Intelligent scheduling method and system for graphitization process of new energy battery material

Abstract

The application provides an intelligent scheduling method and system for a graphitization process of new energy battery materials. The method comprises the following steps: obtaining the operation parameters of a graphitization furnace body, calculating the current reaction intensity and the predicted carbon redundancy capability of the furnace body in the future period; according to the current reaction intensity and the predicted carbon redundancy capability of the furnace body in the future period, calculating a composite scheduling safety index to judge the schedulability of a logistics task, screening out a schedulable task set and determining the scheduling time window of each task; obtaining a path map deployed in the current factory area, and calculating the priority score of the execution of each task at the current time; based on the priority score, the tasks are sorted and sequentially scheduled to ensure the allocation within the scheduling time window; in combination with the optimized task scheduling sequence, the transportation unit of the task allocation and the scheduling safety label, a structured scheduling instruction is generated and is delivered to an execution system. The application improves the carbon efficiency and the intelligent level of the graphitization production system.

Description

Technical Field

[0001] This invention belongs to the technical field of graphitization process of new energy battery materials, and particularly relates to intelligent scheduling method and system for graphitization process of new energy battery materials. Background Technology

[0002] In the preparation of new energy battery materials, graphitization is the core process for transforming carbonaceous materials into high-performance graphite materials. Its production characteristics include high temperature, high energy consumption, and long cycle time, making it the stage with the most concentrated carbon emissions in the anode material production chain. Currently, the industry generally adopts a multi-furnace parallel graphitization production method, but the operating states of each furnace vary significantly, especially at high temperatures, where carbon load fluctuates drastically and forms local peaks, leading to an imbalance between overall energy consumption and carbon emissions, severely affecting the stability of the production rhythm. Due to differences in raw materials, charge density, and process parameters between different batches, the reaction intensity varies between furnaces, making it impossible to match the timing and path scheduling of logistics tasks with the reaction process in real time, often resulting in task backlog, path conflicts, and superimposed energy consumption. Existing logistics scheduling methods are mostly based on static rules or manually set cycles, lacking the ability to perceive the furnace operating status and lacking a mechanism to predict carbon emission trends, thus failing to achieve dynamic scheduling that matches the reaction intensity. Although some research has attempted to introduce energy consumption detection or carbon emission estimation models, these solutions mostly remain at the monitoring level and have failed to establish a two-way constraint relationship between carbon load status and logistics behavior.

[0003] Meanwhile, the graphitization process has strong physical coupling characteristics. The operation of the furnace body and the logistics transportation interact in time and space. Moreover, the complex industrial environment and problems such as signal delay, sensor interference and path resource occupation make real-time control of the system more difficult.

[0004] In summary, the current lack of a dynamic and coordinated scheduling mechanism that combines changes in reaction intensity, carbon emission sensing, and path resource constraints means that the graphitization process still relies on manual experience or fixed strategies for carbon load regulation and logistics optimization, making it difficult to meet the requirements of the new energy battery materials industry for low-carbon and intelligent production. Summary of the Invention

[0005] The purpose of this invention is to propose an intelligent scheduling method and system for the graphitization process of new energy battery materials, so as to solve the above-mentioned problems.

[0006] To achieve the above objectives, a first aspect of the present invention provides an intelligent scheduling method for the graphitization process of new energy battery materials, comprising the following steps:

[0007] S1. Obtain the operating parameters of the graphitization furnace, including furnace temperature, furnace current, furnace voltage, and charging density, in order to calculate the current reaction intensity and predict the carbon redundancy capacity of the furnace in the future.

[0008] S2. Based on the current reaction intensity and the predicted carbon redundancy capacity of the furnace body in the future, calculate the composite scheduling safety index to determine the schedulability of logistics tasks, screen out the set of schedulable tasks, and determine the scheduling time window for each task; wherein, the scheduling time window includes the task start time and the latest time limit.

[0009] S3. Obtain the current deployment path map of the factory area, and calculate the priority score of each task to be executed at the current time based on the set of schedulable tasks; sort the tasks based on the priority scores and schedule them in sequence to ensure that they are allocated within the scheduling time window.

[0010] If the current task is not executable, a delayed execution time is searched in the window until the latest time limit is reached; otherwise, the task will be rolled back into the waiting pool, resulting in an optimized task scheduling sequence and an updated path capacity status. Each task scheduling vector in the optimized task scheduling sequence includes the current task, the delayed execution time, and the feasible path of the current task.

[0011] S4. Combining the optimized task scheduling sequence, the transportation unit of the task allocation, and the scheduling security label, generate a structured scheduling instruction and send it to the execution system.

[0012] Furthermore, the current reaction intensity is obtained by weighted summation of furnace temperature, furnace current, and furnace voltage.

[0013] Furthermore, the predicted carbon redundancy capacity of the furnace body in future time periods is calculated as follows:

[0014] The current reaction intensity is obtained, and the carbon flux of the furnace body at the current time is calculated in combination with the charge density;

[0015] Based on the carbon flux of the furnace body at the current time, the future carbon emission trend value is fitted by extrapolation using a linear fitting method with a sliding window of fixed step size.

[0016] Based on the future carbon emission trend value and the maximum allowable carbon emission flux of the furnace body per unit time, the predicted carbon redundancy capacity of the furnace body in the future period is obtained. The predicted carbon redundancy capacity of the furnace body in the future period is used to measure whether the current furnace body has a scheduling window in the future period. The larger the value, the higher the carbon emission release space of the furnace body.

[0017] Furthermore, S2 specifically includes:

[0018] The target furnace number, planned execution time, task type, initial scheduling priority, disturbance coefficient, and reaction threshold of the current task are obtained. Combined with the current reaction intensity of the target furnace number and the predicted carbon redundancy capability of the furnace body in the future period, a composite scheduling safety index is calculated and generated.

[0019] If the composite scheduling security index is greater than or equal to a preset threshold, it is selected as a set of schedulable tasks.

[0020] Furthermore, the higher the value of the composite scheduling safety index, the more feasible the task is in the current state.

[0021] Furthermore, the priority score is generated as follows:

[0022] Obtain the current deployment path map of the factory area, where each edge represents a passable path in the factory area, with the maximum passable capacity and the current occupancy status;

[0023] Based on the aforementioned path map, the priority score of each task in the schedulable task set is evaluated by combining the latest time limit, the maximum capacity limit, and the current occupancy status, and by introducing the carbon exposure intensity of the path segment.

[0024] Furthermore, after the tasks are sorted and scheduled sequentially based on the priority score, the path allocation adopts a variant of the A* algorithm, and the heuristic function combines path distance and carbon risk index; wherein, the carbon risk index is generated based on path segment length and carbon exposure intensity of path segment.

[0025] Furthermore, the scheduling safety label is calculated and generated based on the reaction intensity of the delayed execution time and the carbon exposure intensity of the path;

[0026] The scheduling safety labels include: SAFE, CAUTION, and BLOCK.

[0027] Furthermore, the step of sending the data to the execution system specifically includes:

[0028] After the structured scheduling instructions shown are generated, they are sent to the actual scheduling execution equipment through the central control system interface. For AGV systems, the instructions are packaged and sent through Modbus, CANopen or industrial Ethernet protocols. For manual task scheduling systems, the instructions are prompted to the operator through the HMI interface.

[0029] In a second aspect, the present invention provides an intelligent scheduling system for the graphitization process of new energy battery materials, the system comprising:

[0030] The carbon sensing and prediction module is used to acquire the operating parameters of the graphitization furnace, including furnace temperature, furnace current, furnace voltage and charge density, in order to calculate the current reaction intensity and predict the carbon redundancy capacity of the furnace in the future.

[0031] The task scheduling window judgment module is used to calculate a composite scheduling safety index based on the current reaction intensity and the predicted carbon redundancy capacity of the furnace body in the future period to judge the schedulability of logistics tasks, filter out a set of schedulable tasks, and determine the scheduling time window for each task; wherein, the scheduling time window includes the task start time and the latest time limit.

[0032] The path constraint optimization module is used to obtain the current deployment path map of the plant area, calculate the priority score of each task to be executed at the current time based on the set of schedulable tasks, sort the tasks based on the priority scores and schedule them in sequence to ensure that they are allocated within the scheduling time window.

[0033] If the current task is not executable, a delayed execution time is searched in the window until the latest time limit is reached; otherwise, the task will be rolled back into the waiting pool, resulting in an optimized task scheduling sequence and an updated path capacity status. Each task scheduling vector in the optimized task scheduling sequence includes the current task, the delayed execution time, and the feasible path of the current task.

[0034] The scheduling instruction generation and execution module is used to combine the optimized task scheduling sequence, the transportation unit of the task allocation, and the scheduling security tag to generate structured scheduling instructions and send them to the execution system.

[0035] The beneficial technical effects of the present invention are at least as follows:

[0036] This invention constructs a carbon flux model based on reaction intensity and a carbon redundancy prediction method to reflect the carbon emission status and carrying capacity of each furnace in real time, and uses this as a basis to dynamically determine the schedulability of multi-source logistics tasks. The system further combines path resource constraints and carbon disturbance risk factors to design a multi-objective optimization mechanism that integrates carbon exposure sensitivity and task urgency, generating scheduling sequences and path allocation schemes under carbon emission constraints. Finally, by introducing structured scheduling instructions and a safety tagging system, risk identification and dynamic path adjustment at the task execution level are achieved, forming a closed-loop control throughout the entire process from carbon load perception, reaction intensity modeling, task selection, path optimization to system execution. This invention, through the deep coupling of reaction intensity and logistics behavior, enables logistics tasks to automatically avoid high-carbon load areas spatially and execute in accordance with carbon redundancy periods temporally, thereby effectively reducing carbon peak superposition, balancing the load of multi-furnace collaborative operation, and improving the carbon efficiency and intelligence level of the graphitization production system. It provides a reproducible, deployable, and practically effective intelligent scheduling solution for the new energy battery materials industry. Attached Figure Description

[0037] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the steps of the intelligent scheduling method for the graphitization process of new energy battery materials according to the present invention. Detailed Implementation

[0039] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0040] In one or more embodiments, such as Figure 1 As shown, an intelligent scheduling method for the graphitization process of new energy battery materials is disclosed. The method includes the following steps:

[0041] S1. Obtain the operating parameters of the graphitization furnace, including furnace temperature, furnace current, furnace voltage, and charge density, in order to calculate the current reaction intensity and predict the carbon redundancy capacity of the furnace in the future.

[0042] Specifically, in the graphitization process, the furnace releases a large amount of carbon load during the high-temperature stage. The release rate is controlled by the combined effect of multiple operating parameters, exhibiting significant temporal dynamics and inter-furnace variations. To achieve coordinated control of subsequent logistics scheduling and carbon emission status, the primary prerequisite is to establish a carbon flux estimation mechanism that closely aligns with the physical reaction process. Based on this, future carbon emission trends can be predicted, thereby obtaining the carbon redundancy capacity of each furnace. This step integrates furnace operating parameters and loading status to construct a reaction intensity-driven carbon flux estimation model. Combined with a short-cycle trend extrapolation mechanism, it obtains the carbon emission redundancy capacity per unit time, providing a definable basic input for task scheduling.

[0043] The data acquisition focuses on the following parameters that can be obtained on-site: furnace temperature. Data is collected via a three-point K-type thermocouple array installed at the top, side walls, and feed end of the furnace, with a sampling period of 10 seconds. The data is input to the control system in floating-point format; furnace body current. The furnace body voltage is measured by a Hall current sensor on the main electrode circuit, connected to the PLC module, and transmitted via a Modbus interface. Read the voltage measurement module (which shares an AD sampling card with the current channel); charge density. The total charge mass is obtained through a weighing system before raw material loading, and the average density is calculated based on the preset volume parameters of the furnace cavity. For example, if the charge mass is 2400 kg and the furnace cavity volume is 3.2 m³, then... Furnace body number Automatically injected when the industrial control system assigns tasks, requiring no manual annotation. Upper limit of process constraints. The maximum permissible carbon flux per unit time is represented by data from the design manual or historical carbon emission monitoring. Furnace temperature, current, and voltage are considered the three main factors constituting the reaction intensity. Due to their different dimensions and response rates, they must first be standardized and combined to form a single numerical index. In practice, the reaction intensity is constructed in the following form:

[0044]

[0045] in, Indicates furnace body In time The reaction intensity is used to characterize the activity level of the reaction process per unit time. , , Standardization coefficients are set according to the principle of keeping each term within the same order of magnitude to avoid any one variable dominating the overall response. For example... , , ; , , Representing the furnace body At any moment Temperature, current, and voltage.

[0046] Considering that carbon emissions in the furnace are proportional to the unit reaction intensity and are also modulated by the unit charge density, the carbon flux is constructed as follows in the actual modeling:

[0047]

[0048] in, Indicates furnace body In time carbon flux, Indicates furnace body The density of the material charged into the furnace, This is an empirical parameter adjustment coefficient, determined by fitting historical furnace carbon emission intensity and reaction indicators. Its function is to unify the actual carbon emission intensity under a unit reaction intensity.

[0049] To achieve advance awareness of the scheduling window, it is necessary to predict the carbon emission intensity trend in the short term. The system uses a sliding window linear fitting method with a fixed step size to... Extrapolate using a window size of the most recent 5 minutes (30 sampling points) to fit the trend value. ,in The default prediction point is 10 minutes. This method runs stably in resource-constrained deployment environments, with prediction accuracy controlled within 10%.

[0050] The resulting future carbon flux forecast is compared with the upper limit threshold to calculate future carbon redundancy capacity.

[0051]

[0052] in, Indicates furnace body In time Carbon redundancy capability at any time Indicates furnace body The maximum permissible carbon emission flux per unit time is determined by process design standards. This represents the future carbon emission trend value obtained through forecasting. This indicator is used to measure whether the current furnace body has a scheduling window in the future; the higher the value, the higher the carbon emission release capacity of the furnace body.

[0053] S2. Based on the current reaction intensity and the predicted carbon redundancy capacity of the furnace body in the future, calculate the composite scheduling safety index to determine the schedulability of logistics tasks, screen out the set of schedulable tasks, and determine the scheduling time window for each task; wherein, the scheduling time window includes the task start time and the latest time limit.

[0054] Specifically, logistics tasks in the graphitization process (such as loading, unloading, and material car transfer) significantly interfere with the furnace reaction process. Blindly implementing scheduling during peak carbon load periods or periods of sudden reaction intensity increases can lead to a surge in carbon emissions, thermal load imbalance, and even structural safety risks to the furnace. Therefore, the scheduling system must possess the ability to pre-assess the feasibility of task execution, especially given the complex context of dynamic changes in carbon load and nonlinear fluctuations in the reaction process. It must simultaneously consider two core input factors: one is the furnace's carbon redundancy capacity from the previous step. Secondly, the state of reaction intensity. These two factors respectively reflect the current furnace body's "carbon carrying capacity" and "reaction disturbance sensitivity," and neither can be omitted.

[0055] Furthermore, graphitization logistics tasks exhibit high heterogeneity: unloading tasks are highly volatile, infeeding tasks have a high risk of delay, and transshipment tasks have long but relatively mild paths. Applying the same judgment logic to all task types would lead to system scheduling instability. Therefore, task characteristic parameters must be introduced and embedded into the decision model. Based on these two system characteristics, this step designs a scheduling feasibility judgment mechanism that integrates task disturbance coefficients and reaction state modulation factors, and introduces a suppression factor for "carbon risk mutations" to further enhance the model's adaptability to scenario specificity and safety control.

[0056] Furthermore, the task input information comes from the scheduling queue of the MES system, and each task... Including: target furnace number Planned execution time Task type (encoded as a category variable) and initial scheduling priority Meanwhile, the perturbation coefficients for each type of task are obtained through training on historical operating conditions. and reaction critical value These two values ​​represent the intensity of the unit-executed disturbance and the maximum tolerable reactive activity, respectively. For example, the furnace exit task... It can be set to 160. It can be set to 1200.

[0057] To establish a system-level computable scheduling decision-making mechanism, the following composite scheduling safety index is constructed. :

[0058]

[0059] in, For target furnace number The carbon redundancy capacity of the furnace body in the future. For target furnace number The current reaction intensity;

[0060] Indicates task At the present moment The safety schedulability score is used to determine whether the task can be executed under the current furnace state. The higher the value, the lower the scheduling risk. and These are the weighting coefficients for the two main factors, reflecting the degree of importance the system places on carbon load and reaction intensity factors in its safety assessment. Indicates the target furnace body of the mission At any moment The carbon redundancy capacity, which is calculated by the carbon flux prediction model in step one, represents the remaining carbon emission space that the furnace body can withstand in the near future. It is a task The disturbance intensity coefficient, obtained from the MES task library or historical execution data, is used to characterize the expected impact of task execution on furnace carbon emissions. For example, the disturbance of the furnace feeding operation is relatively small, while the disturbance intensity of the furnace discharging operation is relatively large. Furnace body At any moment The reaction intensity is calculated by linearly combining real-time data from temperature, current, and voltage sensors to measure the current reaction rate and thermal activity. For the task The maximum acceptable response intensity threshold is set based on historical task execution experience. Exceeding this value will lead to an increase in system risk when task execution is performed.

[0061] The formula consists of three parts: the first term measures the degree of matching between the furnace carbon redundancy capacity and the intensity of task disturbance; a larger value indicates greater safety. The second term measures the distance between the current reaction intensity and the task's tolerance limit; a larger value indicates greater safety. The third term is the introduced "reaction intensity change rate term," which measures whether the reaction state is currently in a phase of drastic fluctuation, even if the current... Within the safe zone, however, a sudden and sharp increase may indicate that the peak threshold is approaching, and task triggering should be suppressed, hence the negative sign penalty. and These are the weighting factors for the two indicators, which can be set as follows: , Emphasizing carbon space priority; The regularization penalty coefficient is used for specific design of risk control in sections with severe graphitization reactions; a value between 0.1 and 0.3 is recommended.

[0062] in, It is a dimensionless scoring index, and its range is usually within Between these values, a higher value indicates that the task is more feasible in the current state. For practical use, a judgment threshold is set. ,when Time-based tasks are schedulable; recommended value. The set of schedulable tasks is as follows:

[0063]

[0064] For tasks that have been determined to be adjustable, in order to support the subsequent sorting optimization module in selecting strategies for the scheduling window, it is also necessary to define the earliest and latest executable times for each task. It can be set to the later of the current system time or the scheduled time. The decreasing trend of furnace carbon redundancy and the perturbation matching coefficient need to be considered. For example, if the carbon redundancy capacity has shown a linear decreasing trend over the past two minutes... ,current ,and The acceptable window is approximately Minutes, final This type of window construction logic facilitates the scheduler's flexible task allocation based on system status.

[0065] The output includes the following two items: (1) a set of schedulable tasks (1) Only tasks that can be executed under the three constraints of sufficient carbon redundancy, safe reaction intensity, and stable reaction state; (2) The scheduling time window for each task. , The task start time. As the latest time limit, this interval will become the time boundary for the scheduler's sorting and resource allocation.

[0066] S3. Obtain the current deployment path map of the factory area, and calculate the priority score of each task to be executed at the current time based on the set of schedulable tasks; sort the tasks based on the priority scores and schedule them in sequence to ensure that they are allocated within the scheduling time window.

[0067] Specifically, graphitization furnaces exhibit typical multi-furnace parallel operation characteristics. The peak carbon emission times of each furnace are not synchronized, and transportation tasks such as feeding and unloading are characterized by significant concentration and suddenness. This leads to frequent instances of multiple tasks competing for the same path resources within similar timeframes, easily causing path conflicts and backlogs, resulting in an imbalance in scheduling rhythm. In the first two steps, the system has already considered the carbon redundancy capacity of each furnace. and reaction strength Together, determine the set of tasks that are currently safe to execute. and for each task Provides scheduling time window At this point, the task has already been filtered at the "physical constraint" level.

[0068] However, in actual scheduling, mere "executability" is far from sufficient. More crucially, among multiple executable tasks, how to coordinate the scheduling order and transportation routes to ensure optimal overall transportation efficiency under limited path resources, and further avoid the formation of logistics interference zones around high-reaction-intensity furnaces. Traditional sorting methods, such as Earliest Deadline First (EDF), cannot perceive path status, and path selection methods, such as the shortest path strategy, do not consider the impact of carbon load, making them unsuitable for the scenario of this invention. Therefore, this step proposes a multi-objective scheduling priority model that integrates carbon perception, path congestion level, task urgency, and carbon disturbance safety factors. Combined with a dynamic resource update mechanism in the path graph, it generates an optimized scheduling sequence and path allocation scheme.

[0069] This step's input includes two variables, both from the previous stage: the task set. Each task Includes task type (feeding, unloading) and target furnace body. Scheduling time window and reaction intensity limit The other input is a path graph. Each edge This indicates a passable path within the factory area, with a maximum passable capacity. Current occupancy status Among them, the path diagram It is a structured abstract representation of the logistics network in a graphitization workshop, where the set of nodes... This indicates key logistics locations within the factory area, such as raw material storage areas, loading stations, entrances to each graphitization furnace, furnace outlet buffer areas, and transfer stations; (Side group) Each edge represents a walkable path between any two nodes. This corresponds to a specific passageway within the factory area (such as an AGV travel lane or a forklift transport corridor). Each edge has a fixed maximum passage capacity. This indicates the maximum number of transport units or traffic flow allowed to pass through this route segment per unit time, typically determined by route width, lane type, and safety distance specifications. It represents the dynamic occupancy status of the route. This data is collected in real-time by the factory's logistics monitoring system. The system uses positioning modules (such as UWB tags or LiDAR positioning systems) deployed on AGVs or forklifts to calculate the instantaneous occupancy of each path segment, thus reflecting the current utilization of path resources. Approaching or equal to When this occurs, it indicates that the path segment is congested, and new tasks will be automatically avoided or delayed during the scheduling process.

[0070] In addition, a borderline "carbon exposure factor" is introduced. This is used to represent the average reaction intensity level of the furnace body in the vicinity of the path segment, and is determined by the nodes at both ends of the path adjacent to the furnace body. The average value is obtained, forming a dynamic path sensitivity.

[0071] Furthermore, for each task It is necessary to assess its current time. Priority scoring during execution Taking into account the following factors: (1) urgency of the task; (2) feasibility of the path (the path is a specific feasible route selected on the graph); (3) carbon disturbance risk of the path; (4) current state of the furnace body and changes in historical load intensity, the following model is constructed:

[0072]

[0073] This formula uses only one main formula, has a controlled number of variables, and embeds path state regularization terms that have engineering significance, as explained below:

[0074] The first item indicates the urgency of the task. The smaller the value, the closer the task is to the latest schedulable time, and the higher the priority.

[0075] The second item is a dual penalty for path resources and carbon disturbance, path For the task The feasible path (obtained through a path lookup algorithm). Represents path segment Current occupancy level Indicates the carbon exposure intensity of the path segment;

[0076] Among them, path It is a complete transportation route, used to describe the overall trajectory of the mission from the starting point to the target furnace body; while path segments It is the basic unit that constitutes the path, representing a passable passage between any two adjacent nodes in the factory area path diagram.

[0077] Regularity coefficient The weight representing the path carbon perturbation constraint is generally recommended to be set between 0.2 and 0.4, depending on the sensitivity of the graphitization system to reaction perturbations;

[0078] coefficient and For example, setting a weighting term to balance the task time constraint and path risk constraint. , This indicates that the time window is the primary sorting criterion.

[0079] The scoring function possesses clear engineering interpretability; all variables are directly obtainable or computationally quantifiable indicators from the current system state, and no unverifiable parameters or abstract model structures are introduced, ensuring feasibility in actual deployment. The model's innovation lies not only in ranking paths based on congestion levels but also, for the first time, incorporating a "path carbon exposure factor" as a dynamic penalty in path selection during task scheduling. In graphitization-sensitive carbon disturbance scenarios, this effectively prevents transport flows from traversing high-reactivity regions, thereby reducing the risk of local carbon accumulation.

[0080] After tasks are sorted, the scheduler attempts to allocate paths and resources to each task sequentially, ensuring that they are within the scheduling window. Allocate resources as early as possible. If execution is currently unavailable due to path occupancy or resource conflicts, attempt to find a delayed execution time within the window. Until the latest deadline is reached. Otherwise, the task will be rolled back into the waiting pool. Path allocation uses a variant of the A* algorithm, and the heuristic function considers both path distance and carbon risk indicators. ,in Indicates the length of the path segment. This is a length-weighted factor. If there are no conflicts, the scheduled execution time will proceed directly. When a task instruction is issued, the output is the execution time and path allocation scheme determined in the task scheduling sequence. The task is immediately marked as "executable" and enters the actual execution phase.

[0081] The final output includes: the optimized task scheduling sequence. Satisfying window constraints

[0082] S4. Combining the optimized task scheduling sequence, the transportation unit of the task allocation, and the scheduling security label, generate a structured scheduling instruction and send it to the execution system.

[0083] Specifically, after completing task sorting and path optimization, the scheduling system generates a task scheduling sequence. ,in Number the task. It is the task's delayed execution time. This is the path from the scheduling origin to the target furnace, i.e., the feasible path for the current task. Each path... It contains a set of ordered nodes, representing logistics access points or key control points within the factory area. All paths originate from the path map deployed within the factory area. Its structure is fixed during system initialization. The path diagram is preset by the PLC or MES system and is coded into identifiable node numbers using the factory's floor plan, such as the nodes in the AGV control system. This likely represents the west entrance of Boiler No. 7. In practical applications, scheduling instructions need to be converted into structured control messages with execution logic, which are then issued by the scheduling system to the AGV task scheduling platform or manual scheduling terminal to achieve the physical execution of the actual task. To ensure that the instructions have system recognition capabilities and execution stability, the instruction structure should include five key fields: task number, allocated resources, path structure, planned time, and task safety tag. These fields together constitute the scheduling instruction. The format is as follows:

[0084]

[0085] in This is a unique number within the task system, generated by the task management system according to the order of task creation. For example, the "baking out" task is set to EX205. The transportation unit assigned to the task is usually the AGV number, which is dynamically allocated by the system through the resource pool. The AGV number represents the identifier of the transportation unit selected by the system to execute the task. When generating scheduling instructions, the system will automatically select the AGV that is in normal condition and closest to the task start point from the currently idle transportation equipment and write its number into the instruction. The task path is a sequence of nodes generated by the scheduling system, such as... ; The planned execution time is the time assignment result from the scheduling and sorting phase; This is a scheduling safety label used to mark carbon load-sensitive states that may be triggered during task execution. All fields can be directly generated by the scheduling control system without secondary calculations.

[0086] Furthermore, in the key fields This is the core innovation in the design of this step, and its construction logic comes from two key indicators in the previous steps: reaction intensity. and path carbon exposure intensity Reaction strength The data is derived from multi-sensor fusion of furnace body current, voltage, and temperature from the furnace control system. Specifically, this is achieved through temperature acquisition (thermocouples), current transformers, and voltage acquisition modules. All data is refreshed every 10 seconds and transmitted to the scheduling platform in real-time via edge computing devices. (Path carbon exposure intensity) This is a dynamic index calculated by the system during path planning. Its value comes from the average current reaction intensity of the furnaces adjacent to the path segment, and is sampled by the system. All adjacent furnace bodies The average is obtained, for example, if the path segment Adjacent to Furnaces No. 3 and No. 5, its can be Provided.

[0087] Based on these two inputs, the system calculates the security label for the task using the following logic. :

[0088]

[0089] in and The carbon load control threshold is derived from system settings. For example... This indicates that the system can be considered to be operating stably when the reaction intensity is less than 1000. This represents the critical point of system carbon load overload, requiring immediate blocking of scheduling behavior.

[0090] This safety tag will be directly transmitted to the execution system in the scheduling instructions, guiding the execution system's strategy selection. For example, after receiving a task instruction tagged "SAFE", the AGV central control system will execute it according to the normal path and speed; if it receives the "CAUTION" tag, it will activate the low-speed mode and increase the obstacle avoidance frequency of path nodes; if it receives the "BLOCK" tag, the system will not execute the task immediately, but will enter the high-risk task buffer, waiting for the next round of system status refresh to reassess whether it can be issued.

[0091] After structured scheduling instructions are generated, they are sent to the actual scheduling execution equipment via the central control system interface. For AGV systems, instructions are packaged and sent via Modbus, CANopen, or industrial Ethernet protocols; for manual task scheduling systems, instructions are presented to the operator via the HMI interface. A time field is attached to the instruction. It can be set to be triggered by a countdown, or it can be automatically aligned with the device system's internal scheduling table; path field The AGV path planning module will be deconstructed into a specific sequence of driving instructions; safety label field The local security controller then parses and loads the corresponding runtime policy template.

[0092] In one or more embodiments, another embodiment of the present invention provides an intelligent scheduling system for the graphitization process of new energy battery materials, the system comprising:

[0093] The carbon sensing and prediction module is used to acquire the operating parameters of the graphitization furnace, including furnace temperature, furnace current, furnace voltage and charge density, in order to calculate the current reaction intensity and predict the carbon redundancy capacity of the furnace in the future.

[0094] The task scheduling window judgment module is used to calculate a composite scheduling safety index based on the current reaction intensity and the predicted carbon redundancy capacity of the furnace body in the future period to judge the schedulability of logistics tasks, filter out a set of schedulable tasks, and determine the scheduling time window for each task; wherein, the scheduling time window includes the task start time and the latest time limit.

[0095] The path constraint optimization module is used to obtain the current deployment path map of the plant area, calculate the priority score of each task to be executed at the current time based on the set of schedulable tasks, sort the tasks based on the priority scores and schedule them in sequence to ensure that they are allocated within the scheduling time window.

[0096] If the current task is not executable, a delayed execution time is searched in the window until the latest time limit is reached; otherwise, the task will be rolled back into the waiting pool, resulting in an optimized task scheduling sequence and an updated path capacity status. Each task scheduling vector in the optimized task scheduling sequence includes the current task, the delayed execution time, and the feasible path of the current task.

[0097] The scheduling instruction generation and execution module is used to combine the optimized task scheduling sequence, the transportation unit of the task allocation, and the scheduling security tag to generate structured scheduling instructions and send them to the execution system.

[0098] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0099] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0100] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0101] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein can be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0102] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0103] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0104] The prior description of this disclosure is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the examples and designs described herein, but should be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent scheduling method for the graphitization process of new energy battery materials, characterized in that, Includes the following steps: S1. Obtain the operating parameters of the graphitization furnace, including furnace temperature, furnace current, furnace voltage, and charging density, in order to calculate the current reaction intensity and predict the carbon redundancy capacity of the furnace in the future. S2. Based on the current reaction intensity and the predicted carbon redundancy capacity of the furnace body in the future, calculate the composite scheduling safety index to determine the schedulability of logistics tasks, screen out the set of schedulable tasks, and determine the scheduling time window for each task; wherein, the scheduling time window includes the task start time and the latest time limit. S3. Obtain the current deployment path map of the factory area, and calculate the priority score of each task to be executed at the current time based on the set of schedulable tasks; sort the tasks based on the priority scores and schedule them in sequence to ensure that they are allocated within the scheduling time window. If the current task is not executable, a delayed execution time is searched in the window until the latest time limit is reached; otherwise, the task will be rolled back into the waiting pool, resulting in an optimized task scheduling sequence and an updated path capacity status. Each task scheduling vector in the optimized task scheduling sequence includes the current task, the delayed execution time, and the feasible path of the current task. S4. Combining the optimized task scheduling sequence, the transportation unit assigned to the task, and the scheduling security tag, generate a structured scheduling instruction and send it to the execution system; The predicted carbon redundancy capacity of the furnace body in the future is calculated as follows: The current reaction intensity is obtained, and the carbon flux of the furnace body at the current time is calculated in combination with the charge density; Based on the carbon flux of the furnace body at the current time, the future carbon emission trend value is fitted by extrapolation using a linear fitting method with a sliding window of fixed step size. Based on the future carbon emission trend value and the maximum allowable carbon emission flux of the furnace body per unit time, the predicted carbon redundancy capacity of the furnace body in the future period is obtained. The predicted carbon redundancy capacity of the furnace body in the future period is used to measure whether the current furnace body has a scheduling window in the future period. The priority score is generated as follows: Obtain the current deployment path map of the factory area, where each edge represents a passable path in the factory area, with the maximum passable capacity and the current occupancy status; Based on the aforementioned path map, the latest time limit, the maximum capacity limit, and the current occupancy status are combined, and the carbon exposure intensity of the path segment is introduced to evaluate the priority score of each task in the schedulable task set. After tasks are sorted and scheduled sequentially based on the priority score, path allocation adopts a variant of the A* algorithm, and the heuristic function combines path distance and carbon risk index; wherein, the carbon risk index is generated based on path segment length and carbon exposure intensity of path segment.

2. The intelligent scheduling method for the graphitization process of new energy battery materials according to claim 1, characterized in that, The current reaction intensity is obtained by weighted summation of furnace temperature, furnace current, and furnace voltage.

3. The intelligent scheduling method for the graphitization process of new energy battery materials according to claim 1, characterized in that, S2 specifically includes: The target furnace number, planned execution time, task type, initial scheduling priority, disturbance coefficient, and reaction threshold of the current task are obtained. Combined with the current reaction intensity of the target furnace number and the predicted carbon redundancy capability of the furnace body in the future period, a composite scheduling safety index is calculated and generated. If the composite scheduling security index is greater than or equal to a preset threshold, it is selected as a set of schedulable tasks.

4. The intelligent scheduling method for the graphitization process of new energy battery materials according to claim 3, characterized in that, The higher the value of the composite scheduling safety index, the more feasible the task is in the current state.

5. The intelligent scheduling method for the graphitization process of new energy battery materials according to claim 1, characterized in that, The scheduling safety label is calculated and generated based on the reaction intensity of the delayed execution time and the carbon exposure intensity of the path. The scheduling safety labels include: SAFE, CAUTION, and BLOCK.

6. The intelligent scheduling method for the graphitization process of new energy battery materials according to claim 1, characterized in that, The process of sending the data to the execution system specifically includes: After the structured scheduling instructions shown are generated, they are sent to the actual scheduling execution equipment through the central control system interface. For AGV systems, the instructions are packaged and sent through Modbus, CANopen or industrial Ethernet protocols. For manual task scheduling systems, the instructions are prompted to the operator through the HMI interface.

7. A system for implementing the intelligent scheduling method for the graphitization process of new energy battery materials as described in claim 1, characterized in that, The system includes: The carbon sensing and prediction module is used to acquire the operating parameters of the graphitization furnace, including furnace temperature, furnace current, furnace voltage and charge density, in order to calculate the current reaction intensity and predict the carbon redundancy capacity of the furnace in the future. The task scheduling window judgment module is used to calculate a composite scheduling safety index based on the current reaction intensity and the predicted carbon redundancy capacity of the furnace body in the future period to judge the schedulability of logistics tasks, filter out a set of schedulable tasks, and determine the scheduling time window for each task; wherein, the scheduling time window includes the task start time and the latest time limit. The path constraint optimization module is used to obtain the current deployment path map of the plant area, calculate the priority score of each task to be executed at the current time based on the set of schedulable tasks, sort the tasks based on the priority scores and schedule them in sequence to ensure that they are allocated within the scheduling time window. If the current task is not executable, a delayed execution time is searched in the window until the latest time limit is reached; otherwise, the task will be rolled back into the waiting pool, resulting in an optimized task scheduling sequence and an updated path capacity status. Each task scheduling vector in the optimized task scheduling sequence includes the current task, the delayed execution time, and the feasible path of the current task. The scheduling instruction generation and execution module is used to combine the optimized task scheduling sequence, the transportation unit of the task allocation, and the scheduling security tag to generate structured scheduling instructions and send them to the execution system.

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