A method, device and equipment for combined scheduling of machines and automated guided vehicles
By establishing a two-way mapping model between transportation corridor data and processing time windows in a dynamic workshop environment, the coordinated scheduling of machines and automated guided vehicles is realized, solving the problems of production continuity and equipment utilization in dynamic environments and improving production stability and efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SUZHOU UNIV
- Filing Date
- 2026-03-18
- Publication Date
- 2026-05-29
Smart Images
Figure CN121860367B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent manufacturing and industrial automation technology, specifically to a method, apparatus, and equipment for the joint scheduling of machines and automated guided vehicles. Background Technology
[0002] Flexible production in smart factories is a core direction for the transformation and upgrading of the manufacturing industry. It aims to achieve real-time optimal allocation of production resources through the deep coupling of machine processing logic and automated guided vehicle (AGV) logistics systems. In recent years, with the maturity of multi-sensor fusion technology and path planning algorithms, workshop scheduling systems have gradually evolved from static scheduling to dynamic and intelligent collaborative scheduling.
[0003] Existing technologies mostly rely on a global scheduling center to pre-select the path for automated guided vehicles (AGVs) based on the machine's processing instruction sequence and predict material arrival times based on average travel speed, thus achieving macro-synchronization between processing cycle time and material transfer. In ideal scenarios with relatively stable environments and fixed process times, this method can maintain basic production continuity and possess a certain degree of scheduling consistency. However, in highly dynamic real-world production scenarios, workshop aisles often experience instantaneous fluctuations in the capacity of material transport paths due to personnel movement, interference from other vehicles, or temporary obstructions. These fluctuations directly cause AGV travel times within specific path segments to exceed the predicted range.
[0004] This kind of spatiotemporal lag at the logistics level can cause a severe misalignment between the machine's original processing task and the actual arrival time of materials, resulting in logical breaks in what was originally a seamless workflow. Existing technologies often treat machine processing instructions and automated guided vehicle (AGV) path planning as two independent decision domains, failing to deeply explore the reverse constraint mechanism of dynamic fluctuations in the logistics environment on the processing time window, making it difficult to achieve synchronous reconstruction of processing flexibility and path constraints. This lack of feedback in the scheduling mode easily leads to ineffective waiting of AGVs or frequent machine downtime, increasing the risk of production process stagnation and system scheduling oscillations, and making it difficult to meet the highly precise control requirements of production logistics in complex environments. Summary of the Invention
[0005] To address the shortcomings of existing technologies, this invention provides a method for the joint scheduling of machines and automated guided vehicles.
[0006] To achieve the above objectives, the technical solution of the present invention is as follows:
[0007] This invention discloses a method for joint scheduling of machines and automated guided vehicles, comprising:
[0008] Acquire machine processing status data; machine processing status data includes process task data with original processing start and end times, automated guided vehicle operation status data, and workshop aisle occupancy data;
[0009] The initial path data for the corresponding transportation task is generated based on the operating status data of the automated guided vehicles and the workshop aisle occupancy data. The initial path data is then divided into several continuous path segments, and a corresponding operating time interval is matched for each path segment to generate transportation corridor data containing spatiotemporal constraints.
[0010] Extract the time interval constraints from the transportation corridor data, and calculate the shiftable or compressible range of the original processing start and end times in reverse based on the time interval constraints, generating corrected processing time window data synchronized with the transportation corridor data;
[0011] Calculate the overlap deviation between the corrected processing time window data and the transportation corridor data on the time axis. When the overlap deviation exceeds the preset safety threshold, recalculate the path segment or correct the processing start time, and perform iterative verification until the overlap deviation converges to within the preset safety threshold, thus obtaining feasible domain consistency data containing execution parameters.
[0012] The feasible domain consistency data is retrieved, transformed into collaborative control instructions, and then issued for execution.
[0013] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention couples AGV paths with time intervals by constructing a transportation corridor, transforming the spatiotemporal occupancy of logistics paths into quantifiable constraint boundaries; and establishes a reverse mapping mechanism from logistics constraints to processing time, correcting processing start and end times in reverse, breaking down the barriers between processing plans and logistics planning in traditional scheduling. Based on this, iterative verification eliminates spatiotemporal overlap deviations in real time, ensuring that the machine processing window and the AGV transportation corridor remain continuously synchronized in a dynamic environment. Thus, this invention achieves deep coordination between processing cycle time and logistics cycle time, effectively solving the problem of process connection gaps caused by channel congestion or equipment fluctuations, significantly reducing equipment idling and system oscillation risks, and improving the continuity of workshop production and overall equipment utilization in dynamic environments.
[0014] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Attached Figure Description
[0015] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein:
[0016] Figure 1 This is a flowchart illustrating a method for joint scheduling of machines and automated guided vehicles according to an embodiment of the present invention.
[0017] Figure 2 This is a schematic diagram of the structure of a joint scheduling device for a machine and an automated guided vehicle according to an embodiment of the present invention;
[0018] Figure 3 This is a schematic diagram of an electronic device structure according to an embodiment of the present invention. Detailed Implementation
[0019] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0020] It should be noted that references to "an embodiment," "embodiment," "example embodiment," etc., in this specification refer to the described embodiment including specific features, structures, or characteristics; however, not every embodiment must include these specific features, structures, or characteristics. Furthermore, such expressions do not refer to the same embodiment. Moreover, when describing specific features, structures, or characteristics in conjunction with embodiments, whether or not explicitly described, it is indicated that incorporating such features, structures, or characteristics into other embodiments is within the knowledge of those skilled in the art.
[0021] In existing technologies, the coordinated scheduling of machine processing and automated guided vehicle (AGV) transportation largely relies on static scheduling or single priority rules, which are insufficient to cope with real-time disturbances in dynamic workshop environments. Traditional scheduling methods separate machine processing plans from AGV path planning. When equipment status fluctuations or channel congestion occur, AGV path blockages cause machines to wait for materials, or machine processing delays cause AGVs to run empty and wait, creating a chain reaction of delays. Existing systems often use static obstacle maps in AGV path planning, failing to provide real-time feedback on changes in machine processing sequence to the path search process; and in machine scheduling, they also fail to use the spatiotemporal constraints of AGV transportation as a basis for adjusting processing windows. Especially under production cycles with multiple concurrent tasks and limited channel resources, single time margin adjustments or local path replanning are insufficient to globally resolve spatiotemporal conflicts, leading to decreased equipment utilization and failing to meet the control requirements of flexible manufacturing for production cycle continuity.
[0022] To address the aforementioned issues, the study discovered a correlation between the spatiotemporal occupancy of automated guided vehicles (AGVs) transport routes and the start and end times of machine processing. Specifically, the time constraints of the transport corridor's route segments and the adjustable windows of the processing tasks form a mapping relationship. By establishing a two-way mapping model between transport corridor data and the adjusted processing time window, the study achieves coordinated adjustment of the spatiotemporal constraints of the route and the processing time sequence boundaries. Further investigation revealed that route reconstruction responds quickly to dynamic congestion but causes significant system disturbance, while time translation, although responding more smoothly, is constrained by process priority. Therefore, the study proposes a strategy for dynamically selecting mitigation strategies based on the overlap deviation value. Further iterative verification and validation demonstrate that the overlap deviation value is introduced as a core control parameter into a closed-loop feedback mechanism, forming an autonomous collaborative system where transport corridor data and processing window data continuously converge to a consistent state.
[0023] After introducing the basic concept of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0024] Example 1
[0025] like Figure 1 As shown, Figure 1 A flowchart illustrating a joint scheduling method for machines and automated guided vehicles (AGVs) is provided, including:
[0026] S101, acquire machine processing status data.
[0027] The machine processing status data includes process task data with original processing start and end times, automated guided vehicle operation status data, and workshop aisle occupancy data.
[0028] For example, the system uses a hardware network consisting of industrial Ethernet, PLC controllers, and vehicle-mounted sensors (such as LiDAR and ultrasonic obstacle avoidance sensors) to extract real-time machine processing status data, such as processing feed status and current load. Simultaneously, it retrieves process task data from the ERP or MES system database interface, including preset raw processing start and end times and the allowed time adjustment range for that process. The automated guided vehicle (AGV) reports its current pose coordinates, remaining battery power, and other operating status data via an onboard positioning module. Furthermore, vision sensors or fiber optic sensor networks deployed at the top of the workshop collect real-time data on the dynamic occupancy of the passageways. This multi-source heterogeneous data undergoes protocol conversion and preprocessing at the edge computing gateway to form an initial dataset in a unified spatiotemporal coordinate system.
[0029] Optionally, the method of this application further includes: obtaining historical completion deviations from machine processing status data, generating execution confidence data based on the statistical distribution of historical completion deviations; and automatically adjusting the processing start and end time elastic step size in the process task data based on the execution confidence data to generate enhanced process baseline data containing disturbance allowance.
[0030] Specifically, after acquiring machine processing status data, the system uses a built-in data mining engine to extract historical completion deviations of the machine over a preset time period (e.g., the past 30 working days). Historical completion deviation refers to the difference between the actual completion time stamp and the planned completion time stamp. Preferably, in one embodiment, the system uses a normal distribution or Gaussian mixture model to statistically fit these deviation samples, thereby calculating the execution confidence data of the current process on a specific machine. This execution confidence data characterizes the machine's probability of completing the process on time when facing uncertainties such as sudden tool wear, program fluctuations, or inconsistent raw material quality.
[0031] To quantify the impact of this uncertainty on the scheduling baseline, the system introduces a dynamic adjustment mechanism for the flexible step size of the processing start and end times. In one embodiment, the logic for generating enhanced process baseline data follows the calculation formula:
[0032] ;
[0033] in, For the generated enhanced process baseline data;
[0034] The original process planning time point;
[0035] To perform confidence level data, its value ranges between [0,1].
[0036] The standard deviation of historical bias;
[0037] The preset disturbance sensitivity coefficient is preferably between 0.1 and 0.5.
[0038] Specifically, when executing confidence data At lower values (e.g., below 0.75), the disturbance reserve term in the formula will automatically increase, thereby widening the elastic boundary between the processing start and end times. When At that time, the disturbance reserve is set to 0.
[0039] In a specific implementation scenario, if the historical completion deviations of the target machine exhibit a clear long-tail distribution, the system will perform logical reasoning using a pre-trained confidence assessment model. This model is built upon a Long Short-Term Memory (LSTM) network, with training conditions set as follows: a learning rate preferably of 0.001, a batch size of 64, and 500 iterations. The training samples cover machine operation logs under different load intensities. The model training employs the Adam optimizer, using the mean squared error between the predicted and actual completion times as the loss function, and introduces a Dropout strategy during training to prevent overfitting. In another embodiment, a GRU network can be used instead of LSTM.
[0040] Preferably, the system sets a preset risk threshold of 0.15. When the calculated disturbance reserve exceeds 15% of the original process duration, the system will trigger an early warning. In this way, the originally static process task data is transformed into enhanced process baseline data containing disturbance reserve, providing data input with physical redundancy for subsequent processing and improving the execution stability of scheduling instructions in industrial noise environments.
[0041] The application of the aforementioned environmental uncertainty feature extraction mechanism enables the system to identify and quantify potential fluctuations in the processing stages from the source. By transforming historical experience data into dynamic enhanced process benchmarks, the system effectively compensates for the low fault tolerance caused by parameter rigidity in traditional scheduling. It achieves adaptive matching between disturbance reserve and dynamic environmental fluctuations, significantly reducing the frequency of spatiotemporal verification failures caused by processing deviations, and improving the robustness of the whole-machine collaborative commands in dynamic interference scenarios and the steady-state operation level of the system.
[0042] S102, generate initial path data for the corresponding transportation task based on the automated guided vehicle operation status data and workshop aisle occupancy data, divide the initial path data into several continuous path segments, match the corresponding running time interval for each path segment, and generate transportation corridor data containing spatiotemporal constraints.
[0043] For example, processor call The algorithm, or Dijkstra's algorithm, starts with the current pose of the automated guided vehicle (AGV) and ends at the target loading / unloading station, calculating the shortest or optimal initial path data without collisions by combining channel occupancy data. The system discretizes this path spatially, dividing it into several continuous path segments with topological relationships. For each path segment, based on the AGV's current kinematic constraints (such as maximum acceleration / deceleration and turning radius) and real-time road conditions, the system calculates the entry and exit times required for the AGV to traverse that specific segment through dynamic simulation or empirical models. This assigns a clear time dimension attribute to each segment, ultimately generating transport corridor data containing a three-dimensional spatial coordinate sequence and a one-dimensional time interval sequence.
[0044] Optionally, generating corrected processing time window data synchronized with the transportation corridor data includes: extracting the latest departure time of the endpoint path segment in the transportation corridor data as the transportation hard constraint boundary; performing a difference calculation between the transportation hard constraint boundary and the original processing start and end times to obtain the maximum boundary value of the shiftable range; and reducing the shiftable range based on the maximum boundary value to generate corrected processing time window data.
[0045] Specifically, the system first extracts the time dimension attributes of the destination path segment from the generated transportation corridor data using a data parsing engine. Preferably, in one embodiment, the system defines the estimated arrival time of the automatically guided transport vehicle at the target loading / unloading station, i.e., the latest departure time of the destination path segment, as the transportation hard constraint boundary. This boundary represents the "deadline" of the material transfer process on the timeline; any processing delay exceeding this limit will lead to a break in the logistics chain.
[0046] Subsequently, the system enters the difference calculation stage, numerically correlating the aforementioned transportation hard constraint boundary with the original processing start and end times. In one embodiment, the system calculates the absolute difference between the transportation hard constraint boundary and the original processing end time. This difference, in physical semantics, represents the time margin of the processing stage relative to the logistics stage, i.e., obtaining the maximum boundary value of the translatable range. To ensure the rigor of data processing, the system sets a preset safety margin coefficient. Its value is preferably between [0.05, 0.15] (for example, set to 0.1), to compensate for acceleration and deceleration fluctuations of the automated guided vehicle in the last segment of the path. The formula for calculating the maximum boundary value is detailed as follows:
[0047] ;
[0048] in, This is the calculated maximum boundary value of the translational range;
[0049] For transportation hard constraint boundaries;
[0050] This refers to the original processing end time defined in the process task data.
[0051] After obtaining the maximum boundary value, the system further performs an interval reduction operation on the original processing translation range. Specifically, the system dynamically compresses or shifts the start and end times of the processing task by determining whether the maximum boundary value falls within a preset allowable translation threshold (e.g., a preset translation threshold of [-300s, 300s]). If the maximum boundary value is negative, it indicates that the logistics link precedes the processing link, and the system will forcibly reduce the processing translation range, thereby generating corrected processing time window data that is completely synchronized with the transportation corridor data in time. This process essentially transforms the path constraints in the physical space into logical constraints in the time domain, providing a precise comparison benchmark for subsequent operations.
[0052] Through the extraction and difference reduction logic of the hard constraint boundaries of transportation described above, the system achieves adaptive correction of the production and processing plan to the dynamic logistics environment. This mechanism effectively solves the problem that the processing end cannot synchronously perceive and adjust when the arrival time of automated guided vehicles fluctuates due to dynamic congestion in the workshop. By accurately quantifying the maximum boundary of the movable range, the timing mismatch between the machine and the automated guided vehicle is eliminated, fundamentally ensuring the physical feasibility of issuing collaborative control commands, and improving the overall logistics turnover efficiency and equipment utilization rate of the workshop.
[0053] S103, extract the time interval constraints from the transportation corridor data, and calculate the shiftable or compressible range of the original processing start and end times in reverse according to the time interval constraints, and generate corrected processing time window data synchronized with the transportation corridor data.
[0054] For example, the system uses the timeline information from the transportation corridor data as a hard constraint and feeds it back into the process task logic. The algorithm extracts the difference between the corridor's end time and start time and compares it with the original processing start and end times. Through back projection technology, it calculates the value by which the start time of the processing can be shifted backward (translation range) or the value by which the processing time can be reduced (compression range) without disrupting the continuity of logistics. Therefore, the system reshapes the original process parameters, generating a corrected processing time window data that is highly aligned with the logistics corridor on the time scale.
[0055] Optionally, recalculating the path segment or correcting the processing start time includes: when recalculating the path segment, calculating the traffic congestion weight of each physical area based on the occupancy frequency and duration of each path segment in the workshop aisle occupancy data, and constructing an environmental cost matrix; and using the start time defined in the corrected processing time window data as the time starting point, using a dynamic heuristic algorithm to search for the path with the minimum cost that satisfies the time constraint in the environmental cost matrix, and generating the updated path segment; when correcting the processing start time, reading the task priority identifier associated with the current transportation task in the process task data, and mapping the overlap deviation value to the shift duration of the processing start time; if the task priority identifier is lower than a preset priority threshold, shifting the processing start time backward according to the shift duration, and generating updated corrected processing time window data; if the task priority identifier is higher than or equal to the preset priority threshold, maintaining the original processing start time and triggering the recalculation of the path segment.
[0056] Specifically, when recalculating path segments, the system first uses real-time acquired workshop aisle occupancy data to digitally model the physical environment. In one embodiment, the system divides the workshop map into several gridded physical regions and continuously monitors the occupancy frequency and duration of each path segment within a preset sampling period (e.g., the past 300 seconds). Preferably, the system calculates the traffic congestion weight for each physical region by introducing an exponentially weighted moving average logic. The traffic congestion weight not only reflects the current congestion intensity but also the randomness of obstacle appearance. Subsequently, the system maps these weights onto each arc segment of the topology map to construct an environmental cost matrix. This matrix represents the dynamic distribution of traffic costs at various points in the workshop in real time.
[0057] To quantify the passage cost of arc segments in the environmental cost matrix, the system uses the following calculation formula:
[0058] ;
[0059] in, The dynamic travel cost from node i to node j in the physical region;
[0060] The physical straight-line distance between nodes;
[0061] and These represent the frequency of occupancy and the longest single occupancy duration for that segment, respectively.
[0062] and The preset weight sensitivity coefficient is preferably in the range of [1.0, 5.0].
[0063] After the environmental cost matrix is constructed, the system uses the processing start time defined in the modified processing time window data as the starting time reference for the calculation path. Specifically, the processor calls a dynamic heuristic search algorithm (such as time-dimensional augmented search). The algorithm performs a global optimal search within the environmental cost matrix. During the search, the algorithm not only considers minimizing spatial distance but also enforces a time constraint: the estimated arrival time of each candidate path must fall within the redundancy allowed by the modified processing time window. If a path segment has a low cost value but would cause the arrival time to exceed the window limit, the path will be automatically eliminated by the algorithm. Preferably, the algorithm's iteration termination condition is set to finding the path with the minimum cost value that meets the time convergence requirement, thereby generating an updated path segment.
[0064] Through this path reconstruction mechanism based on dynamic congestion weights, the system achieves intelligent avoidance of production disturbances by logistics trajectories. This process transforms a static physical map into a dynamically perceptible cost field, using heuristic search to find the optimal solution in the spatiotemporal dimension. This ensures that the updated path segments are not only physically feasible but also highly synchronized with the processing window in the temporal dimension. It effectively solves the problem of original plans failing due to local channel congestion, maximizing the balance between the operating efficiency of automated guided vehicles and the timeliness of production tasks, and guaranteeing the continuity and steady-state characteristics of the entire workshop scheduling system.
[0065] When revising the processing start time, the system first uses a data interface to read the task priority identifier, which is deeply associated with the current transportation task, from the process task data. This identifier is dynamically set by the production management level based on order urgency, material shelf life, and downstream process requirements. Preferably, in one embodiment, the system converts the acquired overlap deviation value into a shift duration for the processing start time using a linear mapping function. This duration is essentially the time difference required to compensate for logistics delays.
[0066] When determining the execution logic, the system compares the task priority identifier with a preset priority threshold. The preset priority threshold is typically set within the range of [1, 10] (e.g., a threshold of 7). Specifically, when the task priority identifier is lower than the preset priority threshold (e.g., the process is a non-critical path task or a routine inventory replenishment task), the system determines that the processing task has high time flexibility. In this case, the processor calls the time shift operator to precisely shift the original processing start time backward according to the shift duration. The correction logic follows the calculation formula below:
[0067] ;
[0068] in, The updated processing start time;
[0069] This refers to the start time of processing before correction;
[0070] The calculated translation time;
[0071] This is the preset translation step size coefficient, preferably 1.0.
[0072] The system generates updated and corrected processing time window data, thereby achieving spatiotemporal consistency without changing the path.
[0073] Preferably, when the task priority indicator is higher than or equal to a preset priority threshold (e.g., the process involves an expensive continuous heat treatment process or a high-priority urgent order), the system determines that the processing sequence has rigid constraints and cannot be easily changed. In this embodiment, the system maintains the original processing start time unchanged and triggers the path reconstruction logic in reverse, forcing the automated guided vehicle to adapt to the processing end by searching for a faster path or invoking a higher-privilege access strategy. This priority-based hierarchical decision-making mechanism ensures the stability of the core production plan.
[0074] Through the aforementioned task priority-based classification and correction process, the system achieves a balance between scheduling flexibility and production rigor. It avoids production rhythm disruptions caused by blindly adjusting processing plans, and utilizes task priority as a decision boundary to achieve differentiated management of spatiotemporal conflicts. This effectively solves the problem of high-value processes being forced to frequently reschedule due to logistical fluctuations. While ensuring the certainty of production tasks, it maximizes the time flexibility of non-core processes, improving the intelligence and robustness of the entire production line scheduling.
[0075] Specifically, based on the occupancy frequency and duration of each path segment in the workshop aisle occupancy data, the traffic congestion weight of each physical area is calculated, and an environmental cost matrix is constructed. Then, using the start time defined in the revised processing time window data as the starting point, a dynamic heuristic algorithm is used to search for the path with the minimum cost that satisfies the time constraint within the environmental cost matrix, generating updated path segments. This includes extracting the real-time occupancy rate of each path segment in the workshop aisle occupancy data and mapping it to the traffic pressure index of each physical area. According to the formula Update the passage cost G of the path segment, where The base length cost is used, and k is the sensitivity coefficient. The end boundary of the corrected processing time window data is used as the target time threshold of the heuristic path search algorithm. During the search process, all branches of the path segment that cannot reach the destination within the target time threshold are eliminated in real time. Based on the updated passage cost G, the heuristic path search algorithm is used to search for the path with the minimum cost in the workshop map topology, which is used as the replanned path segment. The replanned path segment is fed back and rematched with the corresponding running time interval until the spatiotemporal consistency comparison is passed.
[0076] For example, the value of k is preferably in the range of [1.5, 3.5]. This formula ensures that when traffic pressure in a certain area increases, its passage cost increases linearly or exponentially, thereby guiding the heuristic path search algorithm to automatically avoid congested areas.
[0077] During the path search execution phase, the algorithm introduces a "temporal hard pruning" mechanism. Specifically, the system uses the ending boundary of the previously generated corrected processing time window data as the target time threshold for the heuristic path search algorithm. When using a heuristic path search algorithm (such as...) When performing graph topology traversal using either the optimized Dijkstra's algorithm or other methods, the estimated arrival time is calculated for each candidate branch expanded at each step. Preferably, if the estimated arrival time of a branch, after considering real-time traffic pressure, is later than the target time threshold, the algorithm will remove the branch in real time, even if its cost is low. This approach eliminates all path schemes that may cause production tasks to time out from the logical source.
[0078] Based on the updated pass value The algorithm searches the workshop map topology and selects the path with the lowest cost, encapsulating it as a replanned path segment. This then initiates a new round of runtime interval matching. Specifically, this feedback loop continues to run, iteratively correcting the path trajectory and time parameters until the overlap deviation between the new transport corridor data and the corrected processing time window data on the time axis completely converges to a preset safety threshold. Within this timeframe, a spatiotemporal consistency comparison is completed.
[0079] Through the aforementioned heuristic path search algorithm based on dynamic variable weights, this invention achieves real-time resonance between path planning and processing timing. This processing flow directly transforms the complex workshop environment pressures into calculable costs, and combined with strict time threshold filtering, significantly improves the reliability of path reconstruction. It effectively solves the problem of insufficient flexibility in traditional path planning when dealing with sudden congestion, ensuring not only the optimality of the automated guided vehicle's (AGV) travel path but also the timeliness of logistics delivery before the processing window closes, providing robust underlying motion support for the entire joint scheduling system.
[0080] S104 Calculate the overlap deviation value between the corrected processing time window data and the transportation corridor data on the time axis. When the overlap deviation value exceeds the preset safety threshold, recalculate the path segment or correct the processing start time, and perform iterative verification until the overlap deviation value converges to within the preset safety threshold, and obtain feasible domain consistency data containing execution parameters.
[0081] For example, the central processing unit (CPU) performs a difference calculation between the corrected time window data and the time interval of the transportation corridor, quantifying it as a spatiotemporal overlap deviation value. The system's internal logic judgment unit executes threshold triggering logic: if the deviation value exceeds a preset safety redundancy range, an iterative mechanism is triggered based on the system's computing power allocation status. If path reconstruction is chosen, the cost function weights are changed to find faster path segments while avoiding congested areas; if timing correction is chosen, the processing start time is adjusted in millisecond increments. This process recursively approaches the target value until the overlap deviation value converges to the preset safety threshold. Within this range, the feasible domain consistency data, including machine action timestamps, automated guided vehicle speed vectors, and coordinate sequences, was finally locked in.
[0082] The formula for calculating the corrected processing time window data is as follows:
[0083] ;
[0084] ;
[0085] in, and These are the revised processing start time and the revised processing end time, respectively.
[0086] and These are the original processing start time and original processing end time extracted from the process task data, respectively.
[0087] and This is the boundary correction increment calculated based on transport corridor data. .
[0088] Specifically, This represents the adjustment amount by which the processing start time must be shifted due to logistics route conflicts or delays in preceding transportation tasks. For example, , The time required for the automated guided vehicle to travel to a safe distance.
[0089] This indicates the amount of advance adjustment that must be made to the processing end time in order to allow time for materials to be moved out of the buffer zone, or due to the opening time of subsequent transportation sections.
[0090] Preferably, in one embodiment, the system enforces constraints. and The physical meaning of this constraint is that the correction process always seeks a more robust convergent subset within the original time frame, rather than blindly expanding the process time. For example, when it detects that the automated guided vehicle's arrival time at the destination is delayed by 15 seconds due to obstacle avoidance, the system will... Set it to 15, thus setting the corrected processing start time. Push back to ensure the machine is started only after the material has been accurately delivered.
[0091] Through the above technical solution, the system achieves dynamic reduction and precise positioning of the processing time window. This correction method based on "boundary increment" transforms complex spatiotemporal conflicts into simple linear offset calculations, significantly reducing computational overhead. It effectively solves the cascading delay problem of "a change in one part affects the whole" in traditional scheduling schemes. By fine-tuning the processing boundaries in local sections, it ensures the flexible stability of the overall production cycle and, from a mathematical logic perspective, ensures that the issuance of collaborative instructions has extremely high timeliness accuracy and execution determinism.
[0092] S105 calls the feasible domain consistency data, converts it into collaborative control instructions, and issues them for execution.
[0093] For example, consistent data is parsed into specific messages conforming to the underlying communication protocol (such as Modbus TCP, OPC UA, or ROS message format). The system, through a wireless bridge or 5G industrial intranet, synchronously sends the machine-side start / stop control commands and the automated guided vehicle's trajectory movement commands to the execution unit.
[0094] Optionally, after calling the feasible domain consistency data, converting it into collaborative control instructions and issuing them, the method further includes: obtaining the current remaining power in the automated guided vehicle (AGV) operating status data, and retrieving the unit energy consumption curves of each path segment at different speed levels to generate an AGV energy consumption cost field; under the constraint of convergence of overlap deviation values, extracting the time redundancy of each path segment in the transportation corridor data, and searching for the optimal speed instruction value that minimizes the total energy consumption in the AGV energy consumption cost field; and injecting the optimal speed instruction value as an execution parameter into the collaborative control instructions.
[0095] Specifically, after completing the feasible domain consistency check, the system first retrieves the current remaining battery power from the automated guided vehicle (AGV) operating status data. Preferably, in one embodiment, the system retrieves the unit energy consumption curves (e.g., the quadratic function relationship between energy consumption and speed per unit distance) of various AGV models under different loads and speed levels from the local database. The system combines the physical properties of the current path, such as slope and friction coefficient, to map these energy consumption characteristics onto the generated transport corridor, generating a dynamic AGV energy consumption cost field. This cost field intuitively characterizes the energy consumption efficiency of the AGV traveling at different speeds on different road sections.
[0096] To find room for energy-saving optimization, the system was in operation when the overlap deviation value had converged to a preset safety threshold. Under the premise of within limits, the system extracts the time redundancy of each path segment in the transport corridor data. Specifically, due to path reconstruction or processing window adjustments, automated guided vehicles (AGVs) often do not need to travel at their rated maximum speed to arrive on time. By comparing the hard deadline of the transport corridor with the estimated arrival time of the AGV traveling at its current speed, the system identifies "time elastic buffers" that can be used for speed adjustment.
[0097] Under the dual constraints of energy consumption cost and time redundancy, an optimal speed search algorithm is executed. Preferably, in one embodiment, the system uses the Lagrange multiplier method or dynamic programming algorithm to search for a speed allocation scheme that minimizes the total energy consumption of the entire path, provided that the total travel time does not exceed the redundancy. The calculation model for the optimal speed command value is as follows:
[0098] ;
[0099] ;
[0100] in, This represents the minimum total energy consumption.
[0101] For the i-th segment at speed The power function under the following conditions;
[0102] This is the length of the segment;
[0103] The latest delivery time allowed by the transport corridor.
[0104] Using this mathematical model, the system calculates the optimal speed command value for each path segment.
[0105] Finally, the calculated optimal speed command value is used as a key execution parameter and injected into the collaborative control command. Specifically, the message sent to the automated guided vehicle chassis controller not only contains the coordinate sequence but also speed-limiting or constant-speed control operators for different sections. In this way, the system achieves precise empowerment of the execution terminal.
[0106] Through the aforementioned output control logic based on energy consumption balance, this invention transforms previously idle time redundancy into energy efficiency gains. This processing flow breaks away from the blind "arrive as quickly as possible" strategy of traditional scheduling, achieving a balance between logistics speed and energy costs. It effectively solves the problems of uneven power consumption and system efficiency decline caused by frequent charging in large-scale automated guided vehicle (AGV) cluster operations. Building upon the technological foundation of ensuring timely production tasks, it achieves low-carbon and sustainable workshop-level logistics operations, improving the overall operational economic benefits of the system.
[0107] Example 2
[0108] In another embodiment, this application further proposes that the method further includes: extracting the task sequence queue length from the machine processing status data, combining it with the start time of the corrected processing time window data, and calculating the resource occupancy probability value of the equipment within a future preset time; when the resource occupancy probability value is lower than a first preset threshold, generating idle risk data representing the risk of material shortage; when the resource occupancy probability value is higher than a second preset threshold, generating congestion risk data representing the risk of buffer overflow; and encapsulating the idle risk data or congestion risk data into equipment load trend data as a weight adjustment factor for adjusting the path reconstruction weight.
[0109] Specifically, the trend analysis module first extracts the task sequence queue length from the machine processing status data. This indicator directly reflects the current backlog of pending processes. Preferably, in one embodiment, the system combines the start time of the corrected processing time window data with a queuing theory model (such as the M / M / 1 model) and a production step size fluctuation function to calculate the probability value of resource occupancy of the equipment within a future preset time (e.g., the next 15 to 30 minutes). This probability value characterizes the probability that the target equipment will be in a busy state within a specific time window.
[0110] To accurately quantify this trend, the system calculates the resource occupancy probability value using the following formula:
[0111] ;
[0112] in, This is the calculated probability value of resource occupancy.
[0113] The arrival rate of tasks flowing in per unit time;
[0114] The expected processing service rate of the equipment;
[0115] This represents the estimated total duration of the queuing sequence.
[0116] This is the preset observation time span.
[0117] After obtaining the resource occupancy probability value, the system executes a tiered threshold discrimination logic. Preferably, in one embodiment, the system presets a first preset threshold of 0.35 and a second preset threshold of 0.85. Specifically, when the resource occupancy probability value is lower than the first preset threshold (e.g., ...), the system will determine the second preset threshold. When the probability value exceeds a second preset threshold (e.g., when the probability value is less than a certain threshold), the system determines that the equipment may be "starved" and generates idle risk data to represent the risk of material shortage; conversely, when the probability value exceeds a certain threshold (e.g., when the probability value is less than a certain threshold), the system determines that the equipment may be "starved" and generates idle risk data to represent the risk of material shortage. When this occurs, it indicates a severe backlog in downstream processes, and the system generates congestion risk data characterizing the risk of buffer overflow. This risk data is then encapsulated into equipment load trend data, serving as a key control variable.
[0118] As the core of the adjustment mechanism, equipment load trend data is transformed into weight adjustment factors for path reconstruction. Preferably, when idle risk is identified, the system automatically lowers the congestion penalty coefficient in the path search algorithm, forcing the automated guided vehicles (AGVs) to replenish materials via the highest priority path to avoid downtime losses; when congestion risk is identified, the path cost is appropriately increased, guiding the AGVs to postpone delivery by extending travel time or changing routes. This load trend-based weight adjustment mechanism achieves a dynamic game between logistics speed and machine processing capacity.
[0119] By introducing a trend analysis module, the system achieves early detection and precise intervention of production bottlenecks. This process elevates isolated logistics scheduling to a global load balancing strategy, using probabilistic potentials to predict potential material shortages and congestion risks. It effectively solves the problem of global scheduling failure caused by local task accumulation. By dynamically adjusting the weight coefficients of path reconstruction, it achieves automatic peak shaving and valley filling of production flow in the spatiotemporal dimensions, enhancing the system's stable operation under extremely high or low load fluctuations.
[0120] Specifically, the heuristic correlation judgment based on equipment load trend data and enhanced process baseline data includes:
[0121] Acquire equipment load trend data. If the target machine is identified as having an idle risk, extract the overlap deviation value of the corresponding transportation task and forcibly reduce the path search cost coefficient when replanning the path in order to shorten the convergence time of the transportation corridor.
[0122] Perform a time-series consistency check on the transportation corridor data, the corrected processing time window data, and the equipment load trend data. If it is determined that the corrected processing time window data exceeds the time interval of the transportation corridor data, then select to expand and reconstruct the path segment or reduce and adjust the processing time window according to the load trend.
[0123] Continuously monitor the adjusted overlap deviation value until the load balancing constraint and spatiotemporal overlap constraint are met, and obtain the feasible domain consistency data.
[0124] Specifically, the system first acquires real-time equipment load trend data. If this data identifies a target machine as having idle risk (i.e., the equipment is in a "starved" state, with a resource occupancy probability below a preset threshold), the system immediately extracts the overlap deviation value of the corresponding transportation task. At this point, to prioritize material supply to the production line, the processor executes forced intervention logic, dynamically reducing the path search cost coefficient in the path replanning algorithm (e.g., reducing the congestion weight coefficient). (Temporarily reduced by 30%-50%). This operation essentially grants the task higher passage privileges, allowing automated guided vehicles to take routes that may have slight congestion but are physically shorter, thereby reducing the convergence time of the transport corridor and ensuring that materials are delivered as quickly as possible.
[0125] Subsequently, the system enters the multi-dimensional time-series consistency verification stage. The transportation corridor data, the corrected processing time window data, and the equipment load trend data are time-series aligned and compared. Preferably, in one embodiment, if it is determined that the corrected processing time window data exceeds the time interval covered by the transportation corridor data (i.e., a spatiotemporal discontinuity that cannot be directly overlapped), the system does not perform blind shifting, but instead executes differentiated strategy decisions based on the load trend. Specifically, if the load trend shows that the downstream buffer has redundancy and no idle risk, the system preferably performs path segment expansion and reconstruction, that is, by searching for redundant paths to "delay" the arrival time of the automated guided transport vehicle to adapt to processing; conversely, if a strong risk of material shortage is detected, the system calls upon the elastic reserve in the enhanced process baseline data to perform a reduction adjustment of the processing time window, sacrificing some processing elasticity to "reclaim" production time.
[0126] To quantify this decision-making process, the system employs the following heuristic correlation function for the collaborative evaluation of path and time:
[0127] ;
[0128] in, This is a comprehensive evaluation value;
[0129] The reference value for transportation time extracted from transportation corridor data specifically refers to the estimated arrival time of the automated guided vehicle at the target loading and unloading point;
[0130] The processing time reference value is extracted from the corrected processing time window data, specifically referring to the corrected processing start time; This refers to the amount of spatiotemporal overlap.
[0131] As a load risk weight;
[0132] This refers to the sensitivity coefficient dynamically assigned based on the baseline data of the enhancement process.
[0133] During the adjustment process, the system enters continuous monitoring logic to track the overlap deviation value after adjustment in real time. Preferably, within the iteration cycle, the load balancing constraint (to ensure no overflow or idle time) and the spatiotemporal overlap constraint (to ensure precise matching of materials and processing) are continuously backtracked. In one embodiment, the overlap consistency convergence accuracy set by the system is preferably... Seconds. The system finally locks the feasible domain consistency data containing precise timestamps and navigation sequences if and only if all constraints are satisfied.
[0134] By employing the heuristic correlation judgment based on equipment load trends and enhanced process benchmarks, this invention achieves a leap from "single-point alignment" to "global equilibrium." This process deeply binds logistics search costs with machine survival risks, utilizing heuristic decision-making logic to find the optimal balance point under multiple constraints. It effectively solves the problem of extreme load fluctuations that static scheduling cannot handle. Through bidirectional coordination of path and time, it significantly improves the flexibility of logistics turnover and the on-time rate of production tasks in complex production environments, ensuring high-efficiency, low-energy-consumption steady-state operation of the workshop under dynamic disturbances.
[0135] Example 3
[0136] In a specific embodiment of a smart manufacturing workshop, the workshop layout includes three machining centers (MC1, MC2, MC3) and two automated guided vehicles (AGV-001, AGV-002). The system obtains the machining process of workpiece A on MC1, with a planned processing time from the 100th to the 200th second. Simultaneously, AGV-001 receives an instruction to transport the material from warehouse point W (coordinates (0,0)) to loading point MC1 (coordinates (10,0)), with a planned path of P0->P1->P2, and an estimated time occupied in the path segment from the 0th to the 55th second. AGV-002 needs to retrieve the finished product from MC3 (coordinates (5,5)) and transport it to the exit (coordinates (15,5)), with a planned path of P3->P1->P4, and an estimated time occupied in the same path segment P1 from the 30th to the 60th second. Based on this, a joint spatiotemporal state is constructed, including the machine processing time window, the automated guided vehicle path, and the time.
[0137] The system generates transport corridor data for AGV-001, containing a three-dimensional spatial coordinate sequence and a one-dimensional time interval sequence: [(0,0,0)-(10,0,30)], indicating that it moves from (0,0) to (10,0) within 0-30 seconds. Simultaneously, the transport corridor data for AGV-002 is [(5,5,5)-(10,0,35)], indicating that it moves from (5,5) to (10,0) within 5-35 seconds. The system identifies an overlap between the two in the spatiotemporal domain (coordinates ≈ (10,0), time ≈ 30s), constituting a potential spatiotemporal conflict.
[0138] Using the transport corridor of AGV-001 [(0,0,0)-(10,0,30)] as a constraint, the feasible machining region of workpiece A on MC1 is corrected in reverse. Original machining time window. According to the corridor's closing time Time required for the automated guided vehicle to travel to a safe distance and preset safety thresholds The boundary correction increment for the start time is calculated. Considering the current traffic pressure index And with a preset coefficient k=2.0, calculate the additional time cost. According to the corrected formula The corrected processing end time was obtained through similar calculations. This module thus initially modifies the processing feasibility domain of machine MC1 from [100s, 200s] to [208s, 250s].
[0139] The pre-trained confidence assessment model (trained on a dataset containing 10,000 historical scheduling records, using a two-layer LSTM network structure with 128 neurons in the first layer and 64 neurons in the second layer, employing the ReLU activation function, using mean squared error as the loss function, setting the Adam optimizer learning rate to 0.001, training for 500 epochs, and using Dropout=0.2 to prevent overfitting) was used for calculation. The model inputs were the corrected feasible region [208s, 250s], the current workshop dynamics (automated guided vehicle speed, machine status), and task priority (workpiece A has a priority of 8, with a preset high priority threshold of 7), and the output confidence score was 0.75.
[0140] Because the score is higher than the first preset threshold of 0.35 but lower than the second preset threshold of 0.85, the system determines that path replanning is required. The path replanning unit replans a path for the automated guided vehicle-001 based on the updated machine feasible region (MC1 becomes available after 208 seconds). Its goal is to minimize the new path time compared to the original path time. To minimize deviations while avoiding collisions with the automated guided vehicle-002. This is achieved through dynamic heuristics. The algorithm searches for a new path P0->P1->P5->P2, with an estimated time of 58 seconds. Substituting this into the environmental cost calculation formula: (wherein, path change penalty weight) =0.6, traffic conflict cost weight w2=0.4, new route conflict index C_new=0.8, conflict duration D=2 seconds). At this time, the cost J=2.44 is lower than the preset cost threshold of 5.0, and the plan is feasible. The system will automatically guide the transport vehicle-001 to update the route to this new route segment and update the estimated arrival time at MC1 to the 58th second.
[0141] Calculations show that the transport corridors of AGV-001 and AGV-002 do not overlap under the new scheme, thus resolving the conflict. Furthermore, the revised machine processing start time... New arrival time of AGV-001 A 150-second safety buffer is provided, which is much higher than the preset safety threshold. =10s. The confidence model is re-evaluated based on the final complete solution, and the output confidence level is 0.92, which is higher than the second preset threshold of 0.85. The scheduling instruction generation module then generates the final instruction: AGV-001 executes the new path P0->P1->P5->P2; machine MC1 adjusts the processing start time of workpiece A to the 208th second.
[0142] This embodiment demonstrates how the system, in a real-world workshop scenario, begins by detecting spatiotemporal conflicts, then generates transport corridors, reverse-corrects machine feasible domains, intelligently verifies and triggers path replanning, ultimately outputting conflict-free scheduling instructions in a complete closed-loop process. Through this series of collaborative operations, the system successfully resolves path conflicts between automated guided vehicles (AGVs) and ensures that machine processing plans remain efficient and feasible after adjustments. This achieves dynamic and precise matching of logistics and production flows, effectively improving the continuity of equipment utilization and overall production efficiency.
[0143] Example 4
[0144] In one embodiment, such as Figure 2 As shown, a joint scheduling device for machines and automated guided vehicles is provided, the device comprising:
[0145] The status construction module 30 is used to acquire machine processing status data; the machine processing status data includes process task data with original processing start and end times, automated guided vehicle operation status data, and workshop aisle occupancy data.
[0146] The corridor generation module 31 is used to generate initial path data for the corresponding transportation task based on the automatic guided vehicle operation status data and workshop aisle occupancy data, and divide the initial path data into several continuous path segments, match the corresponding running time interval for each path segment, and generate transportation corridor data containing spatiotemporal constraints.
[0147] The data correction module 32 is used to extract the time interval constraints in the transportation corridor data, and calculate the shiftable or compressible range of the original processing start and end times in reverse according to the time interval constraints, and generate corrected processing time window data synchronized with the transportation corridor data.
[0148] The verification and judgment module 33 is used to calculate the overlap deviation value between the corrected processing time window data and the transportation corridor data on the time axis. When the overlap deviation value exceeds the preset safety threshold, the path segment is recalculated or the processing start time is corrected, and iterative verification is performed until the overlap deviation value converges to within the preset safety threshold, and feasible domain consistency data containing execution parameters is obtained.
[0149] The instruction issuing module 34 is used to call the feasible domain consistency data, convert it into collaborative control instructions, and issue them for execution.
[0150] Example 5
[0151] This application also provides an electronic device, in some embodiments, referring to... Figure 3 As shown, the electronic device 700 includes an input unit 710, a memory 720, a processor 730, and an output unit 740. The memory 720 stores program instructions that can be executed on the processor 730. The processor 730 can execute the joint scheduling method and / or technical solution of the machine and automated guided vehicle based on the foregoing embodiments by calling the program instructions. The electronic device 700 can be a mobile terminal device such as a mobile phone or computer.
[0152] Furthermore, embodiments of this application also provide a computer-readable storage medium for storing a computer program that executes a joint scheduling method for a machine and an automated guided vehicle. For example, computer program instructions, when executed by a computer, can invoke or provide the methods and / or technical solutions according to this application through the operation of the computer. The program instructions that invoke the methods of this application may be stored in a fixed or removable storage medium, and / or transmitted via data streams in broadcast or other signal carrying media, and / or stored in a storage medium that operates according to the program instructions.
[0153] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device, or fabricating them separately as individual integrated circuit modules, or fabricating multiple modules or steps as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.
[0154] The technical features of the above embodiments can be arbitrarily integrated. For the sake of brevity, not all possible integrations of the technical features in the above embodiments are described. However, as long as the integration of these technical features does not contradict each other, they should be considered to be within the scope of this specification.
[0155] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.
Claims
1. A method for joint scheduling of machines and automated guided vehicles, characterized in that, The method includes: Acquire machine processing status data; the machine processing status data includes process task data with original processing start and end times, automated guided vehicle operation status data, and workshop aisle occupancy data; The initial path data for the corresponding transportation task is generated based on the automated guided vehicle operation status data and the workshop aisle occupancy data. The initial path data is then divided into several continuous path segments, and a corresponding running time interval is matched for each path segment to generate transportation corridor data containing spatiotemporal constraints. Extract the time interval constraints from the transportation corridor data, and calculate the shiftable or compressible range of the original processing start and end times in reverse based on the time interval constraints, to generate corrected processing time window data synchronized with the transportation corridor data; Calculate the overlap deviation value between the corrected processing time window data and the transportation corridor data on the time axis. When the overlap deviation value exceeds the preset safety threshold, recalculate the path segment or correct the processing start time, and perform iterative verification until the overlap deviation value converges to within the preset safety threshold to obtain feasible domain consistency data containing execution parameters. Recalculate path segments or correct processing start times, including: When recalculating path segments, based on the occupancy frequency and duration of each path segment in the workshop aisle occupancy data, the traffic congestion weight of each physical area is calculated, an environmental cost matrix is constructed, and the start time defined by the modified processing time window data is used as the time starting point. A dynamic heuristic algorithm is used to search for the path with the minimum cost that satisfies the time constraint in the environmental cost matrix to generate updated path segments. Specifically, this includes: extracting the real-time occupancy rate of each path segment in the workshop aisle occupancy data and mapping it to the traffic pressure index of each physical area. According to the formula Update the passage cost G of the path segment, where With the base length cost and k as the sensitivity coefficient, the end boundary of the corrected processing time window data is used as the target time threshold of the heuristic path search algorithm. During the search process, all branches of the path segment that cannot reach the destination within the target time threshold are eliminated in real time. Based on the updated passage cost G, the heuristic path search algorithm is used to search for the path with the minimum cost in the workshop map topology, which is used as the replanned path segment. The replanned path segment is fed back and rematched with the corresponding running time interval until the spatiotemporal consistency comparison is passed. When correcting the processing start time, the task priority identifier associated with the current transportation task is read from the process task data, and the overlap deviation value is mapped to the shift duration of the processing start time. If the task priority identifier is lower than the preset priority threshold, the processing start time is shifted backward according to the shift duration to generate updated corrected processing time window data. If the task priority identifier is higher than or equal to the preset priority threshold, the original processing start time is maintained and the recalculation of the path segment is triggered. The feasible domain consistency data is invoked, transformed into collaborative control instructions, and then issued for execution.
2. The joint scheduling method for machines and automated guided vehicles according to claim 1, characterized in that, The method further includes: Obtain historical completion deviations from the machine processing status data, and generate execution confidence data based on the statistical distribution of the historical completion deviations; Based on the execution confidence data, the processing start and end time elastic step size in the process task data is automatically adjusted to generate enhanced process baseline data containing disturbance allowance.
3. The joint scheduling method for machines and automated guided vehicles according to claim 1, characterized in that, Generate corrected processing time window data synchronized with the transport corridor data, including: Extract the latest departure time of the destination path segment from the transportation corridor data as the transportation hard constraint boundary; The maximum boundary value of the translation range is obtained by performing a difference calculation between the transportation hard constraint boundary and the original processing start and end time. The translational range is reduced based on the maximum boundary value to generate the corrected processing time window data.
4. The joint scheduling method for machines and automated guided vehicles according to claim 2, characterized in that, The method further includes: Extract the task sequence queue length from the machine processing status data, and combine it with the start time of the corrected processing time window data to calculate the probability value of resource occupancy of the equipment within a future preset time. When the resource occupancy probability value is lower than the first preset threshold, idle risk data representing the risk of material shortage is generated; When the resource occupancy probability value is higher than the second preset threshold, congestion risk data representing the risk of buffer overflow is generated; The idle risk data or congestion risk data is encapsulated into device load trend data; Heuristic association judgment is performed based on the equipment load trend data and the enhanced process baseline data, and the judgment result is used as the weight adjustment factor for the adjustment path reconstruction weight.
5. The joint scheduling method for machines and automated guided vehicles according to claim 4, characterized in that, Heuristic association judgment is performed based on the equipment load trend data and the enhanced process baseline data, including: The equipment load trend data is obtained. If the target machine is found to be at risk of being idle, the overlap deviation value of the corresponding transportation task is extracted. The timing consistency of the transport corridor data, the corrected processing time window data, and the equipment load trend data is checked. If it is determined that the corrected processing time window data exceeds the time interval of the transport corridor data, then the expansion and reconstruction of the execution path segment or the reduction and adjustment of the processing time window are selected according to the load trend. The adjusted overlap deviation value is continuously monitored until the load balancing constraint and the spatiotemporal overlap constraint are satisfied, thus obtaining the feasible domain consistency data.
6. The joint scheduling method for machines and automated guided vehicles according to claim 1, characterized in that, The formula for calculating the corrected processing time window data is as follows: ； ； in, and These are the revised processing start time and the revised processing end time, respectively. and These are the original processing start time and original processing end time extracted from the process task data, respectively; and This is the boundary correction increment calculated based on the transport corridor data. .
7. The joint scheduling method for machines and automated guided vehicles according to claim 1, characterized in that, After invoking the feasible domain consistency data, converting it into collaborative control instructions, and issuing them, the method further includes: The current remaining battery power in the operating status data of the automated guided vehicle is obtained, and the unit energy consumption curves of each path segment at different speed levels are retrieved to generate the energy consumption cost field of the automated guided vehicle. Under the constraint of convergence of the overlap deviation value, the time redundancy of each path segment in the transportation corridor data is extracted, and the optimal speed command value that minimizes the total energy consumption is searched in the energy consumption cost field of the automated guided vehicle. The optimal speed command value is injected as an execution parameter into the cooperative control command.
8. A joint scheduling device for machines and automated guided vehicles, characterized in that, The device includes: The status construction module is used to acquire machine processing status data; the machine processing status data includes process task data with original processing start and end times, automated guided vehicle operation status data, and workshop aisle occupancy data. The corridor generation module is used to generate initial path data for the corresponding transportation task based on the automatic guided vehicle operation status data and the workshop aisle occupancy data, and to divide the initial path data into several continuous path segments, match the corresponding running time interval for each path segment, and generate transportation corridor data containing spatiotemporal constraints. The data correction module is used to extract the time interval constraints in the transportation corridor data, and calculate the shiftable or compressible range of the original processing start and end times in reverse according to the time interval constraints, and generate corrected processing time window data synchronized with the transportation corridor data. The verification and judgment module is used to calculate the overlap deviation value between the corrected processing time window data and the transportation corridor data on the time axis. When the overlap deviation value exceeds the preset safety threshold, the path segment or the corrected processing start time is recalculated and iterative verification is performed until the overlap deviation value converges to within the preset safety threshold, and feasible domain consistency data containing execution parameters is obtained. The instruction issuance module is used to call the feasible domain consistency data, convert it into collaborative control instructions, and issue them for execution. The joint scheduling device for machines and automated guided vehicles is used to implement the joint scheduling method for machines and automated guided vehicles as described in any one of claims 1 to 7.