A task-aware device resource scheduling method and system

Abstract

This invention relates to the field of equipment resource scheduling technology, and discloses a task-aware equipment resource scheduling method and system. The method includes: parsing the tire vulcanization task instructions issued by the APS scheduling engine, identifying the temporal dependencies between vulcanization processes and constructing a task topology graph; traversing the nodes of the topology graph, verifying the processing accuracy of the vulcanizing machine and the mold compatibility, and outputting a resource requirement vector; calculating the fit weight between the vulcanization process and the vulcanizing machine when the equipment status meets the conditions; generating a resource allocation sequence in descending order of fit, converting the optimal combination into a task execution instruction and sending it to the actuator. This method improves the ability to perceive task characteristics and the dynamic adaptability between equipment and tasks, and achieves optimal matching of vulcanizing machine resources through dynamic error prevention verification, avoiding defects caused by mismatch between vulcanizing machine capacity and vulcanization process parameters.

Description

Technical Field

[0001] This invention relates to the field of equipment resource scheduling technology, and in particular to a task-aware equipment resource scheduling method and system. Background Technology

[0002] In discrete manufacturing scenarios, production resource scheduling, as a core link connecting production planning and execution, directly impacts the quality of production scheduling implementation. Tire vulcanization is a high-pressure, high-temperature, and precision process. Even minor deviations in mold compatibility (such as tread width / rim diameter) and processing accuracy (such as pressure control) can directly lead to defective products (such as tire deformation and blurred tread patterns). Therefore, in the complex environment of multi-specification tire mixed-line production, how to use task awareness to schedule equipment resources and avoid defects caused by mismatches between vulcanizing machine capacity and vulcanization process parameters has become an urgent problem to be solved.

[0003] Traditional resource scheduling methods often rely on static rules or manual experience, making it difficult to accurately interpret complex task instructions issued by the APS scheduling engine. This is especially true in multi-process collaborative scenarios, where insufficient identification of potential temporal dependencies between processes leads to a one-sided task logic model, resulting in mismatches between equipment resources and task requirements. This static scheduling model lacks a deep understanding of task characteristics, preventing the dynamic constraints in APS scheduling instructions from being effectively translated into resource allocation criteria, causing problems such as scheduling chain breaks and low resource utilization. In practice, existing scheduling schemes have been found to insufficiently consider the dynamic adaptability of equipment status and task requirements. Specifically, they cannot verify the matching degree between the equipment capacity database and task resource requirements in real time, and the perception of the current load status and fault information of equipment is lagging, resulting in a lack of systematic adaptation assessment. This scheduling mechanism is ill-suited to meet the needs of dynamic resource adjustments in flexible production models. Summary of the Invention

[0004] This invention provides a task-aware device resource scheduling method and system, the main purpose of which is to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a task-aware device resource scheduling method, comprising:

[0006] S1. Parse the tire vulcanization task instructions issued by the APS production scheduling engine, identify the temporal dependencies between vulcanization processes, and construct a task topology graph with the vulcanization processes as nodes and the temporal dependencies as edges.

[0007] S2. Traverse the vulcanization process nodes of the task topology graph, verify whether the processing accuracy of the vulcanizing machine meets the vulcanizing equipment accuracy threshold based on the tire special equipment capability database, and verify the compatibility between the mold specifications of the vulcanizing machine and the tire specifications and models. When the verification is passed, output the resource requirement vector of the vulcanization process.

[0008] S3. When the current utilization rate of the vulcanizing machine is lower than the preset load threshold and the fault status of the vulcanizing machine is marked as normal, calculate the compatibility weight between the vulcanizing process and the vulcanizing machine based on the resource demand vector.

[0009] S4. Generate a resource allocation sequence according to the descending order of the fit weights, convert the vulcanization process-vulcanizing machine combination with the highest fit in the resource allocation sequence into a task execution instruction, and send it to the actuator of the vulcanizing machine to trigger the start of the tire vulcanization task.

[0010] To address the above problems, the present invention also provides a task-aware device resource scheduling system, the system comprising:

[0011] The task topology construction module is used to parse the tire vulcanization task instructions issued by the APS scheduling engine, identify the temporal dependencies between vulcanization processes, and construct a task topology graph with the vulcanization processes as nodes and the temporal dependencies as edges.

[0012] The equipment capability verification and resource requirement generation module is used to traverse the vulcanization process nodes of the task topology graph, verify whether the processing accuracy of the vulcanizing machine meets the vulcanization equipment accuracy threshold based on the tire-specific equipment capability database, and verify the compatibility between the mold specifications of the vulcanizing machine and the tire specifications and models. When the verification is passed, the resource requirement vector of the vulcanization process is output.

[0013] The adaptability weight calculation module is used to calculate the adaptability weight between the vulcanizing process and the vulcanizing machine based on the resource demand vector when the current utilization rate of the vulcanizing machine is lower than the preset load threshold and the fault status of the vulcanizing machine is normal.

[0014] The resource allocation and instruction execution module is used to generate a resource allocation sequence according to the descending order of the fit weight, convert the vulcanization process-vulcanizing machine combination with the highest fit in the resource allocation sequence into a task execution instruction, and send it to the actuator of the vulcanizing machine to trigger the start of the tire vulcanization task.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 1. This invention, by parsing the task instructions issued by the APS scheduling engine, identifies the temporal dependencies between vulcanization processes and constructs a task topology graph. This solves the problem of insufficient identification of process temporal dependencies in traditional scheduling, which leads to a one-sided task logic model. It achieves deep perception of task characteristics, effectively transforming the dynamic constraints in the APS scheduling instructions into resource allocation criteria and avoiding scheduling link breaks. Furthermore, by traversing the nodes of the topology graph and verifying the vulcanization machining accuracy and mold specification compatibility based on the tire-specific equipment capability database, it outputs a resource requirement vector, solving the problem of mismatch between equipment and task requirements and ensuring accurate correspondence between equipment capabilities and task requirements.

[0017] 2. By calculating the fit weight based on the resource demand vector when the equipment load is normal and in good condition, and combining historical process data and mold change time to achieve a systematic evaluation of fit, the problem of lack of systematic fit evaluation in the existing solution is solved, and the rationality of resource allocation sequence is improved. By generating allocation sequence in descending order of fit and converting it into execution instructions, the problems of increased defect rate, equipment idleness or production delay caused by equipment capacity mismatch, process sequence conflict or unreasonable resource allocation are avoided. This effectively improves the efficiency and accuracy of equipment resource scheduling and meets the needs of dynamic resource adjustment in flexible production mode. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a task-aware device resource scheduling method according to an embodiment of the present invention.

[0019] Figure 2 This is a functional block diagram of a task-aware device resource scheduling system provided in an embodiment of the present invention;

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0022] This application provides a task-aware device resource scheduling method. The execution entity of the task-aware device resource scheduling method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the task-aware device resource scheduling method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms.

[0023] Reference Figure 1 The diagram shown is a flowchart illustrating a task-aware device resource scheduling method according to an embodiment of the present invention. In this embodiment, the task-aware device resource scheduling method includes:

[0024] S1. Parse the tire vulcanization task instructions issued by the APS production scheduling engine, identify the temporal dependencies between vulcanization processes, and construct a task topology graph with the vulcanization processes as nodes and the temporal dependencies as edges.

[0025] In this embodiment of the invention, the step of parsing the tire vulcanization task instruction issued by the APS production scheduling engine includes:

[0026] Decipher the tire specification code, vulcanization process sequence, and process parameter set contained in the tire vulcanization task instruction;

[0027] Verify the completeness and format compliance of the process parameters in the aforementioned process parameter set;

[0028] Output a structured task data set, which includes tire specifications, vulcanization process sequence, and a verified set of process parameters.

[0029] In this embodiment, the APS scheduling engine is an intelligent decision-making system for production planning and scheduling. It can generate optimal production task instructions based on production resources, order requirements, etc. In the tire manufacturing field, it is mainly used to coordinate the connection between the vulcanization process and the preceding and following processes. The tire vulcanization task instruction is an instruction file output by the APS scheduling engine that contains specific requirements for tire production, covering information such as production objects, process sequences, and process standards. The vulcanization process is a key process in tire manufacturing that cross-links rubber through heating and pressurization, including sub-processes such as preheating, vulcanization, and cooling. Each sub-process must meet specific process parameter requirements.

[0030] In the embodiments of this application, the temporal dependency relationship refers to the sequential execution constraint between vulcanization processes, that is, a certain process can only start after another process is completed (such as the vulcanization process needs to be started after the preheating process is completed); the task topology graph is a model that graphically represents the execution logic of the vulcanization process, where nodes represent processes and directed edges represent the temporal dependencies between processes.

[0031] In this embodiment of the application, parsing the tire vulcanization task instruction issued by the APS production scheduling engine includes the following steps:

[0032] First, the tire vulcanization task instruction issued by the APS production scheduling engine is read to extract three types of core information: tire specification and model code (e.g., "205 / 55R16", indicating a tire width of 205mm, an aspect ratio of 55%, and a rim diameter of 16 inches); vulcanization process sequence (e.g., the execution order of "preheating → vulcanization → cooling"); and process parameter set (e.g., preheating temperature 100℃, preheating time 5min; vulcanization temperature 160℃, vulcanization pressure 1.5MPa, vulcanization time 15min; cooling temperature 50℃, cooling time 8min).

[0033] Secondly, the extracted set of process parameters is compared with the preset tire vulcanization process parameter template (which includes three necessary parameters: temperature, pressure, and time). If any parameter is missing (such as vulcanization pressure), it is determined to be incomplete and the completion mechanism is triggered.

[0034] Finally, check whether the parameter units conform to industry standards (e.g., temperature unit is ℃ instead of K, pressure unit is MPa instead of Pa), and whether the numerical range is within a reasonable range (e.g., vulcanization temperature is usually 150-180℃, if the instruction is 250℃, it is judged as a format abnormality). Finally, integrate the verified information into structured data.

[0035] In this embodiment of the invention, identifying the temporal dependencies between vulcanization processes and constructing a task topology graph with the vulcanization processes as nodes and the temporal dependencies as edges includes:

[0036] Each independent vulcanization process is instantiated as a topology node, which contains a process identifier and a set of process parameters.

[0037] Analyze the process sequence constraints between vulcanization processes;

[0038] Establish node connection rules based on the aforementioned process sequence constraints;

[0039] Directed edges representing the execution order of processes are generated based on the node connection rules;

[0040] Aggregate all the topological nodes and the directed edges to form a task topology graph, wherein the direction of the directed edges represents the start-up timing constraints of the vulcanization process.

[0041] In this embodiment of the application, identifying the temporal dependencies between vulcanization processes and constructing a task topology graph includes the following steps:

[0042] First, each independent vulcanization process is instantiated as a topology node. The topology node includes a process identifier and a set of process parameters. This means that each vulcanization process is converted into a topology node. The node information includes a process identifier (such as "preheat-P01" and "vulcanization-P02") and a corresponding set of process parameters (such as the "vulcanization-P02" node being associated with parameters of 160℃, 1.5MPa, and 15min).

[0043] Secondly, analyzing the process sequence constraints between vulcanization processes refers to extracting constraint rules from the process description of the APS task instruction. For example, "The vulcanization process must be started after the preheating process is completed" (absolute constraint); "The cooling process must be started within 3 minutes after the vulcanization process is completed" (time window constraint).

[0044] Next, establishing node connection rules based on the process sequence constraints means defining the connection logic between nodes according to the constraints: absolute constraints correspond to strong connection rules such as "the subsequent process can only start after the preceding process is completed"; time window constraints correspond to weak connection rules such as "the completion time of the preceding process + 3 minutes ≥ the start time of the subsequent process".

[0045] Then, generating directed edges representing the execution order of the process based on the node connection rules means drawing directed edges based on the connection rules: the preheating process (P01) points to the vulcanization process (P02), forming a directed edge P01→P02, representing "vulcanization can only be started after preheating is completed"; the vulcanization process (P02) points to the cooling process (P03), forming a directed edge P02→P03, representing "cooling will be started within 3 minutes after vulcanization is completed";

[0046] Finally, aggregating all the topological nodes and directed edges to form a task topology graph means integrating all the topological nodes (P01, P02, P03) and directed edges (P01→P02, P02→P03) to form a complete task topology graph. In the graph, nodes carry process parameters, and the directions of directed edges clearly define the execution order. For example, the direction P01→P02 directly reflects the constraint that "preheating start-up is earlier than vulcanization".

[0047] In summary, this step addresses the issues that arise in traditional manual task instruction parsing, which is prone to errors due to misreading tire specification codes and omissions of process parameters (such as overlooking cooling time). Furthermore, existing scheduling methods often rely solely on textual descriptions of process dependencies (e.g., "preheat then vulcanize"), leading to misunderstandings and potentially reversed order (e.g., mistakenly scheduling cooling before vulcanization). This step, by explicitly defining temporal constraints through directed edges, reduces the error rate in process sequence.

[0048] S2. Traverse the vulcanization process nodes of the task topology graph, verify whether the processing accuracy of the vulcanizing machine meets the vulcanizing equipment accuracy threshold based on the tire-specific equipment capability database, and verify the compatibility between the mold specifications of the vulcanizing machine and the tire specifications and models. When the verification passes, output the resource requirement vector of the vulcanization process.

[0049] In this embodiment of the invention, the step of verifying whether the processing accuracy of the vulcanizing machine meets the vulcanizing equipment accuracy threshold based on the tire-specific equipment capability database includes:

[0050] Search the tire-specific equipment capability database for the rated pressure accuracy range of the target vulcanizing machine;

[0051] Extract the tooling pressure accuracy requirement value from the process parameters of the vulcanization process nodes;

[0052] Determine whether the rated pressure accuracy range covers the tooling pressure accuracy requirement value;

[0053] When the required tooling pressure accuracy is within the rated pressure accuracy range, a processing accuracy satisfaction indicator is generated.

[0054] In this embodiment of the invention, verifying the compatibility between the mold specifications of the vulcanizing machine and the tire specifications includes:

[0055] Obtain the tread width parameters and rim diameter parameters of the vulcanizing machine mold;

[0056] Extract the design tread width and design rim diameter from the tire specifications associated with the current vulcanization process node;

[0057] Compare whether the difference between the tread width parameter and the designed tread width is within the first set tolerance range;

[0058] Compare whether the difference between the rim diameter parameter and the designed rim diameter is within the second set tolerance range;

[0059] When both differences are within the corresponding tolerance range, mold compatibility is generated via an identifier.

[0060] In this embodiment of the invention, the resource requirement vector includes: extracting mold clamping force requirements, steam pressure requirements, vulcanization time requirements, and tire blank positioning accuracy requirements from the process parameter set of the vulcanization process node, and combining the extracted requirement parameters into a resource requirement vector.

[0061] In detail, the vulcanization process node in the task topology graph refers to the node representing the vulcanization process in the topology graph generated in step S1. Each node contains a process identifier and a corresponding set of process parameters (such as vulcanization temperature, pressure, etc.).

[0062] Furthermore, the tire-specific equipment capability database is a structured database that stores the performance parameters of tire production-specific equipment (such as vulcanizing machines). It includes basic data such as the equipment's rated pressure accuracy and mold specifications, serving as a benchmark for equipment capability verification. Its initial data values ​​will remain consistent throughout the entire process.

[0063] In detail, processing accuracy refers to the deviation range between the actual pressure and the set pressure of the vulcanizing machine during operation, while the vulcanizing equipment accuracy threshold is the maximum allowable pressure deviation of the vulcanizing process. Both are core indicators for measuring whether the vulcanizing machine can meet the process requirements.

[0064] Furthermore, the compatibility between mold specifications and tire specifications refers to whether the physical parameters of the vulcanizing mold (such as tread width and rim diameter) match the design parameters of the tire to be produced. The compatibility directly affects the tire molding quality.

[0065] In detail, the resource requirement vector is a multi-dimensional array formed by quantifying the resources required for the vulcanization process (such as clamping force, steam pressure, etc.), providing standardized input for subsequent equipment adaptation calculations.

[0066] In this embodiment, traversing the vulcanization process nodes of the task topology graph means selecting the vulcanization process nodes sequentially from the task topology graph generated by S1 according to the pointing order of the directed edges (e.g., the node order of preheating → vulcanization → cooling). For example, for a topology graph containing nodes P01 (preheating), P02 (vulcanization), and P03 (cooling), only node P02 (vulcanization process node) is traversed to extract its process parameter set (e.g., vulcanization pressure 1.5MPa, accuracy requirement ±0.05MPa).

[0067] In this embodiment of the application, verifying whether the processing accuracy of the vulcanizing machine meets the vulcanizing equipment accuracy threshold based on the tire-specific equipment capability database includes the following steps:

[0068] First, querying the rated pressure accuracy range of the target vulcanizing machine in the tire-specific equipment capability database means accessing the tire-specific equipment capability database and retrieving the rated pressure accuracy parameter of the vulcanizing machine with the number M001, which is found to be ±0.08MPa.

[0069] Secondly, extracting the tooling pressure accuracy requirement value from the process parameter set of the vulcanization process node means extracting the tooling pressure accuracy requirement value of ±0.05MPa from the process parameter set of the current vulcanization process node (such as P02) (this value comes from the process parameter set verified in S1 to ensure data chain closure).

[0070] Next, determining whether the rated pressure accuracy range covers the tooling pressure accuracy requirement value refers to comparing the rated pressure accuracy range (±0.08MPa) with the requirement value (±0.05MPa). Since ±0.05MPa is completely within the ±0.08MPa range, it is determined to cover the requirement.

[0071] Finally, when the required tooling pressure accuracy is within the rated pressure accuracy range, generating a processing accuracy satisfaction flag means generating a flag "accuracy passed" based on the above judgment result and storing it in a temporary database as a prerequisite for subsequent compatibility verification.

[0072] For example, if a vulcanization process requires a pressure deviation of no more than ±0.05MPa, and the rated deviation of the vulcanizing machine M001 is ±0.08MPa, the test can be passed because the required value is within the equipment's capacity. However, if the required value is ±0.03MPa, and the equipment's rated range is ±0.02MPa, the test will fail.

[0073] In this embodiment of the application, verifying the compatibility between the mold specifications of the vulcanizing machine and the tire specifications includes the following steps:

[0074] Obtaining the tread width and rim diameter parameters of the vulcanizing machine mold refers to retrieving the parameters of the mold matching the M001 vulcanizing machine from the tire special equipment capability database, resulting in a tread width parameter of 205mm and a rim diameter parameter of 16 inches.

[0075] Extracting the design tread width and design rim diameter from the tire specifications associated with the current vulcanization process node means resolving the design tread width of 205mm and the design rim diameter of 16 inches from the tire specification “205 / 55R16” associated with the current vulcanization process node.

[0076] Comparing the difference between the tread width parameter and the designed tread width to see if it is within the first set tolerance range means setting the first set tolerance range to ±1mm. If the difference between the mold tread width (205mm) and the design value (205mm) is 0mm, and this difference is within the tolerance range, the tread width is determined to be compatible.

[0077] Comparing the difference between the rim diameter parameter and the designed rim diameter to see if it is within the second set tolerance range means setting the second set tolerance range to ±0.5 inches. If the difference between the mold rim diameter (16 inches) and the design value (16 inches) is 0 inches, it is within the tolerance range, and the rim diameter is determined to be compatible.

[0078] When both differences are within the corresponding tolerance range, generating a "mold compatibility pass" signifies that both differences meet the tolerance requirements.

[0079] In this embodiment of the application, when the verification is passed, the output resource requirement vector of the vulcanization process refers to extracting four core parameters from the process parameter set of the vulcanization process node P02 after both the processing accuracy and mold compatibility have been verified: the mold clamping force requirement is 500kN, the steam pressure requirement is 1.6MPa, the vulcanization time requirement is 15min, and the tire blank positioning accuracy requirement is ±0.5mm. The above parameters are combined in order to form the resource requirement vector [500kN, 1.6MPa, 15min, ±0.5mm].

[0080] In general, incompatibility between molds and tire specifications (such as using a 17-inch mold to produce a 16-inch tire) will lead to a high defect rate. This step significantly reduces the compatibility error rate through tolerance comparison.

[0081] S3. When the current utilization rate of the vulcanizing machine is lower than the preset load threshold and the fault status of the vulcanizing machine is marked as normal, calculate the compatibility weight between the vulcanizing process and the vulcanizing machine based on the resource demand vector.

[0082] In this embodiment of the invention, calculating the compatibility weight between the vulcanization process and the vulcanizing machine based on the resource demand vector includes:

[0083] Obtain historical process data of the vulcanizing machine for the target tire specification;

[0084] The specification matching degree is calculated based on the tread width and rim diameter parameters obtained in the mold compatibility verification.

[0085] The mold change time is estimated based on the vulcanization time requirement in the resource demand vector.

[0086] The historical process data and the mold change time are weighted and calculated to generate the adaptation weight.

[0087] In this embodiment of the invention, the historical process data includes: the yield rate index of historical production cycles.

[0088] In detail, the current utilization rate refers to the ratio of the actual running time of the vulcanizing machine to the total available time in the current period, which is used to measure the load level of the equipment; the preset load threshold is a pre-set critical value for judging whether the vulcanizing machine is in an idle state, for example, it is set to 70%. When the current utilization rate is lower than this value, it indicates that the equipment has spare capacity.

[0089] In detail, the fault status indicator is a mark used to record the operating status of the vulcanizing machine. "Normal" means that the equipment is fault-free and can be put into production, while "abnormal" means that the equipment needs maintenance. The compatibility weight is a numerical value that quantifies the degree of matching between the vulcanizing process and the vulcanizing machine. The higher the weight, the better the compatibility between the two.

[0090] In detail, historical process data refers to the accumulated operating records of the vulcanizing machine when it produced the target tire specifications in the past. In this embodiment, it specifically refers to the yield rate index of the historical production cycle (i.e., the ratio of qualified tires to total output). When the current utilization rate of the vulcanizing machine is lower than the preset load threshold and the fault status of the vulcanizing machine is marked as normal, the compatibility weight between the vulcanizing process and the vulcanizing machine is calculated based on the resource demand vector.

[0091] In this embodiment, the equipment load status is first determined, and the current utilization rate of the vulcanizing machine M001 is obtained, assumed to be 60%, with a preset load threshold of 70%. Since 60% < 70%, the load condition is met. Next, the equipment fault status is confirmed, and the fault status identifier of the vulcanizing machine M001 is queried as "normal", indicating no maintenance requirement. When both of the above conditions are met simultaneously, the resource requirement vector (such as [the mold clamping force of 500kN and vulcanization time of 15min output in S2]) is invoked to start the adaptation weight calculation.

[0092] In this embodiment of the application, obtaining the historical process data of the vulcanizing machine for the target tire specification means retrieving the historical records of the vulcanizing machine M001 producing "205 / 55R16" model tires from the tire production database, and extracting the yield rate index from the historical process data, such as the average yield rate of 98% in the past 30 days.

[0093] In this embodiment of the application, calculating the specification matching degree based on the tread width parameter and rim diameter parameter obtained in the mold compatibility verification means calling the parameters of the mold compatibility verification in S2: the tread width of the vulcanizing machine M001 is 205mm and the rim diameter is 16 inches, which is completely consistent with the tire design parameters (tread width 205mm and rim diameter 16 inches).

[0094] In this embodiment of the application, the calculation of specification matching degree includes: setting the matching degree calculation formula as "(1-absolute value of parameter difference / design parameter)×100%", since the difference is 0, the specification matching degree is 100%.

[0095] In this embodiment of the application, the estimated mold change time based on the vulcanization time requirement in the resource demand vector refers to extracting the vulcanization time requirement of 15min from the resource demand vector output by S2, establishing the correlation between mold change time and vulcanization time based on historical data (for example, mold change time = vulcanization time × 20%), then the estimated mold change time is 15min × 20% = 3min.

[0096] In this embodiment of the application, the weighted calculation of the historical process data and the mold change time to generate the adaptation weight includes the following steps: Assuming that the weight of historical process data (yield rate) is 70%, the weight of mold change time is 30%, and the yield rate is 98%, it is converted into a standardized score of 98 points (out of 100 points); the mold change time is 3 minutes, and the benchmark mold change time is set to 5 minutes, which is converted into a standardized score of (1-3 / 5)×100=40 points; then the adaptation weight is 98×70%+40×30%=68.6+12=80.6.

[0097] Furthermore, the historical production cycle yield rate refers to the ratio of the number of qualified tires per batch to the total number of tires produced by the vulcanizing machine in the past when producing the same tire specifications. For example, when vulcanizing machine M001 produces "205 / 55R16" tires, if a batch of 100 tires is produced and 98 are qualified, the yield rate for that batch is 98%. This indicator directly reflects the processing stability of the equipment for the target process and provides a quality dimension reference for adaptability calculation.

[0098] In summary, this step addresses the issues of "equipment overload scheduling" and "misallocation of faulty equipment" through dual constraints of load thresholds and fault status. By weighting historical process data and mold change time, it incorporates "quality stability" and "scheduling efficiency" into the same adaptation model for the first time, improving the accuracy of process-equipment matching compared to traditional scheduling methods that only consider equipment idle status.

[0099] S4. Generate a resource allocation sequence according to the descending order of the fit weights, convert the vulcanization process-vulcanizing machine combination with the highest fit in the resource allocation sequence into a task execution instruction, and send it to the actuator of the vulcanizing machine to trigger the start of the tire vulcanization task.

[0100] In this embodiment of the invention, the step of converting the vulcanization process-vulcanizing machine combination with the highest fit in the resource allocation sequence into a task execution instruction includes:

[0101] Obtain the vulcanization temperature profile from the tire specifications;

[0102] Configure pressure control parameters according to the steam pressure demand in the resource demand vector;

[0103] Integrate the embryo positioning accuracy requirement from the resource requirement vector;

[0104] Generate a stream of resolvable binary control instructions for the vulcanizing machine actuator.

[0105] In detail, the descending order of compatibility weights refers to arranging the compatibility weights of the vulcanization process and each vulcanizing machine in descending order to clarify the equipment priority.

[0106] In this embodiment of the application, the resource allocation sequence is a list of vulcanizing process-vulcanizing machine combinations formed in descending order of fit weight, with the first item in the list being the optimal matching combination; the vulcanizing process-vulcanizing machine combination refers to the correspondence between a single vulcanizing process and a matching vulcanizing machine, such as "205 / 55R16 vulcanizing process-vulcanizing machine M001".

[0107] In this embodiment, the task execution instruction is an operation command that the vulcanizing machine actuator can recognize, which includes all process parameters and control logic required for the vulcanizing process; the vulcanizing machine actuator is a hardware component that receives and executes the control instruction, and is responsible for driving the vulcanizing machine to complete actions such as preheating, pressurization, and timing.

[0108] Furthermore, the vulcanization temperature curve is a temperature control standard that changes over time, such as "heating to 160℃ in 0-5 minutes and maintaining 160℃ in 5-20 minutes", which is determined by the tire specifications and model; the pressure control parameters are set values ​​used to adjust the steam pressure of the vulcanizing machine, which need to be matched with the steam pressure requirements in the resource demand vector.

[0109] Furthermore, the tire blank positioning accuracy requirement is the allowable deviation of the tire blank's installation position in the vulcanizing machine, which directly affects the tire forming accuracy; the binary control command flow is to convert process parameters and control logic into a machine-recognizable binary code sequence to ensure that the actuator can accurately interpret it.

[0110] In this embodiment of the application, generating a resource allocation sequence based on the descending order of the fitness weights means assuming that, after calculation by S3, the fitness weight of vulcanizing machine M001 is 80.6, that of vulcanizing machine M002 is 72.3, and that of vulcanizing machine M003 is 65.8, and that the resource allocation sequence is formed by arranging them in descending order: [M001 (80.6) → M002 (72.3) → M003 (65.8)].

[0111] In this embodiment of the application, converting the vulcanization process-vulcanizing machine combination with the highest fit in the resource allocation sequence into a task execution instruction means extracting the first combination "205 / 55R16 vulcanization process-vulcanizing machine M001" from the resource allocation sequence. This combination is the optimal match, and converting the process parameters (such as temperature, pressure, etc.) corresponding to this combination into an instruction format executable by the vulcanizing machine. Sending the instruction to the actuator of the vulcanizing machine to trigger the start of the tire vulcanization task means sending the task execution instruction to the actuator of the vulcanizing machine M001 via industrial Ethernet. After receiving the instruction, the actuator starts the vulcanization program and completes preheating, vulcanization, cooling and other actions according to the instructions.

[0112] In this embodiment, obtaining the vulcanization temperature curve from the tire specification refers to calling the mapping database of tire specifications and vulcanization temperature curves (pre-set in the system), inputting the tire specification "205 / 55R16", and retrieving the corresponding vulcanization temperature curve: 0-5 min from room temperature to 160℃, 5-20 min maintaining 160℃, and 20-25 min cooling to 80℃. This curve provides a benchmark for temperature control during the vulcanization process, ensuring that different tire specifications adopt differentiated heating, heat preservation, and cooling strategies.

[0113] In this embodiment, configuring the pressure control parameter based on the steam pressure demand in the resource demand vector means extracting the steam pressure demand of 1.6 MPa from the resource demand vector output by S2, and calculating the pressure control parameter as 1.6 MPa × 1.05 = 1.68 MPa based on the calibration formula of the vulcanizing machine pressure control system (pressure control parameter = steam pressure demand × 1.05, with a 5% redundancy reserve). This parameter is then written into the pressure control module to ensure that the actual steam pressure during vulcanization remains stable within the range of 1.6 MPa ± 5%.

[0114] In this embodiment, integrating the tire blank positioning accuracy requirement from the resource requirement vector means extracting the tire blank positioning accuracy requirement as ±0.5mm from the resource requirement vector and converting this parameter into a positioning control command for the vulcanizing machine's robotic arm. Specifically, after the robotic arm grasps the tire blank, it must place it in a preset position within the mold, with a deviation not exceeding ±0.5mm (the conversion rule is based on the robotic arm's positioning accuracy calibration data). During the integration process, the system automatically verifies whether this accuracy requirement is compatible with the vulcanizing machine's robotic arm's maximum positioning error (±0.3mm). Since ±0.5mm > ±0.3mm, it is determined to be compatible, and the integration is completed (the verification logic ensures that the equipment capability meets the process requirements).

[0115] In this embodiment, generating a resolvable binary control command stream for the vulcanizing machine actuator refers to converting the vulcanizing temperature curve (digitized into a time-temperature array), pressure control parameters (1.68 MPa), and tire blank positioning accuracy requirements (±0.5 mm) obtained in the aforementioned steps into binary code according to a preset protocol (such as the Modbus-RTU protocol).

[0116] Temperature curve: 00101101 (representing the heating stage command), 00110010 (representing the heat preservation stage command); Pressure parameter: 01001101 (corresponding to 1.68MPa); Positioning accuracy: 01101001 (corresponding to ±0.5mm); These binary codes are concatenated into a continuous command stream according to the execution order (e.g., 00101101001100100100110101101001), which can be directly parsed and converted into mechanical actions by the vulcanizing machine actuator.

[0117] In summary, traditional techniques require manual input of parameters for instruction generation, which is prone to execution failure due to formatting errors. This step, however, achieves end-to-end automation of "process requirements - equipment parameters - binary instructions" for the first time through automatic integration of multiple parameters and binary conversion, thereby reducing the instruction error rate.

[0118] like Figure 2 The diagram shown is a functional block diagram of a task-aware device resource scheduling system provided in an embodiment of the present invention.

[0119] The task-aware device resource scheduling system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the task-aware device resource scheduling system 100 may include a task topology construction module 101, a device capability verification and resource requirement generation module 102, an adaptation weight calculation module 103, and a resource allocation and instruction execution module 104. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0120] In this embodiment, the functions of each module / unit are as follows:

[0121] The task topology construction module 101 is used to parse the tire vulcanization task instructions issued by the APS scheduling engine, identify the temporal dependencies between vulcanization processes, and construct a task topology graph with the vulcanization processes as nodes and the temporal dependencies as edges.

[0122] The equipment capability verification and resource requirement generation module 102 is used to traverse the vulcanization process nodes of the task topology map, verify whether the processing accuracy of the vulcanizing machine meets the vulcanization equipment accuracy threshold based on the tire-specific equipment capability database, and verify the compatibility between the mold specifications of the vulcanizing machine and the tire specifications and models. When the verification is passed, the resource requirement vector of the vulcanization process is output.

[0123] The adaptability weight calculation module 103 is used to calculate the adaptability weight between the vulcanizing process and the vulcanizing machine based on the resource demand vector when the current utilization rate of the vulcanizing machine is lower than the preset load threshold and the fault status of the vulcanizing machine is normal.

[0124] The resource allocation and instruction execution module 104 is used to generate a resource allocation sequence according to the descending order of the fit weight, convert the vulcanization process-vulcanizing machine combination with the highest fit in the resource allocation sequence into a task execution instruction, and send it to the actuator of the vulcanizing machine to trigger the start of the tire vulcanization task.

[0125] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0126] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0128] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0129] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A task-aware device resource scheduling method, characterized in that, The method includes: S1. Parse the tire vulcanization task instructions issued by the APS production scheduling engine, identify the temporal dependencies between vulcanization processes, and construct a task topology graph with the vulcanization processes as nodes and the temporal dependencies as edges. S2. Traverse the vulcanization process nodes of the task topology graph, verify whether the processing accuracy of the vulcanizing machine meets the vulcanizing equipment accuracy threshold based on the tire special equipment capability database, and verify the compatibility between the mold specifications of the vulcanizing machine and the tire specifications and models. When the verification is passed, output the resource requirement vector of the vulcanization process. S3. When the current utilization rate of the vulcanizing machine is lower than the preset load threshold and the fault status of the vulcanizing machine is marked as normal, calculate the compatibility weight between the vulcanizing process and the vulcanizing machine based on the resource demand vector. S4. Generate a resource allocation sequence according to the descending order of the fit weights, convert the vulcanization process-vulcanizing machine combination with the highest fit in the resource allocation sequence into a task execution instruction, and send it to the actuator of the vulcanizing machine to trigger the start of the tire vulcanization task.

2. The task-aware device resource scheduling method as described in claim 1, characterized in that, The analysis of the tire vulcanization task instructions issued by the APS production scheduling engine includes: Decipher the tire specification code, vulcanization process sequence, and process parameter set contained in the tire vulcanization task instruction; Verify the completeness and format compliance of the process parameters in the aforementioned process parameter set; Output a structured task data set, which includes tire specifications, vulcanization process sequence, and a verified set of process parameters.

3. The task-aware device resource scheduling method as described in claim 1, characterized in that, The process of identifying the temporal dependencies between vulcanization processes and constructing a task topology graph with the vulcanization processes as nodes and the temporal dependencies as edges includes: Each independent vulcanization process is instantiated as a topology node, which contains a process identifier and a set of process parameters. Analyze the process sequence constraints between vulcanization processes; Establish node connection rules based on the aforementioned process sequence constraints; Directed edges representing the execution order of processes are generated based on the node connection rules; Aggregate all the topological nodes and the directed edges to form a task topology graph, wherein the direction of the directed edges represents the start-up timing constraints of the vulcanization process.

4. The task-aware device resource scheduling method as described in claim 1, characterized in that, The verification of whether the processing accuracy of the vulcanizing machine meets the vulcanizing equipment accuracy threshold based on the tire-specific equipment capability database includes: Search the tire-specific equipment capability database for the rated pressure accuracy range of the target vulcanizing machine; Extract the tooling pressure accuracy requirement value from the process parameters of the vulcanization process nodes; Determine whether the rated pressure accuracy range covers the tooling pressure accuracy requirement value; When the required tooling pressure accuracy is within the rated pressure accuracy range, a processing accuracy satisfaction indicator is generated.

5. The task-aware device resource scheduling method as described in claim 1, characterized in that, The verification of the compatibility between the mold specifications of the vulcanizing machine and the tire specifications and models includes: Obtain the tread width parameters and rim diameter parameters of the vulcanizing machine mold; Extract the design tread width and design rim diameter from the tire specifications associated with the current vulcanization process node; Compare whether the difference between the tread width parameter and the designed tread width is within the first set tolerance range; Compare whether the difference between the rim diameter parameter and the designed rim diameter is within the second set tolerance range; When both differences are within the corresponding tolerance range, mold compatibility is generated by marking.

6. The task-aware device resource scheduling method as described in claim 1, characterized in that, The resource requirement vector includes: extracting mold clamping force requirements, steam pressure requirements, vulcanization time requirements, and tire blank positioning accuracy requirements from the process parameters set of the vulcanization process nodes, and combining the extracted requirement parameters into a resource requirement vector.

7. The task-aware device resource scheduling method as described in claim 1, characterized in that, The calculation of the compatibility weight between the vulcanization process and the vulcanizing machine based on the resource demand vector includes: Obtain historical process data of the vulcanizing machine for the target tire specification; The specification matching degree is calculated based on the tread width and rim diameter parameters obtained in the mold compatibility verification. The mold change time is estimated based on the vulcanization time requirement in the resource demand vector. The historical process data and the mold change time are weighted and calculated to generate the adaptation weight.

8. The task-aware device resource scheduling method as described in claim 7, characterized in that, The historical process data includes: yield rate indicators for historical production cycles.

9. The task-aware device resource scheduling method as described in claim 1, characterized in that, The step of converting the vulcanization process-vulcanizing machine combination with the highest fit in the resource allocation sequence into task execution instructions includes: Obtain the vulcanization temperature profile from the tire specifications; Configure pressure control parameters according to the steam pressure demand in the resource demand vector; Integrate the embryo positioning accuracy requirement from the resource requirement vector; Generate a stream of resolvable binary control instructions for the vulcanizing machine actuator.

10. A task-aware device resource scheduling system, characterized in that, The system includes: The task topology construction module is used to parse the tire vulcanization task instructions issued by the APS scheduling engine, identify the temporal dependencies between vulcanization processes, and construct a task topology graph with the vulcanization processes as nodes and the temporal dependencies as edges. The equipment capability verification and resource requirement generation module is used to traverse the vulcanization process nodes of the task topology graph, verify whether the processing accuracy of the vulcanizing machine meets the vulcanization equipment accuracy threshold based on the tire-specific equipment capability database, and verify the compatibility between the mold specifications of the vulcanizing machine and the tire specifications and models. When the verification is passed, the resource requirement vector of the vulcanization process is output. The adaptability weight calculation module is used to calculate the adaptability weight between the vulcanizing process and the vulcanizing machine based on the resource demand vector when the current utilization rate of the vulcanizing machine is lower than the preset load threshold and the fault status of the vulcanizing machine is normal. The resource allocation and instruction execution module is used to generate a resource allocation sequence according to the descending order of the fit weight, convert the vulcanization process-vulcanizing machine combination with the highest fit in the resource allocation sequence into a task execution instruction, and send it to the actuator of the vulcanizing machine to trigger the start of the tire vulcanization task.

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