Reservation success rate statistical analysis method and device, equipment and storage medium

Abstract

The present application relates to the technical field of data statistics, and particularly relates to a reservation success rate statistical analysis method, device, equipment and storage medium, the method obtains machine running data through a preset statistical time period, after preprocessing, sequentially passes through four times progressive screening through reservation rules, sharing rules, emergency conditions and bottleneck machine table list, respectively obtains first, second, third and fourth success rate counts, and calculates total reservation success rate based on the four success rate counts; the method forms a standardized statistical process from data collection, priority screening to success rate quantification, realizes objective and quantifiable evaluation of the effectiveness of the reservation strategy, solves the problems of non-standard reservation success rate statistics, low optimization efficiency and insufficient static rule adaptability in the prior art, and improves the reliability of high-priority batch reservation; and by directly associating reservation optimization information with real-time production data, the accuracy of failure cause positioning and optimization iteration efficiency are significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data statistics, and particularly relates to a reservation success rate statistical analysis method and device, equipment and a storage medium. BACKGROUND

[0002] In a semiconductor manufacturing and automation control system, a high-priority BulletLot generally refers to a production lot with high priority, including lots with priority levels of 0 (P0) and 1 (P1). After a P0-level lot is processed at a previous processing station, the current station needs to reserve the entire machine resource in an idle waiting state, i.e., idle machine waiting. After a P1-level lot is processed at a previous processing station, the current station needs to reserve the load port (LoadPort) of the machine in an idle waiting state, i.e., idle LoadPort waiting.

[0003] BulletLots are mostly used for critical process nodes or emergency order production, and their rapid flow between machines has a decisive influence on overall production efficiency and delivery cycle. If the reservation operation of idle machine or idle LoadPort fails, it will directly cause the flow delay of high-priority lots, and further cause production scheduling conflicts.

[0004] The prior art has the following deficiencies: first, there is a lack of standardized statistical method for reservation success rate of high-priority lots, which cannot objectively evaluate the effectiveness of the reservation strategy; second, the cause analysis of reservation failure relies on manual investigation, and no correlation mechanism is established between production data such as device load, processing queue, priority parameters, etc., resulting in low optimization iteration efficiency; third, the priority determination logic has insufficient dynamic adaptability to real-time production state, and traditional static reservation rules are difficult to cope with complex machine resource competition scenarios; the foregoing problems cause the reservation success rate of high-priority lots to fluctuate greatly, which seriously restricts the response capability of the automation control system to emergency tasks.

[0005] It can be seen that the prior art still needs to be improved and enhanced. SUMMARY

[0006] In order to overcome the deficiencies of the prior art, the purpose of the present application is to provide a reservation success rate statistical analysis method, which solves the problems of non-standard reservation success rate statistics, low optimization efficiency, and insufficient adaptability of static rules in the prior art, and improves the reliability of high-priority lot reservation and the overall efficiency of production scheduling.

[0007] The first aspect of the present application provides a method for statistical analysis of reservation success rate, comprising: obtaining and preprocessing machine running data based on a preset statistical time period to obtain preprocessed running information; obtaining a reservation rule corresponding to a high-priority batch, performing a first screening on the preprocessed running information based on the reservation rule to obtain a first success rate count and first to-be-confirmed information; obtaining a sharing rule corresponding to the high-priority batch, performing a second screening on the first to-be-confirmed information based on the sharing rule to obtain a second success rate count and second to-be-confirmed information; obtaining a preset emergency condition, performing a third screening on the second to-be-confirmed information based on the emergency condition to obtain a third success rate count and third to-be-confirmed information; obtaining a pre-constructed bottleneck machine table, performing a fourth screening on the third to-be-confirmed information based on the bottleneck machine table to obtain a fourth success rate count and final failure information; calculating a total reservation success rate based on the first success rate count, the second success rate count, the third success rate count and the fourth success rate count, and generating reservation optimization information based on the calculated total reservation success rate and the final failure information.

[0008] Optionally, in the first implementation manner of the first aspect of the present application, the obtaining and preprocessing of the machine running data based on the preset statistical time period to obtain the preprocessed running information comprises: obtaining machine running data based on the preset statistical time period, wherein the machine running data comprises machine information and a plurality of batch information, the machine information comprises a total number of loading ports, and each batch information comprises a priority, a start processing time, an end processing time, an actual arrival time and a remaining queuing time; arranging each batch information in the machine running data in ascending order according to the end processing time to obtain sorted running data; and performing screening processing on the sorted running data according to the priority to retain batch information with a priority belonging to level 0 and level 1, thereby obtaining the preprocessed running information.

[0009] Optionally, in the second implementation form of the first aspect of the present application, the step of obtaining the reservation rule corresponding to the high-priority batch, and performing a first screening on the pre-processed processing information based on the reservation rule to obtain the first success rate count and the first to-be-confirmed information comprises: obtaining the reservation rule corresponding to the high-priority batch, wherein the reservation rule comprises a 0-level reservation rule and a 1-level reservation rule; setting an initial value of the first success rate count as 0, and constructing an initially empty first list for recording the first to-be-confirmed information; when the priority of the batch information is 0 level, if the start processing time of the current batch information > the end processing time of the last batch information on the machine, the first success rate count is increased by 1; if the start processing time of the current batch information ≤ the end processing time of the last batch information on the machine, the current batch information is loaded into the first list; when the priority of the batch information is 1 level, the arrival correction time of the current batch information is calculated according to the preset buffer time and the actual arrival time, and the in-process batch data corresponding to the arrival correction time is obtained, wherein the in-process batch data comprises the in-process batch quantity and the in-process batch information corresponding to the in-process batch quantity; if the total number of loading ports > the number of processing batches, the first success rate count is increased by 1; if the total number of loading ports = the number of processing batches, the current batch information is loaded into the first list.

[0010] Optionally, in the third implementation form of the first aspect of the present application, the step of obtaining the sharing rule corresponding to the high-priority batch, and performing a second screening on the first to-be-confirmed information based on the sharing rule to obtain the second success rate count and the second to-be-confirmed information comprises: obtaining the sharing rule corresponding to the high-priority batch, wherein the sharing rule comprises a 0-level sharing rule and a 1-level sharing rule; setting an initial value of the second success rate count as 0, and constructing an initially empty second list for recording the second to-be-confirmed information; obtaining the in-process batch data corresponding to the current batch information based on the arrival correction time of the current batch information; when the priority of the current batch information is 0 level, if there is in-process batch information with a 0-level priority in the in-process batch data, the second success rate count is increased by 1; if there is no in-process batch information with a 0-level priority in the in-process batch data, the current batch information is loaded into the second list; when the priority of the current batch information is 1 level, if there is in-process batch information with a 0-level or 1-level priority in the in-process batch data, the second success rate count is increased by 1; if there is no in-process batch information with a 0-level or 1-level priority in the in-process batch data, the current batch information is loaded into the second list.

[0011] Optionally, in the fourth implementation form of the first aspect of the present application, the obtaining of the preset emergency condition, the three times of screening of the second to-be-confirmed information based on the emergency condition, and the obtaining of the third success rate count and the third to-be-confirmed information comprise: obtaining a preset emergency condition, the preset emergency condition being a preset emergency threshold; setting an initial value of the third success rate count as 0, and constructing an initially empty third list for recording the third to-be-confirmed information; obtaining in-process batch data corresponding to the current batch information based on the arrival time of the current batch information; if there is any in-process batch information with a remaining queuing time < the preset emergency threshold in the obtained in-process batch data, the third success rate count is incremented by 1; and if all in-process batch information has a remaining queuing time ≥ the preset emergency threshold in the obtained in-process batch data, the current batch information is loaded into the third list.

[0012] Optionally, in the fifth implementation form of the first aspect of the present application, the obtaining of the pre-constructed bottleneck machine list, the four times of screening of the third to-be-confirmed information based on the bottleneck machine list, and the obtaining of the fourth success rate count and the final failure information comprise: setting an initial value of the fourth success rate count as 0, and constructing an initially empty fourth list for recording the final failure information; obtaining the pre-constructed bottleneck machine list based on the current batch information, and comparing the machine information and the pre-constructed bottleneck machine list; if the comparison result shows that the current machine is a bottleneck machine, the fourth success rate count is incremented by 1; and if the comparison result shows that the current machine is not a bottleneck machine, the current batch information is loaded into the fourth list.

[0013] Optionally, in the sixth implementation form of the first aspect of the present application, the total reserved success rate based on the first success rate count, the second success rate count, the third success rate count and the fourth success rate count, and the generation of the reserved optimization information based on the calculated total reserved success rate and the final failure information comprise: the total reserved success rate based on the first success rate count, the second success rate count, the third success rate count and the fourth success rate count; the location of the reserved failure reason based on each batch information in the final failure information; the generation of the optimization improvement strategy according to the reserved failure reason, and the generation of the reserved optimization information in combination with the generated optimization improvement strategy and the calculated total reserved success rate.

[0014] The second aspect of the present application provides a reservation success rate statistical analysis device, comprising: an acquisition module, configured to acquire machine running data and perform preprocessing based on a preset statistical time period to obtain preprocessed running information; a first screening module, configured to acquire a reservation rule corresponding to a high-priority batch, perform first screening on the preprocessed running information based on the reservation rule, and obtain a first success rate count and first to-be-confirmed information; a second screening module, configured to acquire a sharing rule corresponding to the high-priority batch, perform second screening on the first to-be-confirmed information based on the sharing rule, and obtain a second success rate count and second to-be-confirmed information; a third screening module, configured to acquire a preset emergency condition, perform third screening on the second to-be-confirmed information based on the emergency condition, and obtain a third success rate count and third to-be-confirmed information; a fourth screening module, configured to acquire a pre-constructed bottleneck machine table, perform fourth screening on the third to-be-confirmed information based on the bottleneck machine table, and obtain a fourth success rate count and final failure information; and a generation module, configured to calculate a total reservation success rate based on the first success rate count, the second success rate count, the third success rate count and the fourth success rate count, and generate reservation optimization information based on the calculated total reservation success rate and the final failure information.

[0015] The third aspect of the present application provides a reservation success rate statistical analysis device, comprising: a memory and at least one processor, wherein the memory stores instructions; and the at least one processor invokes the instructions in the memory to enable the reservation success rate statistical analysis device to perform each step of the reservation success rate statistical analysis method described in any of the above aspects.

[0016] The fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and the instructions are executed by a processor to implement each step of the reservation success rate statistical analysis method described in any of the above aspects.

[0017] In the technical solution of the present application, machine running data is acquired based on a preset statistical time period, and after 0-level and 1-level high-priority batches are screened out through preprocessing, four progressive screenings are performed in turn based on a reservation rule, a sharing rule, an emergency condition and a bottleneck machine table, to obtain first, second, third and fourth success rate counts respectively, and a total reservation success rate is calculated based on the four success rate counts; the method forms a standardized statistical process from data acquisition, priority screening to success rate quantification, realizes objective and quantifiable evaluation of the effectiveness of a reservation strategy, solves the problems of non-standard reservation success rate statistics, low optimization efficiency and insufficient adaptability of static rules in the prior art, and improves the reliability of high-priority batch reservation; and by directly associating reservation optimization information with real-time production data, manual investigation is replaced, and the accuracy of failure cause positioning and the optimization iteration efficiency are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A logic flowchart of a reservation success rate statistical analysis method provided for an embodiment of the present application is shown in FIG. 1.

[0019] Figure 2 A structural schematic diagram of a reservation success rate statistical analysis device provided for an embodiment of the present application is shown in FIG. 2.

[0020] Figure 3 A structural schematic diagram of a reservation success rate statistical analysis device provided for an embodiment of the present application is shown in FIG. 2. DETAILED DESCRIPTION

[0021] The present application provides a reservation success rate statistical analysis method, device, equipment and storage medium. In the present application, the terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily mean a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0022] For the sake of understanding, the specific flow of the embodiments of the present application is described below. Please refer to FIG. 1 Figure 1 One embodiment of the reservation success rate statistical analysis method in the embodiments of the present application includes:

[0023] 101, based on a preset statistical time period, obtain machine running data and preprocess to obtain preprocessed running information;

[0024] In this embodiment, based on a preset statistical time period, such as 1 day or 1 week, machine running data is obtained and preprocessed to obtain valid information containing only high-priority batches within the statistical time period.

[0025] 102, obtain a reservation rule corresponding to the high-priority batch, and perform a first screening on the preprocessed running information based on the reservation rule to obtain a first success rate count and first to-be-confirmed information;

[0026] In this embodiment, the first screening is performed based on a preset reservation rule to count the number of initial successes that meet the basic reservation requirements to obtain a first success rate count; the remaining batch information that does not meet the reservation rule is taken as to-be-confirmed information to enter the next layer.

[0027] 103、obtain a sharing rule corresponding to the high-priority batch, perform secondary screening on the first to-be-confirmed information based on the sharing rule, and obtain a second success rate count and second to-be-confirmed information;

[0028] In this embodiment, the second success rate count is obtained by performing secondary screening based on the preset sharing rule and counting the number of successes in the high-level Lot resource sharing scenario. The batch information that does not meet the sharing rule is taken as to-be-confirmed information for further circulation.

[0029] 104、obtain a preset emergency condition, perform tertiary screening on the second to-be-confirmed information based on the emergency condition, and obtain a third success rate count and third to-be-confirmed information;

[0030] In this embodiment, the third success rate count is obtained by performing tertiary screening based on the preset emergency condition and counting the number of successes in the emergency Lot resource occupation scenario. The to-be-confirmed range is further narrowed. The batch information that does not meet the emergency condition is taken as to-be-confirmed information for further circulation.

[0031] 105、obtain a pre-constructed bottleneck machine table, perform quaternary screening on the third to-be-confirmed information based on the bottleneck machine table, and obtain a fourth success rate count and final failure information;

[0032] In this embodiment, the fourth success rate count is obtained by performing quaternary screening based on the pre-constructed bottleneck machine table and counting the number of successes in the bottleneck machine resource scheduling scenario. The final failure information is determined. The four layers of screening are in a progressive relationship. The to-be-confirmed information of a previous layer of screening is input data of a subsequent layer of screening. It is ensured that special scenarios that are not covered by previous rules are gradually investigated, and the actual resource scheduling logic of the semiconductor automation production is fitted.

[0033] 106、total the first success rate count, the second success rate count, the third success rate count, and the fourth success rate count to obtain a total reserved success rate, and generate reserved optimization information based on the calculated total reserved success rate and the final failure information.

[0034] The application discloses a reservation success rate statistical analysis method, which obtains machine running data through a preset statistical time period, filters out 0-level and 1-level high-priority batches after preprocessing, and then sequentially performs four times of progressive filtering through a reservation rule, a sharing rule, an emergency condition and a bottleneck machine table list to obtain first, second, third and fourth success rate counts respectively, and calculates a total reservation success rate based on the four success rate counts; the method forms a standardized statistical process from data collection, priority filtering to success rate quantification, realizes objective and quantifiable evaluation of the effectiveness of a reservation strategy, solves the problems of non-standard reservation success rate statistics, low optimization efficiency and insufficient static rule adaptability in the prior art, and improves the reliability of high-priority batch reservation; and by directly associating reservation optimization information with real-time production data, manual investigation is replaced, and the accuracy of failure cause positioning and the optimization iteration efficiency are significantly improved.

[0035] In the embodiment of the application, the machine running data is obtained based on a preset statistical time period and is preprocessed to obtain preprocessed running information, including:

[0036] 201. Obtain machine running data based on a preset statistical time period, wherein the machine running data comprises machine information and a plurality of batch information, the machine information comprises a total number of loading ports, and each batch information comprises a priority, a start processing time, an end processing time, an actual arrival time and a remaining queuing time;

[0037] In this embodiment, the machine information further comprises a machine number, and the batch information further comprises a batch ID; the total number of loading ports in the machine information is a key parameter for subsequent 1-level priority batch empty loading port judgment; the start processing time and the end processing time in the batch information are used to judge whether the machine is idle, the actual arrival time is used to calculate the arrival correction time of the 1-level priority batch, and the remaining queuing time is used to judge whether there is an emergency batch.

[0038] 202. Arrange each batch information in the machine running data in ascending order according to the end processing time to obtain sorted running data;

[0039] In this embodiment, sorting a plurality of batch information according to the end processing time can ensure that when subsequent 0-level priority batch judgment is performed, i.e., when it is judged whether the machine is idle, the start time of the current batch and the end time of the previous batch can be accurately compared, and judgment errors caused by time sequence disorder are avoided.

[0040] 203. Filter and process the sorted running data according to the priority, and retain batch information with a priority belonging to 0-level and 1-level to obtain preprocessed running information;

[0041] In the embodiment, screening is performed based on priority, only batch information with priority of 0 and 1 is reserved, and finally pre-processing running information is obtained; irrelevant batches are filtered in advance, the processing amount of subsequent screening links is reduced, and the scene demand of a large amount of running data in semiconductor production is adapted; and through sorting processing cooperating with screening processing, invalid data with low priority and time confusion can be removed, high-quality input is provided for subsequent four-layer screening, and statistical error is reduced from the source.

[0042] In the embodiment of the application, the pre-processing running information is screened based on the reservation rule corresponding to the high-priority batch to obtain the first success rate count and the first to-be-confirmed information, and the method comprises the following steps:

[0043] 301. Obtain a reservation rule corresponding to a high-priority batch, wherein the reservation rule comprises a 0-level reservation rule and a 1-level reservation rule.

[0044] In the embodiment, different judgment logics, i.e., different reservation rules, are designed for different requirements of the 0-level priority batch and the 1-level priority batch, i.e., the 0-level priority batch needs to be empty and the 1-level priority batch needs to be empty at a LoadPort, so as to avoid statistical deviation of a traditional method and improve the accuracy and scene adaptability of high-priority batch reservation judgment.

[0045] 302. Set the initial value of the first success rate count as 0, and construct an initial empty first list for recording the first to-be-confirmed information.

[0046] In the embodiment, batch information that does not meet the basic reservation requirement is stored in the first list, so as to provide a clear screening object for subsequent special scene investigation.

[0047] 303. When the priority of the batch information is 0, if the start processing time of the current batch information > the end processing time of the last batch information on the machine, the first success rate count is increased by 1; if the start processing time of the current batch information <= the end processing time of the last batch information on the machine, the current batch information is loaded into the first list.

[0048] In the embodiment, for the P0 batch, i.e., the batch with a priority of 0: if the start processing time (TrackinTime) of the current batch > the end processing time (TrackoutTime) of the last batch, it is indicated that the machine has no other Lot processing, i.e., the machine is empty and succeeds, at this time, the first success rate count is increased by 1; otherwise, the batch is stored in the first list.

[0049] 304. When the priority of the batch information is level 1, calculate the arrival correction time of the current batch information according to the preset buffer time and the actual arrival time, and obtain the in-process batch data corresponding to the arrival correction time. The in-process batch data includes the number of in-process batches and the in-process batch information corresponding to the number of in-process batches.

[0050] 305. If the total number of loading ports is greater than the number of processing batches, the first success rate count is incremented by 1; if the total number of loading ports equals the number of processing batches, the current batch information is loaded into the first list.

[0051] In this embodiment, for batch P1, i.e., batch with priority level 1, the arrival correction time T0 is first calculated based on the actual arrival time (ArriveTime) + the preset buffer time T1. Then, the total number of loading ports n of the machine is obtained, and the number of batches in progress m of the machine at time T0 is counted. If n > m, it indicates that there is an empty LoadPort, and the first success rate count is incremented by 1. If n = m, it indicates that there is no empty LoadPort, and the current batch information is stored in the first list. The number of batches in progress m needs to traverse all Lots being processed by the machine at time T0. If the TrackinTime of a Lot is less than T0 and less than TrackoutTime, it is counted in m to ensure that the LoadPort occupancy status at time T0 is accurately reflected. In addition, since there may be a short scheduling delay after the Lot actually arrives, the buffer time T1 can be configured to 5 minutes to correct the actual arrival time, ensuring that the time benchmark for judging empty LoadPorts is more in line with the actual production rhythm and improving the accuracy of the first screening.

[0052] In this embodiment of the invention, the step of obtaining the shared rules corresponding to the high-priority batches, and performing a second filtering on the first information to be confirmed based on the shared rules to obtain a second success rate count and a second information to be confirmed, includes:

[0053] 401. Obtain the sharing rules corresponding to the high-priority batches, wherein the sharing rules include level 0 sharing rules and level 1 sharing rules;

[0054] In this embodiment, the core logic of the obtained sharing rule is as follows: In semiconductor production, higher-level Lots (P0 / P1) have higher priority than lower-level Lots, but Lots of the same or higher level (such as P0 having priority over P1) can share machines / LoadPorts without forcibly freeing up resources. Therefore, this scenario should not be judged as a reservation failure. Since the preset sharing rule is adapted to the resource sharing characteristics of higher-level Lots, it can solve the drawback of traditional statistics that judges failure as long as a machine or LoadPort is occupied, so that the statistical data conforms to the core logic of priority scheduling in actual production.

[0055] 402. Set the initial value of the second success rate count to 0, and construct an initially empty second list for recording the second information to be confirmed;

[0056] In this embodiment, the current batch information corresponding to the ongoing batch data is stored in the second list only when there are no high-level Lots in the ongoing batch data. This makes subsequent filtering more focused on the special cases of low-level Lot occupation, and further provides clear filtering objects for subsequent investigation of special scenarios.

[0057] 403. Based on the arrival correction time of the current batch information, obtain the ongoing batch data corresponding to the current batch information;

[0058] In this embodiment, the acquired batch data in progress includes multiple batch information entries, and the content of the batch information is consistent with the content of the batch information. The acquisition of the batch data in progress must be consistent with the batch data in progress at time T0 in a single screening process to ensure that the time base of the screening is consistent before and after, and to avoid judgment contradictions caused by differences in time dimensions.

[0059] 404. When the priority of the current batch information is level 0, if there is a batch information with priority level 0 in the batch data, the second success rate count is incremented by 1; if there is no batch information with priority level 0 in the batch data, the current batch information is loaded into the second list.

[0060] 405. When the priority of the current batch information is level 1, if there is a batch information with priority level 0 or 1 in the batch data, the second success rate count is incremented by 1; if there is no batch information with priority level 0 or 1 in the batch data, the current batch information is loaded into the second list.

[0061] In this embodiment, for batch P0, if there is a Lot with priority level 0 in the ongoing batch data, meaning that other P0s are occupying resources, it indicates that the machine is shared between higher levels, and the reservation is determined to be successful, with the second success rate count incremented by 1; otherwise, the current batch information is stored in the second list. For batch P1, if there is a Lot with priority level 0 or 1 in the ongoing batch data, meaning that P0 or P1 is occupying resources, it indicates that the LoadPort is shared between higher levels, and the reservation is determined to be successful, with the second success rate count incremented by 1; otherwise, the current batch information is stored in the second list. By supplementing the statistical scenarios where the basic reservation failed but can be determined to be successful due to sharing between higher levels, the omission of success counts is avoided, the statistical completeness is improved, and the success rate calculation is ensured to be more comprehensive.

[0062] In the embodiment of the present application, the preset emergency condition is obtained, and the second to-be-confirmed information is screened three times based on the emergency condition to obtain a third success rate count and third to-be-confirmed information, comprising:

[0063] 501. A preset emergency condition is obtained, and the preset emergency condition is a preset emergency threshold;

[0064] In the embodiment, the remaining queuing time (RemainQtime) is the estimated waiting time of the Lot from the current time to the start of processing. When RemainQtime < the preset emergency threshold, it indicates that the Lot needs to be processed urgently, and it is an Urgent Lot, which needs to occupy the machine or LoadPort preferentially. The preset emergency threshold can be set according to actual production needs, such as 2 hours.

[0065] 502. The initial value of the third success rate count is set to 0, and a third list for recording the third to-be-confirmed information is constructed, which is initially empty;

[0066] In the embodiment, the to-be-confirmed information stored in the third list needs to include the RemainQtime data of the in-process Lot, which provides a key basis for subsequent bottleneck machine screening of non-urgent low-level occupation. In addition, the batch information of the non-urgent low-level Lot is stored in the third list, so that the final screening is more focused on the influence of equipment characteristics.

[0067] 503. Based on the arrival correction time of the current batch information, the in-process batch data corresponding to the current batch information is obtained;

[0068] 504. If the remaining queuing time of any in-process batch information in the obtained in-process batch data is less than the preset emergency threshold, the third success rate count is incremented by 1;

[0069] 505. If the remaining queuing time of all in-process batch information in the obtained in-process batch data is greater than or equal to the preset emergency threshold, the current batch information is loaded into the third list;

[0070] In the embodiment, even if the in-process Lot is low-level, i.e., non-P0 or P1, as long as it belongs to the Urgent Lot, its production priority is higher than the reserved demand of the ordinary high-level Lot, so this scenario should not be determined as a reservation failure. Specifically, if there is any Lot in the in-process batch data whose remaining queuing time (RemainQtime) is less than the preset emergency threshold, it indicates that the Lot is an Urgent Lot and needs to be prioritized. At this time, it is determined that the reservation is successful, and the third success rate count is incremented by 1. If the RemainQtime of all in-process Lots is greater than or equal to the emergency threshold, the current batch information is stored in the third list.

[0071] In the embodiment, the dynamic production factor Urgent Lot is included in the statistics, the limitation of the traditional static reservation rule is broken, the flexibility and practicability of the statistical method are improved, the misjudgment of reservation failure caused by the occupation of resources by the Urgent Lot is prevented, and the success rate statistics conforms to the scheduling principle of the priority of the Urgent Lot in actual production.

[0072] In the embodiment, the pre-constructed bottleneck machine table is acquired, the third to-be-confirmed information is screened four times based on the bottleneck machine table, and fourth success rate counting and final failure information are obtained.

[0073] 601、Set the initial value of the fourth success rate counting as 0, and construct an initial empty fourth list for recording final failure information;

[0074] In the embodiment, the fourth list is used as a final reservation failure problem data set, contains batch information (LotID, priority, processing time), machine information (number, whether bottleneck), in-process Lot information (level, RemainQtime), provides complete data support for subsequent failure cause analysis, can be directly used for subsequent positioning causes such as blacklist, whether the Lot is in a suspended state, manual intervention, machine state, and the like, does not need additional manual data collection, and improves data processing efficiency.

[0075] 602、Based on the current batch information, a pre-constructed bottleneck machine table is acquired, and machine information is compared with the pre-constructed bottleneck machine table;

[0076] In the embodiment, the bottleneck machine table is pre-constructed by a production system based on factors such as equipment load rate, processing efficiency, and process importance, and can be dynamically updated, for example, if the load of a machine is increased after a failure in actual production, the machine can be added as a bottleneck machine; the bottleneck machine is a key node in the production process, and if it is forced to be empty or empty LoadPort, the overall production efficiency will be reduced, therefore, even if a non-urgent low-level Lot is occupied, it should not be judged as reservation failure; by including the equipment characteristic of the bottleneck machine in the statistics, statistical deviation caused by different equipment types is avoided, and the success rate calculation is ensured to be consistent with the actual resource scheduling logic.

[0077] 603、If the comparison result shows that the current machine is a bottleneck machine, the fourth success rate counting is increased by 1;

[0078] 604、If the comparison result shows that the current machine is not a bottleneck machine, the current batch information is loaded into the fourth list;

[0079] In the embodiment, it is judged whether the current machine is in the bottleneck machine list. If the current machine is in the bottleneck machine list, it is indicated that the machine is a bottleneck machine, and the bottleneck machine needs to be produced preferentially, and cannot be forced to be empty or empty LoadPort. At this time, it is judged that the reservation is successful, and the fourth success rate count is increased by 1. If the current machine is not in the bottleneck machine list, it is indicated that the machine corresponding to the current batch information is not a bottleneck machine. At this time, there is a reservation failure, and the current batch information is stored in the fourth list. Through four layers of screening, the batch occupied by the non-bottleneck machine and the non-emergency low-grade Lot is finally judged as a failure, and the core scene of accurately positioning the reservation problem is positioned.

[0080] In the embodiment of the application, the total reservation success rate is calculated based on the first success rate count, the second success rate count, the third success rate count and the fourth success rate count, and the reservation optimization information is generated based on the calculated total reservation success rate and the final failure information, which comprises:

[0081] 701, calculating the total reservation success rate based on the first success rate count, the second success rate count, the third success rate count and the fourth success rate count;

[0082] In the embodiment, the total reservation success rate is calculated based on the first to fourth success rate counts, and the calculation formula is: total reservation success rate=(first success rate count+second success rate count+third success rate count+fourth success rate count) / total statistical batch quantity, wherein the total statistical batch quantity=the sum of four success counts+the number of batch information in the fourth list; the total reservation success rate is a core index for measuring the effectiveness of the high-grade Lot reservation strategy, and can be compared according to a preset statistical time period to analyze the improvement effect of the reservation strategy.

[0083] 702, positioning the reservation failure reason based on each batch information in the final failure information;

[0084] In the embodiment, the final failure information in the fourth list is used to locate the reservation failure reason, and the main analysis direction is as follows:

[0085] 1. Blacklist investigation: combined with the site, machine and time information of the failed batch (Lot), it is verified whether the batch is listed in the unshippable blacklist; if the batch is in the blacklist, the batch cannot enter the reservation scheduling.

[0086] 2. Batch state investigation: verify whether the failed batch is in the hold state; the batch in the hold state will be excluded from the reservation scheduling list.

[0087] 3. Human intervention investigation: the machine shipment operation log is called to check whether there is a record of manually adjusting the shipment order, and unreasonable intervention may cause the high-grade batch to fail to be preferentially reserved.

[0088] 4. Machine state troubleshooting: Check the running state of the machine at the time of failure, confirm whether the machine has changed to a non-production state due to failure, maintenance, etc., and non-production state machines cannot perform reserved operations.

[0089] 703. Generate an optimization improvement strategy according to the reservation failure reason, and generate reservation optimization information in combination with the generated optimization improvement strategy and the calculated total reservation success rate;

[0090] In this embodiment, targeted optimization improvement strategies are generated according to the positioning of the reservation failure reason to improve the reservation success rate; specifically:

[0091] 1. Blacklist maintenance optimization: Construct a dynamic blacklist update mechanism, regularly clean up invalid records (such as Lot that has been restored to normal), reduce the risk of being mistakenly added to the blacklist, and ensure that high-level Lots can enter the schedule normally.

[0092] 2. Lot Hold rate control: Optimize the Lot quality detection process to reduce the Hold situation caused by unnecessary quality doubts; establish a Hold state approval mechanism to prevent arbitrary suspension of high-level Lots.

[0093] 3. Artificial intervention specification: Develop intervention criteria for high-level Lot delivery, clearly state that manual adjustment is only allowed in emergency failure scenarios, and keep intervention records for subsequent tracing.

[0094] 4. Machine state management: Establish a machine state early warning mechanism to notify maintenance plans in advance; optimize the fault response process to shorten the time required for the machine to recover from a non-production state.

[0095] In this embodiment, the reservation optimization information is generated in combination with the total reservation success rate and the optimization improvement strategy, and the generated reservation optimization information includes the total reservation success rate, the reservation failure reason and the optimization improvement strategy; by generating the reservation optimization information, a closed loop of statistics, analysis and optimization is formed: solving the limitation of only statistics without improvement in the prior art, providing a feasible optimization direction for production, promoting the continuous improvement of the reservation success rate, and improving the response ability of the automatic control system to high-level Lots.

[0096] The reservation success rate statistical analysis method in the embodiments of the present application is described above, and the reservation success rate statistical analysis device in the embodiments of the present application is described below. Please refer to Figure 2 An embodiment of the reservation success rate statistical analysis device in the embodiments of the present application includes:

[0097] The acquisition module 801 is configured to acquire machine running data based on a preset statistical time period and pre-process the machine running data to obtain pre-processed running information.

[0098] The first screening module 802 is configured to acquire a reservation rule corresponding to the high-priority batch, perform first screening on the preprocessed running information based on the reservation rule, and obtain a first success rate count and first to-be-confirmed information.

[0099] The second screening module 803 is configured to acquire a sharing rule corresponding to the high-priority batch, perform second screening on the first to-be-confirmed information based on the sharing rule, and obtain a second success rate count and second to-be-confirmed information.

[0100] The third screening module 804 is configured to acquire a preset emergency condition, perform third screening on the second to-be-confirmed information based on the emergency condition, and obtain a third success rate count and third to-be-confirmed information.

[0101] The fourth screening module 805 is configured to acquire a pre-constructed bottleneck machine table, perform fourth screening on the third to-be-confirmed information based on the bottleneck machine table, and obtain a fourth success rate count and final failure information.

[0102] The generating module 806 is configured to calculate a total reservation success rate based on the first success rate count, the second success rate count, the third success rate count, and the fourth success rate count, and generate reservation optimization information based on the calculated total reservation success rate and the final failure information.

[0103] Based on the same idea as the method in the above embodiment, the device provided by the present application can implement the method of the above embodiment.

[0104] The above Figure 2 The reservation success rate statistical analysis device in the embodiment of the present application is described in detail from the perspective of a modular functional entity, and the reservation success rate statistical analysis device in the embodiment of the present application is described in detail from the perspective of hardware processing.

[0105] Figure 3is a structural schematic view of a reservation success rate statistical analysis device provided by an embodiment of the present application. The reservation success rate statistical analysis device 900 can have great differences due to different configurations or performances, and can include one or more central processing units (CPUs) 910 (for example, one or more processors) and a memory 920, one or more storage media 930 (for example, one or more mass storage devices) storing application programs 933 or data 932. The memory 920 and the storage media 930 can be temporary storage or persistent storage. The programs stored in the storage media 930 can include one or more modules (not shown in the figure), and each module can include a series of instruction operations in the reservation success rate statistical analysis device 900. Further, the processor 910 can be configured to communicate with the storage media 930 and execute the series of instruction operations in the storage media 930 on the reservation success rate statistical analysis device 900, so as to implement the steps of the reservation success rate statistical analysis method provided by the above-mentioned method embodiments.

[0106] The reservation success rate statistical analysis device 900 can further include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input and output interfaces 960, and / or one or more operating systems 931, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, and the like. Those skilled in the art can understand that the reservation success rate statistical analysis device 900 can further include other components that are not shown in the figure, and the components shown in the figure can be combined or distributed. Figure 3 The structure of the reservation success rate statistical analysis device shown in the figure does not constitute a limitation on the reservation success rate statistical analysis device, and can include more or fewer components than shown in the figure, or combine certain components, or different arrangement of components.

[0107] The present application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium or a volatile computer readable storage medium. The computer readable storage medium stores instructions, and when the instructions are run on a computer, the computer executes the steps of the reservation success rate statistical analysis method.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system or device, unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0109] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0110] Finally, it should be noted that: the above only for the preferred examples of the present application, and not for limiting the present application, although the present application is described in detail with reference to the foregoing embodiments, for those skilled in the art, it still can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A statistical analysis method for reservation success rate, characterized in that, include: Based on a preset statistical time period, the machine running data is acquired and preprocessed to obtain preprocessed running information; Obtain the reservation rules corresponding to high-priority batches, and perform a screening of the pre-processed shipment information based on the reservation rules to obtain a first success rate count and a first confirmation information; Obtain the sharing rules corresponding to the high-priority batches, and perform a second filtering on the first information to be confirmed based on the sharing rules to obtain a second success rate count and a second information to be confirmed; specifically, perform a second filtering based on the preset sharing rules, count the number of successful resource sharing scenarios between high-level batches, and obtain a second success rate count; the remaining batch information that does not meet the sharing rules continues to circulate as information to be confirmed. Obtain preset emergency conditions, and perform three filtering operations on the second information to be confirmed based on the emergency conditions to obtain a third success rate count and a third information to be confirmed; specifically, perform a third filtering operation based on the preset emergency conditions, count the number of successful times in the emergency batch resource occupation scenario, obtain a third success rate count, and further narrow down the scope to be confirmed; the remaining batch information that does not meet the emergency conditions continues to flow as information to be confirmed. Obtain a pre-built bottleneck machine list, and perform four rounds of filtering on the third information to be confirmed based on the bottleneck machine list to obtain a fourth success rate count and final failure information; specifically, perform a fourth filtering based on the pre-built bottleneck machine list, count the number of successful bottleneck machine resource scheduling scenarios, obtain a fourth success rate count, and determine the final failure information. Based on the total reservation success rate calculated from the first success rate count, the second success rate count, the third success rate count, and the fourth success rate count, reservation optimization information is generated.

2. The statistical analysis method for reservation success rate according to claim 1, characterized in that, The process involves acquiring machine running data based on a preset statistical time period and preprocessing it to obtain preprocessed running information, including: Based on a preset statistical time period, the machine running data is obtained. The machine running data includes machine information and multiple batch information. The machine information includes the total number of loading ports, and each batch information includes priority, start processing time, end processing time, actual arrival time and remaining queuing time. Based on the end processing time, the batch information in the machine running data is sorted in ascending order to obtain sorted running data; Based on the priority, the sorted shipment data is filtered, and batch information belonging to priority level 0 and 1 is retained to obtain preprocessed shipment information.

3. The statistical analysis method for reservation success rate according to claim 2, characterized in that, The step of obtaining the reservation rules corresponding to high-priority batches, and then filtering the pre-processed shipment information based on the reservation rules to obtain a first success rate count and a first pending confirmation information, includes: Obtain the reservation rules corresponding to the high-priority batches, wherein the reservation rules include level 0 reservation rules and level 1 reservation rules; Set the initial value of the first success rate count to 0, and construct an initially empty first list to record the first information to be confirmed; When the priority of the batch information is 0, if the start processing time of the current batch information is greater than the end processing time of the previous batch information on the machine, the first success rate count is incremented by 1; if the start processing time of the current batch information is less than or equal to the end processing time of the previous batch information on the machine, the current batch information is loaded into the first list. When the priority of the batch information is level 1, the arrival correction time of the current batch information is calculated based on the preset buffer time and the actual arrival time, and the in-process batch data corresponding to the arrival correction time is obtained. The in-process batch data includes the number of in-process batches and the in-process batch information corresponding to the number of in-process batches. If the total number of loading ports is greater than the number of processing batches, the first success rate count is incremented by 1; if the total number of loading ports equals the number of processing batches, the current batch information is loaded into the first list.

4. The statistical analysis method for reservation success rate according to claim 3, characterized in that, The step of obtaining the shared rules corresponding to the high-priority batches, and performing a second filtering on the first information to be confirmed based on the shared rules to obtain a second success rate count and a second information to be confirmed, includes: Obtain the sharing rules corresponding to the high-priority batches, wherein the sharing rules include level 0 sharing rules and level 1 sharing rules; Set the initial value of the second success rate count to 0, and construct an initially empty second list to record the second information to be confirmed; Based on the arrival correction time of the current batch information, obtain the ongoing batch data corresponding to the current batch information; When the priority of the current batch information is 0, if there is a batch information with priority 0 in the ongoing batch data, the second success rate count is incremented by 1; if there is no batch information with priority 0 in the ongoing batch data, the current batch information is loaded into the second list. When the priority of the current batch information is level 1, if there is a batch information with a priority of level 0 or level 1 in the ongoing batch data, the second success rate count is incremented by 1; if there is no batch information with a priority of level 0 or level 1 in the ongoing batch data, the current batch information is loaded into the second list.

5. The statistical analysis method for reservation success rate according to claim 3, characterized in that, The process of obtaining preset emergency conditions, and then filtering the second information to be confirmed three times based on these emergency conditions to obtain a third success rate count and a third information to be confirmed, includes: Obtain a preset emergency condition, wherein the preset emergency condition is a preset emergency threshold; Set the initial value of the third success rate count to 0, and construct an initially empty third list to record the third confirmation information; Based on the arrival correction time of the current batch information, obtain the ongoing batch data corresponding to the current batch information; If any of the ongoing batch data obtained contains an ongoing batch with a remaining queuing time less than the preset emergency threshold, then the third success rate count is incremented by 1. If the remaining queuing time of all ongoing batches in the acquired ongoing batch data is greater than or equal to the preset emergency threshold, then the current batch information will be loaded into the third list.

6. The statistical analysis method for reservation success rate according to claim 2, characterized in that, The process of obtaining a pre-built bottleneck machine list, and then performing four rounds of filtering on the third information to be confirmed based on the bottleneck machine list, yields a fourth success rate count and final failure information, including: Set the initial value of the fourth success rate counter to 0, and construct an initially empty fourth list to record the final failure information; Obtain a pre-built list of bottleneck machines based on the current batch information, and compare the machine information with the pre-built list of bottleneck machines. If the comparison result indicates that the current machine is the bottleneck machine, then the fourth success rate count is incremented by 1; If the comparison results indicate that the current machine is not the bottleneck machine, then the information of the current batch will be loaded into the fourth list.

7. The statistical analysis method for reservation success rate according to claim 6, characterized in that, The process of generating reservation optimization information based on the total reservation success rate using the first success rate count, the second success rate count, the third success rate count, and the fourth success rate count, and based on the calculated total reservation success rate and final failure information, includes: The total reserved success rate is based on the first success rate count, the second success rate count, the third success rate count, and the fourth success rate count; Based on the batch information in the final failure information, locate the reasons for the reserved failures; Based on the reasons for reservation failure, an optimization and improvement strategy is generated. Combined with the generated optimization and improvement strategy and the calculated total reservation success rate, reservation optimization information is generated.

8. A device for statistical analysis of reserved success rate, characterized in that, include: The acquisition module is used to acquire machine running data based on a preset statistical time period and preprocess it to obtain preprocessed running information. A primary filtering module is used to obtain the reservation rules corresponding to high-priority batches, and to perform a primary filtering on the pre-processed shipment information based on the reservation rules to obtain a first success rate count and a first confirmation information. The secondary filtering module is used to obtain the sharing rules corresponding to high-priority batches, and to perform secondary filtering on the first information to be confirmed based on the sharing rules to obtain a second success rate count and a second information to be confirmed. Specifically, the second filtering is performed based on the preset sharing rules, and the number of successful resource sharing scenarios between high-level batches is counted to obtain a second success rate count. The remaining batch information that does not meet the sharing rules continues to flow as information to be confirmed. The three-stage filtering module is used to obtain preset emergency conditions and perform three-stage filtering on the second information to be confirmed based on the emergency conditions to obtain a third success rate count and a third information to be confirmed. Specifically, the third filtering is performed based on the preset emergency conditions to count the number of successful times in the emergency batch resource occupation scenario to obtain a third success rate count, further narrowing down the scope to be confirmed. The remaining batch information that does not meet the emergency conditions continues to flow as information to be confirmed. The four-stage filtering module is used to obtain a pre-built bottleneck machine list, and to perform four-stage filtering on the third information to be confirmed based on the bottleneck machine list to obtain a fourth success rate count and final failure information; specifically, based on the pre-built bottleneck machine list, the fourth filtering is performed to count the number of successful bottleneck machine resource scheduling scenarios, obtain a fourth success rate count, and determine the final failure information. The generation module is used to generate reservation optimization information based on the total reservation success rate of the first success rate count, the second success rate count, the third success rate count, and the fourth success rate count, and based on the calculated total reservation success rate and the final failure information.

9. A device for statistical analysis of reserved success rates, characterized in that, The reservation success rate statistical analysis device includes: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors invokes the instructions in the memory to cause the reservation success rate statistical analysis device to perform the steps of the reservation success rate statistical analysis method as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement each step of the reservation success rate statistical analysis method as described in any one of claims 1-7.

Images