the path conflict ratio of the intersection of the moving path of the lifting operation platform and the job dense area, and the task waiting duration; The path conflict ratio of the intersection is obtained by calculating the ratio of the path conflict frequency to the intersection quantity to obtain the path conflict ratio of the lifting operation platform; The fast-paced scheduling domain construction module marks the job dense areas in the non-equilibrium state as the rhythm compression state based on the task throughput balance state of the job dense areas in the current scheduling cycle, and constructs a local fast-paced scheduling domain; The admission judgment module performs scheduling admission judgment on the lifting operation platform based on the task execution record and the scheduling adaptability index of the lifting operation platform, restricts unqualified lifting operation platforms from entering the fast-paced scheduling domain, and at the same time performs admission annotation on the qualified lifting operation platforms; The fast-paced scheduling module performs dynamic misaligned scheduling on the admitted lifting operation platforms in the fast-paced scheduling domain by enabling task reallocation, path refresh compression, and yielding order arrangement; The non-fast-paced scheduling module performs task target transfer on the unqualified lifting operation platforms restricted from entering the fast-paced scheduling domain.

2. The management system of a mobile lifting operation platform based on intelligent scheduling according to claim 1, characterized in that, The dense area annotation module extracts the high-frequency job aggregation area clusters of each scheduling cycle and marks the job dense areas specifically including: Real-time collect the position coordinates of all task targets in each scheduling cycle to generate a standard two-dimensional plane task point set; Project the task point set onto the job area map and perform grid division, and construct a task density sequence based on the task frequency; Use the density peak clustering algorithm to perform clustering operations on the task density sequence, extract the local density centers, and identify the high-frequency job aggregation area clusters in the grid cells, and mark them as job dense areas.

3. The management system of a mobile lifting operation platform based on intelligent scheduling according to claim 1, wherein, The scheduling adaptability evaluation module calculates the scheduling adaptability index of the lifting operation platform according to the path conflict ratio of the intersection of the moving path of the lifting operation platform and the job dense area, and the task waiting duration specifically including: Collect the path segment sets of all lifting operation platform tasks in the previous scheduling cycle, and the path segments are represented by the moving trajectory lines between the cycle start position and the task target position of the lifting operation platform; Retrieve the job dense areas identified in the previous scheduling cycle, and count the intersection quantities of each lifting operation platform path segment in the corresponding job dense areas; Obtain the total duration of task start waiting of the lifting operation platform in the previous scheduling cycle as the response delay index item; Perform normalization weighted operation on the path conflict ratio and the response delay index to calculate the scheduling adaptability index of each lifting operation platform.

4. The management system of a mobile lifting operation platform based on intelligent scheduling according to claim 1, characterized in that, The fast-paced scheduling domain construction module marks the job dense areas in the non-equilibrium state as the rhythm compression state based on the task throughput balance state of the job dense areas in the current scheduling cycle, and constructs a local fast-paced scheduling domain specifically including: Based on the coordinates of the job dense areas in the current scheduling cycle, establish an independent task rhythm monitoring sequence for each job dense area; Calculate the ratio of the number of newly added tasks to the number of completed tasks in the task-intensive area per unit time in the computing rhythm monitoring sequence as the throughput balance coefficient. When the throughput balance coefficient is greater than 1, it is determined that the tasks in the task-intensive area are in an unbalanced state; When the task throughput in the task-intensive area is in an unbalanced state, mark the corresponding area as a rhythm compression area, and merge the rhythm compression areas based on the job logic association between the rhythm compression areas to form a local fast-rhythm scheduling domain.

5. The management system of a mobile lifting operation platform based on intelligent scheduling according to claim 4, characterized in that, The judgment method of the job logic association between the rhythm compression areas is to construct a job flow trajectory through the change sequence of the task target area in the historical scheduling cycle. For the rhythm compression areas on the same job flow trajectory, it is determined that there is a logical association between them.

6. The management system of a mobile lifting operation platform based on intelligent scheduling according to claim 1, characterized in that, The access judgment module performs scheduling access judgment on the lifting operation platform based on the task execution record and scheduling adaptability index of the lifting operation platform, restricts unqualified lifting operation platforms from entering the fast-rhythm scheduling domain, and at the same time performs access marking on qualified lifting operation platforms, specifically including: Obtain the current task completion progress of each lifting operation platform in the current cycle, and calculate the remaining time required to complete the task; Use the remaining time required to complete the task and the scheduling adaptability index as inputs to construct an access scoring function for the lifting operation platform and generate an access score matrix; Set the access score threshold for the fast-rhythm scheduling domain, and based on the threshold comparison, screen out unqualified lifting operation platforms with scores not meeting the standard, prohibit them from participating in the task scheduling of the corresponding fast-rhythm scheduling domain, and at the same time perform access marking for the lifting operation platforms with scores meeting the standard to enter the fast-rhythm scheduling domain.

7. The management system of a mobile lifting operation platform based on intelligent scheduling according to claim 1, wherein, The fast-rhythm scheduling module in the fast-rhythm scheduling domain enables dynamic staggered scheduling of the admitted lifting operation platforms through task reallocation, path refresh compression, and yielding order arrangement, specifically including: In the local fast-rhythm scheduling domain, recycle the unexecuted job task sequences of all unqualified lifting operation platforms in the current fast-rhythm scheduling domain, and round-robin allocate the unexecuted job task sequences to the admitted lifting operation platforms according to the principle of proximity; Perform a micro-interval sliding window process on the path of each admitted lifting operation platform to generate a compressed path refresh sequence for refined time-sharing path control; Identify all path intersection nodes of the lifting operation platforms in each local fast-rhythm scheduling domain, and construct a yielding order graph based on task urgency to coordinate the scheduling rhythm, where the task urgency is determined in order based on the waiting duration of the task; Perform time-axis rearrangement on the path refresh sequence and the yielding order graph to form a multi-lifting operation platform task time sequence mutual exclusion matrix, and allocate non-overlapping execution windows; Dynamically issue task scheduling instructions according to the mutual exclusion matrix to perform relative staggered scheduling of the start and end times of multi-lifting operation platform tasks in the same local fast-rhythm scheduling domain.

8. The management system of a mobile lifting operation platform based on intelligent scheduling according to claim 1, characterized in that, The non-fast-rhythm scheduling module performs task target transfer on unqualified lifting operation platforms restricted from entering the fast-rhythm scheduling domain, specifically including: Collect the task sequences that fail to pass the access judgment of the local fast-rhythm scheduling domain, and record the original target positions of the tasks; Screen the candidate target points in the original target positions of the tasks that are in non-task-intensive areas; During the current scheduling cycle, calculate the path costs for task transfer to candidate target points for each unqualified lifting operation platform, and form a task target substitution scoring table; Based on the task target substitution scoring table, select the optimal transfer target point, direct the task target to the optimal transfer target point, and establish a new candidate line for the scheduling path; Based on the new candidate line for the scheduling path, guide the corresponding lifting operation platform to operate in a non-operation-intensive area.

9. The management system of a mobile lifting operation platform based on intelligent scheduling according to claim 8, characterized in that, When calculating the path costs for task transfer to candidate target points for each unqualified lifting operation platform, it is necessary to mark the path through lines covered by all local fast-paced scheduling domains as path breaks.

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