A method, system and device for adaptive update of process sequencing reference values

Abstract

The application discloses a kind of process sequencing reference value self-adapting updating method, system and equipment, it is related to algorithm engineering technical field.The method includes: obtaining first candidate task set and calculating initial reference value;Update set and save old reference value;Second candidate task set is generated based on non-off task and additional task, additional local extreme value is calculated;Whether additional local extreme value satisfies the condition of replacing old reference value is judged;If it is satisfied, additional local extreme value is used as current reference value;If it is not satisfied, current reference value is determined according to old reference task state.The application introduces the priority determination path of additional local extreme value, avoids forced traversal of all candidate tasks each time candidate set changes, triggers recalculation only when necessary, and clearly defines the reference updating logic path, solves the logical confusion problem caused by the mixed processing of reference replacement and task off-scenario in the prior art, reduces the scanning cost and improves the scheduling efficiency.

Description

Technical Field

[0001] This application relates to the field of algorithm engineering technology, specifically to an adaptive update method, system, and device for process sorting benchmark values. Background Technology

[0002] Advanced Planning and Scheduling (APS) systems are widely used in smart manufacturing to solve production task scheduling problems under complex constraints. In APS scenarios with multiple admission policies and some policies enabling threshold tolerance, determining whether a task meets the admission criteria often relies on a dynamic baseline value. This baseline value is usually the extreme value of a certain dimension in the current candidate set. As tasks are selected and subsequent tasks are added, the candidate set is in a dynamic evolution process, and the baseline value changes accordingly. Therefore, it needs to be maintained in real time to ensure the correct execution of the scheduling policy.

[0003] In existing technologies, the maintenance of dynamic baseline values ​​typically employs a naive approach: recalculating the extreme value by traversing all candidate tasks after each change in the candidate set. However, this approach has significant drawbacks: firstly, frequent traversals lead to high repetitive scanning costs, impacting scheduling efficiency; secondly, baseline replacement (the emergence of a better task) and baseline task removal (the removal of the original baseline task) are often handled interchangeably, lacking systematic branching logic and prone to baseline drift or state inconsistencies. Therefore, designing an adaptive baseline value update method with separable paths, controllable complexity, and alignment with the candidate set evolution process has become a pressing technical problem for those skilled in the art. Summary of the Invention

[0004] This paper addresses the technical problems in the dynamic reference value maintenance method of APS system, which suffers from high scanning costs due to frequent full traversal and reference drift or inconsistent status caused by mixed processing of reference replacement and departure scenarios.

[0005] In a first aspect, embodiments of this application provide an adaptive update method for process sequencing benchmark values, the adaptive update method for process sequencing benchmark values ​​comprising: Obtain the first candidate task set for the current round, and calculate the extreme value of the comparison parameters of all tasks in the first candidate task set under all threshold strategies as the initial baseline value, and use the task identifier corresponding to the extreme value in the comparison parameters as the baseline task identifier. The first candidate task set is updated based on all selected threshold strategies to remove some tasks, while the initial baseline value before the update is saved as the old baseline value. A second candidate task set is generated based on the non-leaving tasks and the additional task set of the first candidate task set, and the extreme value of the comparison parameter of the additional task set under the current threshold policy is calculated as the additional local extreme value. Based on the current threshold strategy, determine whether adding a local extremum satisfies the condition of replacing the old benchmark value, and determine the current benchmark value of the second candidate task set based on the judgment result; If the added local extremum satisfies the replacement condition, then the added local extremum is used as the current baseline value of the second candidate task set, and the task identifier corresponding to the added local extremum is used as the baseline task identifier. If adding a local extremum does not satisfy the replacement condition, then the current benchmark value and benchmark task identifier of the second candidate task set are re-determined based on the old benchmark task state corresponding to the old benchmark value.

[0006] In conjunction with the first aspect, in one implementation, the step of re-determining the current benchmark value of the second candidate task set based on the old benchmark task state corresponding to the old benchmark value includes: Obtain the old baseline task identifier corresponding to the old baseline value; Determine whether the task identifier for the departure task is consistent with the old baseline task identifier; If the task identifier of the departure task is inconsistent with the old baseline task identifier, the old baseline value will be used as the current baseline value. If the task identifier of the departing task is consistent with the old baseline task identifier, then the extreme value of the comparison parameter of the second candidate task set is recalculated as the current baseline value.

[0007] In conjunction with the first aspect, in one implementation, determining whether the addition of a local extremum based on the current threshold strategy satisfies the condition for replacing the old benchmark value includes: Obtain the comparison direction corresponding to the current threshold strategy, and determine the relationship between the additional local extremum and the old benchmark value based on the comparison direction.

[0008] In conjunction with the first aspect, in one implementation, the calculation of the extreme value among the comparison parameters of all tasks in the first candidate task set under each threshold policy as the initial benchmark value includes: Sort all candidate tasks in the first candidate task set by unique identifiers; Calculate the attribute values ​​of all candidate tasks in the first candidate task set under all threshold policies, and select the extreme value in the predetermined comparison direction among all attribute values ​​under the current threshold policy as the initial benchmark value.

[0009] In conjunction with the first aspect, in one implementation, selecting the extreme value in the predetermined comparison direction among all attribute values ​​under the current threshold strategy as the initial benchmark value further includes: If there are parallel extreme values ​​among all attribute values, the initial baseline value and the corresponding baseline task are determined by sorting them according to the unique identifier.

[0010] In conjunction with the first aspect, in one implementation, the step of uniquely identifying and sorting all candidate tasks in the first candidate task set includes: All candidate tasks are uniquely identified and sorted using predetermined stable rules, which include: ID dictionary order, timestamp order, business priority label sorting, and composite key sorting.

[0011] In conjunction with the first aspect, in one implementation, saving the initial baseline value before the update as the old baseline value includes: The initial baseline values ​​and corresponding baseline task identifiers before the first candidate task set is updated are saved using any one of the following methods: memory image, version number snapshot, or transaction log.

[0012] In conjunction with the first aspect, in one implementation, after determining the current baseline value of the second candidate task set based on the judgment result, the method further includes: We analyze the changes in baseline values ​​and baseline task identifiers after this round of updates to generate baseline change signals.

[0013] Secondly, this application provides an adaptive update system for process sequencing benchmark values, the adaptive update system for process sequencing benchmark values ​​comprising: The baseline initialization module is used to obtain the first candidate task set of the current round, and calculate the extreme value of the comparison parameters of all tasks in the first candidate task set under each threshold policy as the initial baseline value, and use the task identifier corresponding to the extreme value in the comparison parameters as the baseline task identifier. The set update and snapshot module is used to determine the departing tasks that leave the first candidate task set after the update, generate a second candidate task set based on the non-departing tasks and the appended task set of the first candidate task set, and save the initial baseline value before the update as the old baseline value. The local extremum calculation module is used to calculate the extreme values ​​of the comparison parameters of the additional task set under the current threshold policy as additional local extremums. The benchmark update module is used to determine whether adding a local extremum satisfies the condition of replacing the old benchmark value based on the current threshold strategy, and to determine the current benchmark value of the second candidate task set based on the judgment result. If the added local extremum satisfies the replacement condition, then the added local extremum is used as the current benchmark value of the second candidate task set, and the task identifier corresponding to the added local extremum is used as the benchmark task identifier. If the additional local extremum does not meet the replacement condition, the current benchmark value and benchmark task identifier of the second candidate task set are re-determined based on the old benchmark task state corresponding to the old benchmark value.

[0014] Thirdly, this application provides an adaptive update device for process sequencing benchmark values, the adaptive update device for process sequencing benchmark values ​​includes a processor, a memory, and an adaptive update program for process sequencing benchmark values ​​stored in the memory and executable by the processor, wherein when the adaptive update program for process sequencing benchmark values ​​is executed by the processor, it implements the steps of the adaptive update method for process sequencing benchmark values ​​as described above.

[0015] The beneficial effects of the technical solutions provided in this application include: This application avoids forcibly traversing all candidate tasks every time the candidate set changes by introducing a priority decision path for appending local extrema, triggering full or partial recalculation only when necessary. Simultaneously, it explicitly separates the benchmark update logic into two main paths: "append preemption" and "old benchmark state determination," resolving the logical confusion caused by the mixed handling of benchmark replacement and task departure scenarios in existing technologies. Attached Figure Description

[0016] Figure 1 A flowchart illustrating an embodiment of the adaptive update method for process sorting benchmark values ​​in this application; Figure 2 This is a schematic diagram of the hardware structure of the process sequencing benchmark value adaptive update device involved in the embodiments of this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0018] This paper addresses the technical problems in the dynamic reference value maintenance method of APS system, which suffers from high scanning costs due to frequent full traversal and reference drift or inconsistent status caused by mixed processing of reference replacement and departure scenarios.

[0019] Firstly, such as Figure 1 As shown in the figure, this application provides an adaptive update method for process sorting benchmark values, the adaptive update method for process sorting benchmark values ​​includes: Step S1: Obtain the first candidate task set for the current round, and calculate the extreme value of the comparison parameters of all tasks in the first candidate task set under each threshold strategy as the initial benchmark value, and use the task identifier corresponding to the extreme value in the comparison parameters as the benchmark task identifier.

[0020] In some preferred embodiments, step S1 includes: Step S1a: Sort all candidate tasks in the first candidate task set by unique identifiers.

[0021] It is understandable that by sorting all candidate tasks by unique identifiers, after calculating the attribute values ​​of all candidate tasks under the current threshold strategy, if there are multiple tasks in parallel when selecting the extreme values ​​in the predetermined comparison direction (i.e., parallel extreme values), the initial benchmark value and the corresponding benchmark task are determined according to the sorting by the unique identifiers, so as to ensure the certainty of subsequent benchmark task determination.

[0022] Preferably, a predetermined stable rule is used to uniquely identify and sort all candidate tasks. The predetermined stable rule includes any one of the following: ID dictionary order rule, timestamp order rule, business priority label sorting rule, and composite key sorting rule.

[0023] Understandably, lexicographical ordering of IDs is simple to implement and highly reproducible for unique identification. Business priority tag sorting, using unique identifiers, better aligns with operational strategies. Timestamp ordering emphasizes first-come, first-served characteristics. Composite key sorting (such as business priority + ID) is suitable for scenarios with strong multi-objective constraints.

[0024] Step S1b: Calculate the attribute values ​​of all candidate tasks in the first candidate task set under the threshold policy for each one.

[0025] It should be noted that attribute values ​​are scalar results used for comparison among candidate tasks under the current threshold strategy. They can be directly mapped from a single field or calculated from multiple business fields. Attribute value weights can be obtained through manual setting, historical learning, or distribution from the configuration center.

[0026] Specifically, multiple threshold strategies are pre-set before scheduling, and each threshold strategy corresponds to a pre-defined comparison direction. At the beginning of scheduling, for each threshold strategy, the attribute values ​​of each task under that strategy (such as delivery time margin, priority score, switching loss, etc.) are extracted from the first candidate task set as comparison parameters.

[0027] It should be noted that the comparison parameters can be scalar attributes such as task priority score, delivery time margin, and line change loss value.

[0028] Step S1c: After determining the selected threshold strategy from all threshold strategies, select the extreme value of all attribute values ​​in the comparison direction corresponding to the selected threshold strategy as the initial benchmark value, and use the task identifier corresponding to the extreme value in the comparison parameters as the benchmark task identifier.

[0029] It should be noted that when selecting a threshold strategy, one or more threshold strategies can be selected. When multiple threshold strategies are selected, each selected threshold strategy is used as the current threshold strategy, and subsequent steps S2-S5 are executed in parallel. The current baseline value, current baseline task identifier, and baseline change signal under different current threshold strategies are output. The current threshold strategy mentioned below refers only to one of the selected threshold strategies; the execution steps for the other selected threshold strategies are the same as this step, thereby achieving the effect of simultaneous execution of multiple admission strategies.

[0030] Specifically, the predetermined comparison direction is pre-set, such as pursuing the maximum value (e.g., priority) or the minimum value (e.g., line switching loss).

[0031] In some preferred embodiments, after calculating the attribute values ​​of all candidate tasks under the current threshold strategy, if there are multiple tasks with the same extreme value in the predetermined comparison direction among all attribute values ​​under the current threshold strategy (i.e., parallel extreme values), then the initial benchmark value and the corresponding benchmark task are determined according to the unique identifier.

[0032] Understandably, for scenarios where multiple tasks have the same attribute values ​​(parallel extreme values), explicit disambiguation rules are provided to avoid the uncertainty of the benchmark task caused by random selection. This ensures that in multi-round computation or distributed environments, the same benchmark task is always selected when faced with the same input, eliminating the risk of benchmark drift.

[0033] Step S2: Update the first candidate task set based on all selected threshold strategies to remove some tasks, while saving the initial baseline value before the update as the old baseline value.

[0034] Optionally, if the selected threshold strategy includes multiple threshold strategies, then each selected threshold strategy is used as the current threshold strategy to perform the update and subsequent steps S3-S5.

[0035] It should be noted that updating the first candidate task set involves updating the task schedule according to the initial baseline. Some tasks selected and dispatched become departure tasks. The remaining tasks in the first candidate task set are then treated as non-departure tasks and, together with the subsequent additional task set, form the second candidate task set for the new round. A snapshot of the "current baseline value and baseline tasks" is saved completely before subsequent updates are performed; this clearly distinguishes between the "old state" and the "new state," allowing for accurate determination in later steps whether the changes in this round of updates were caused by newly added tasks or by the departure of baseline tasks.

[0036] In some alternative implementations, the initial baseline values ​​and corresponding baseline task identifiers before the first candidate task set is updated are saved as the old baseline state using any one of the following methods: memory mirroring, version number snapshot, or transaction log.

[0037] Understandably, among the various old benchmark snapshot methods mentioned above, memory mirroring has the lowest latency, version number snapshots are more suitable for distributed consistency, and transaction logs are more conducive to auditing and traceability.

[0038] Step S3: Generate a second candidate task set based on the non-leaving tasks and the additional task set of the first candidate task set, and calculate the extreme value of the comparison parameter of the additional task set under the current threshold strategy as the additional local extreme value.

[0039] It should be noted that after the baseline value is determined, the scheduling of the first candidate task set will be updated. After the update, some tasks in the original first candidate task set will be removed due to scheduling issues. Therefore, when conducting the second round after the update, it is necessary to supplement the original task set with additional tasks. At this time, the remaining tasks in the first candidate task set will be combined with the additional task set to form a new second candidate task set for the second round.

[0040] The above step S3 specifically includes: Step S3a: Generate a second candidate task set based on the non-leaving tasks and the additional task set of the first candidate task set.

[0041] Step S3b: After calculating the attribute values ​​of all candidate tasks in the additional task set under the current threshold policy, select the extreme value in the predetermined comparison direction among all attribute values ​​as the additional local extreme value.

[0042] Step S4: Determine whether adding a local extremum satisfies the condition of replacing the old benchmark value based on the current threshold strategy.

[0043] Specifically, the comparison direction corresponding to the current threshold strategy is obtained, and the relationship between the additional local extremum and the old benchmark value is determined based on the comparison direction.

[0044] It is understandable that the above implementation supports different comparison directions under different threshold strategies (such as maximum value priority or minimum value priority), enabling the method to adapt to diverse scheduling business scenarios. Furthermore, abstracting the judgment of replacement conditions into a merit-based judgment based on "comparison direction" enhances the algorithm's versatility and scalability.

[0045] Optionally, if the strategy is priority maximization, then it is determined whether the additional local extremum is greater than the old benchmark value. If the strategy is cost minimization, then it is determined whether the additional local extremum is less than the old benchmark value.

[0046] The specific judgment includes the following steps: Case 1: If the addition of a local extremum satisfies the replacement condition, then the addition of the local extremum is used as the current baseline value of the second candidate task set, and the task identifier corresponding to the addition of the local extremum is used as the baseline task identifier.

[0047] It is worth noting that if the added local extremum satisfies the replacement condition, then the added local extremum is used as the current benchmark value of the second candidate task set. At this time, the benchmark task identifier is also updated to the task identifier in the added set that generated the extremum. Under this path, there is no need to care about the updated departing tasks, because the new benchmark has been established by the added tasks. In the above embodiments of this application, the comparison scope is narrowed from the second candidate task set to the added task set. This makes it possible for most rounds to complete the benchmark value update only within the size of the added task set, reducing the average single-round latency and reducing unnecessary full candidate task set scanning.

[0048] Case 2: If adding a local extreme value does not meet the replacement condition, then the current baseline value and baseline task identifier of the second candidate task set are re-determined based on the old baseline task status corresponding to the old baseline value.

[0049] Understandably, if adding a local extremum does not satisfy the replacement condition, it means that the new task has not exceeded the old benchmark. In this case, whether the current benchmark value changes depends on whether the old benchmark task is still present. This step redetermines the current benchmark value of the second candidate task set based on the state of the old benchmark task corresponding to the old benchmark value.

[0050] In some specific implementations, situation two above includes: Step A: Obtain the old baseline task identifier corresponding to the old baseline value and the task identifier of the current round of departure task.

[0051] Understandably, an exit task refers to a task that is selected or removed from the first set of candidate tasks.

[0052] Step B: Determine whether the task identifier of the departure task is consistent with the old baseline task identifier. It is worth noting that this application avoids the cost of recalculation by comparing task identifiers.

[0053] Specifically, step B includes the following situations: Scenario 1 (Non-Benchmark Departure): If the task identifier of the departing task is inconsistent with the old benchmark task identifier, it means that the candidate task holding the old benchmark value is still in the second candidate task set. In this case, the old benchmark value is directly used as the current benchmark value, and the benchmark task identifier remains unchanged.

[0054] It is worth noting that in this scenario, no recalculation is required, significantly reducing CPU consumption. This application simplifies complex operations through task identifier recognition.

[0055] Optionally, the above-mentioned task identifier identification method can adopt any one of explicit write-back, lazy non-write-back, and status flag write-back.

[0056] Understandably, among the various methods mentioned above, explicit write-back offers higher readability and better auditability, lazy write-back reduces assignment overhead, and status flag write-back facilitates integration with the event bus. Designers can choose the appropriate identification method based on their actual needs.

[0057] Scenario 2 (Benchmark Departure): If the task identifier of the departing task matches the old benchmark task identifier, it means that the task holding the old benchmark value has been removed, and the old benchmark is invalid. In this case, the extreme value of the comparison parameter needs to be recalculated from the non-departing task set and the additional task set in the second candidate task set as the current benchmark value.

[0058] Understandably, limiting recalculation to necessary scenarios where the baseline is removed can control the frequency of high-cost computations while ensuring correctness. Compared to forced recalculation in each round, this solution avoids a full scan of the complete candidate task set in most rounds; compared to methods lacking a removal and reselection mechanism, this solution avoids ranking bias caused by the long-term retention of expired baselines.

[0059] Furthermore, the current benchmark value established in step S4 will be used as the input benchmark for the next round.

[0060] The above embodiments implement a "three-path update" mechanism through the above steps: 1. If the added task directly exceeds the old baseline value, the baseline value is quickly updated using the extreme value of the added task. 2. If the added task does not exceed the old baseline value and the old baseline task has not left the system, the old baseline value is maintained. 3. If the added task does not exceed the old baseline value and the old baseline task has left the system, a recalculation is triggered.

[0061] Understandably, compared to the naive approach of recalculating the extreme value of all candidates after each change in the prior art, this embodiment concentrates the worst-case complexity in a few necessary scenarios (baseline exit), which greatly reduces the average scan overhead.

[0062] In some preferred embodiments, the adaptive update method for process sorting benchmark values ​​of this application further includes: Step S5: Determine whether the baseline value or the baseline task identifier has changed in this round, and generate a baseline change signal.

[0063] Understandably, this signal can be output to subsequent affected decision modules or incremental rearrangement modules as a basis for whether to trigger downstream calculations, thereby reducing the cost of repeated decision-making between upstream and downstream, reducing missed triggers and false triggers, and improving the consistency of the incremental rearrangement link.

[0064] In summary, this invention optimizes benchmark maintenance from frequent full traversal to a three-path update mode that triggers recalculation only when a better benchmark is added or the benchmark holder leaves, by constructing an adaptive update mechanism of "additional priority determination + old benchmark state diversion". This significantly reduces the scanning cost and CPU consumption during the dynamic candidate set evolution process. At the same time, the introduction of a deterministic disambiguation strategy and an old benchmark snapshot mechanism effectively solves the benchmark drift problem in parallel extreme value scenarios, improving the consistency and reproducibility of scheduling results. In addition, by explicitly distinguishing benchmark replacement and task departure scenarios and outputting standardized change signals, the risk of state inconsistency is eliminated, the interface coupling with the downstream incremental rescheduling module is reduced, and engineering implementation, auditing, and fault location are facilitated. Thus, while ensuring the correct execution of the scheduling strategy, both efficiency and stability are improved.

[0065] Secondly, embodiments of this application also provide an adaptive update system for process sequencing benchmark values. The adaptive update system for process sequencing benchmark values ​​includes: a benchmark initialization module, a set update and snapshot module, a local extremum calculation module, and a benchmark update module. The benchmark initialization module is used to obtain a first candidate task set for the current round, and calculate the extreme value of the comparison parameters of all tasks in the first candidate task set under each threshold strategy as an initial benchmark value, using the task identifier corresponding to the extreme value in the comparison parameters as the benchmark task identifier. The set update and snapshot module is used to determine the departing tasks that leave the first candidate task set after the update, generate a second candidate task set based on the non-departing tasks and the additional task set of the first candidate task set, and save the initial benchmark value before the update as the old benchmark value. The local extremum calculation module is used to calculate the extreme value of the comparison parameters of the additional task set under the current threshold strategy as the additional local extremum. The benchmark update module is used to determine whether the additional local extremum meets the condition for replacing the old benchmark value based on the current threshold strategy, and determine the current benchmark value of the second candidate task set based on the determination result. If the added local extremum satisfies the replacement condition, then the added local extremum is used as the current benchmark value of the second candidate task set, and the task identifier corresponding to the added local extremum is used as the benchmark task identifier. If the additional local extremum does not meet the replacement condition, the current benchmark value and benchmark task identifier of the second candidate task set are re-determined based on the old benchmark task state corresponding to the old benchmark value.

[0066] The functions of each module in the above-mentioned adaptive update system for process sorting benchmark values ​​correspond to the steps in the above-mentioned adaptive update method embodiment for process sorting benchmark values. Their functions and implementation processes will not be described in detail here.

[0067] Thirdly, embodiments of this application provide an adaptive update device for process sorting benchmark values. The adaptive update device for process sorting benchmark values ​​can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0068] Reference Figure 2 , Figure 2 This is a schematic diagram of the hardware structure of the adaptive update device for process sequencing benchmark values ​​involved in the embodiments of this application. In the embodiments of this application, the adaptive update device for process sequencing benchmark values ​​may include a processor, a memory, a communication interface, and a communication bus.

[0069] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0070] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the adaptive update device for process sequencing baseline values, as well as interfaces used for interconnecting the device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0071] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0072] The processor can be a general-purpose processor, which can call the adaptive update program for process sequencing baseline values ​​stored in memory and execute the adaptive update method for process sequencing baseline values ​​provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the adaptive update program for process sequencing baseline values ​​is called can be referred to in various embodiments of the adaptive update method for process sequencing baseline values ​​of this application, and will not be repeated here.

[0073] Those skilled in the art will understand that Figure 2The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0074] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0075] The present application stores an adaptive update program for process sorting benchmark values ​​on a computer-readable storage medium, wherein when the adaptive update program for process sorting benchmark values ​​is executed by a processor, it implements the steps of the adaptive update method for process sorting benchmark values ​​as described above.

[0076] The method implemented when the process sequencing benchmark value adaptive update program is executed can be referred to in various embodiments of the process sequencing benchmark value adaptive update method of this application, and will not be repeated here.

[0077] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0078] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0079] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0080] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0081] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0082] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0083] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for adaptively updating process sequencing benchmark values, characterized in that, The adaptive update method for the process sequencing benchmark value includes: Obtain the first candidate task set for the current round, and calculate the extreme value of the comparison parameter of all tasks in the first candidate task set under each threshold policy as the initial benchmark value. Use the task identifier corresponding to the extreme value in the comparison parameter as the benchmark task identifier. The comparison parameter is the attribute value of each candidate task extracted from the first candidate task set under the current threshold policy. The first candidate task set is updated based on all selected threshold strategies to remove some tasks, while the initial baseline value before the update is saved as the old baseline value. A second candidate task set is generated based on the non-leaving tasks and the additional task set of the first candidate task set, and the extreme value of the comparison parameter of the additional task set under the current threshold policy is calculated as the additional local extreme value. Based on the current threshold strategy, it is determined whether adding a local extremum satisfies the condition of replacing the old baseline value, and the current baseline value of the second candidate task set is determined according to the judgment result; where, If the added local extremum satisfies the replacement condition, then the added local extremum is used as the current baseline value of the second candidate task set, and the task identifier corresponding to the added local extremum is used as the baseline task identifier. If the addition of a local extremum does not meet the replacement condition, the current benchmark value and benchmark task identifier of the second candidate task set shall be re-determined according to the old benchmark task state corresponding to the old benchmark value. The step of re-determining the current benchmark value of the second candidate task set based on the old benchmark task status corresponding to the old benchmark value includes: obtaining the old benchmark task identifier corresponding to the old benchmark value; determining whether the task identifier of the departing task is consistent with the old benchmark task identifier; wherein, if the task identifier of the departing task is inconsistent with the old benchmark task identifier, the old benchmark value is used as the current benchmark value; if the task identifier of the departing task is consistent with the old benchmark task identifier, the extreme value of the comparison parameter of the second candidate task set is recalculated as the current benchmark value. The step of determining whether adding a local extremum satisfies the condition of replacing the old benchmark value based on the current threshold strategy includes: obtaining the comparison direction corresponding to the current threshold strategy, and determining the size relationship between the added local extremum and the old benchmark value based on the comparison direction.

2. The adaptive update method for process sequencing benchmark values ​​as described in claim 1, characterized in that, The calculation of the extreme values ​​among the comparison parameters of all tasks in the first candidate task set under each threshold strategy as the initial benchmark value includes: Sort all candidate tasks in the first candidate task set by unique identifiers; Calculate the attribute values ​​of all candidate tasks in the first candidate task set under each threshold policy, and select the extreme value in the predetermined comparison direction among all attribute values ​​under the current threshold policy as the initial benchmark value.

3. The adaptive update method for process sequencing benchmark values ​​as described in claim 2, characterized in that, The step of selecting the extreme value in the predetermined comparison direction among all attribute values ​​under the current threshold strategy as the initial benchmark value also includes: If there are parallel extreme values ​​among all attribute values, the initial baseline value and the corresponding baseline task are determined by sorting them according to the unique identifier.

4. The adaptive update method for process sequencing benchmark values ​​as described in claim 2, characterized in that, The step of uniquely identifying and sorting all candidate tasks in the first candidate task set includes: All candidate tasks are uniquely identified and sorted using predetermined stable rules, which include: ID dictionary order, timestamp order, business priority label sorting, and composite key sorting.

5. The adaptive update method for process sequencing benchmark values ​​as described in claim 1, characterized in that, The step of saving the initial baseline value before the update as the old baseline value includes: The initial baseline values ​​and corresponding baseline task identifiers before the first candidate task set is updated are saved using any one of the following methods: memory image, version number snapshot, or transaction log.

6. The adaptive update method for process sequencing benchmark values ​​as described in claim 1, characterized in that, After determining the current baseline value of the second candidate task set based on the judgment result, the method further includes: We analyze the changes in baseline values ​​and baseline task identifiers after this round of updates to generate baseline change signals.

7. A process sequencing benchmark adaptive update system for executing the process sequencing benchmark adaptive update method as described in claim 1, characterized in that, The adaptive update system for process sequencing benchmark values ​​includes: The baseline initialization module is used to obtain the first candidate task set of the current round, and calculate the extreme value of the comparison parameters of all tasks in the first candidate task set under each threshold policy as the initial baseline value, and use the task identifier corresponding to the extreme value in the comparison parameters as the baseline task identifier. The set update and snapshot module is used to determine the departing tasks that leave the first candidate task set after the update, generate a second candidate task set based on the non-departing tasks and the appended task set of the first candidate task set, and save the initial baseline value before the update as the old baseline value. The local extremum calculation module is used to calculate the extreme values ​​of the comparison parameters of the additional task set under the current threshold policy as additional local extremums. The benchmark update module is used to determine whether adding a local extremum satisfies the condition of replacing the old benchmark value based on the current threshold strategy, and to determine the current benchmark value of the second candidate task set based on the judgment result; wherein, If the added local extremum satisfies the replacement condition, then the added local extremum is used as the current benchmark value of the second candidate task set, and the task identifier corresponding to the added local extremum is used as the benchmark task identifier. If the additional local extremum does not meet the replacement condition, the current benchmark value and benchmark task identifier of the second candidate task set are re-determined based on the old benchmark task state corresponding to the old benchmark value.

8. A process sequencing baseline value adaptive update device, characterized in that, The process sorting baseline value adaptive update device includes a processor, a memory, and a process sorting baseline value adaptive update program stored in the memory and executable by the processor, wherein when the process sorting baseline value adaptive update program is executed by the processor, it implements the steps of the process sorting baseline value adaptive update method as described in any one of claims 1 to 6.

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