CN113094197A - Method, device, equipment and storage medium for judging instruction submission abnormity - Google Patents
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Abstract
The method comprises the steps of calculating the time consumption of the instruction according to the submission time of the instruction; determining an abnormal threshold value of the instruction according to the service type of the instruction and the submission time of the instruction; judging whether the consumed time of the instruction exceeds the exception threshold, and if the consumed time of the instruction exceeds the exception threshold of the instruction, determining that the instruction is submitted to exception; wherein the anomaly threshold is determined by: dividing the transaction time to obtain a plurality of time slices; determining the time consumption distribution of processing instructions of each service type in a plurality of time slices within preset days; and calculating and storing an abnormal threshold value of the processing time of the processing instruction of each service type in each time slice according to the time consumption distribution of the processing instruction of each service type in a plurality of time slices. The method reduces the labor consumption and improves the accuracy of instruction submission.
Description
Technical Field
The present invention relates to the field of data analysis, and in particular, to a method, an apparatus, a device, and a storage medium for determining an instruction submission exception.
Background
The asset hosting business is a business which takes a commercial bank or other legal organizations with certain qualification as a host, signs a consignment asset hosting contract with a consignor according to related laws and regulations, safely keeps assets of consignment investment and fulfills related duties of the consignor. The capital settlement is the most important link in the escrow business, and refers to the capital receipt and payment behaviors related to escrow assets, which are transacted by the escrow according to the compliance instructions and other legal documents signed by the parties. The fund transfer is required to be completed before the deadline time corresponding to different transfer scenes in the fund transfer process, the command system generates a command in the whole fund transfer process, the command is submitted to the transfer system, a worker conducts audit judgment on the command and then submits the command to the host, and the host deducts the account balance to complete the deduction.
In the fund clearing process in the prior art, most of business personnel send prompt mails to the staff every day to remind the staff of paying attention to instructions with overlong processing time and prevent abnormal submission conditions such as missed delivery or late delivery, but the mode consumes manpower, and errors and careless omission easily occur when the business personnel send the prompt mails due to different delivery deadline times of different fund clearing. Therefore, there is a need for a method for determining an instruction commit exception, which can automatically determine instructions with too long processing time, reduce the labor consumption, and improve the accuracy of instruction commit.
Disclosure of Invention
Embodiments of the present disclosure provide a method, an apparatus, a device, and a storage medium for determining an instruction commit exception, so as to reduce labor consumption and improve an accuracy of instruction commit.
In order to achieve the above object, in one aspect, an embodiment herein provides a method for determining an instruction commit exception, where the method includes:
calculating the time consumption of the instruction according to the submission time of the instruction;
determining an abnormal threshold value of the instruction according to the service type of the instruction and the submission time of the instruction;
judging whether the consumed time of the instruction exceeds the exception threshold, and if the consumed time of the instruction exceeds the exception threshold of the instruction, determining that the instruction is submitted to exception;
wherein the anomaly threshold is determined by:
dividing the transaction time to obtain a plurality of time slices;
determining the time consumption distribution of processing instructions of each service type in a plurality of time slices within preset days;
and calculating and storing an abnormal threshold value of the processing time of the processing instruction of each service type in each time slice according to the time consumption distribution of the processing instruction of each service type in a plurality of time slices.
Preferably, the determining the exception threshold of the instruction according to the service type of the instruction and the submission time of the instruction includes:
determining a target service type according to the service type of the instruction;
determining a target time slice according to the submission time of the instruction;
according to the target service type and the target time slice, inquiring the target service type and an abnormal threshold value under the target time slice from a database;
and taking the inquired exception threshold value as the exception threshold value of the instruction.
Preferably, the dividing the transaction time to obtain a plurality of time slices includes:
the transaction time is divided according to a preset time period to form a plurality of equal time slices.
Preferably, before determining the time consumption distribution of the processing instructions of each service type in a plurality of time slices in the preset number of days, the method further includes:
and eliminating the processing instructions with the processing time consumption larger than a preset error value from the processing instructions of each service type in the preset days.
Preferably, the calculating and storing the processing instruction of each service type before the exception threshold for processing the consumed time in each time slice according to the consumed time distribution of the processing instruction of each service type in a plurality of time slices further includes:
determining idle time slices according to the time consumption distribution of the processing instructions in a plurality of time slices, wherein the number of the processing instructions corresponding to the idle time slices does not exceed a set number;
merging adjacent idle time slices according to the time sequence to obtain merged time slices;
and integrating the merged time slices and the time slices which are not merged according to the time sequence, and re-determining the time slices.
Preferably, the calculating and storing the processing instruction of each service type before the exception threshold for processing the consumed time in each time slice according to the consumed time distribution of the processing instruction of each service type in a plurality of time slices further includes:
judging whether processing instructions exist in each time slice of the current service type;
if the processing instruction exists in each time slice, calculating and storing an abnormal threshold value of the processing time consumption of the processing instruction of the current service type in each time slice according to the time consumption distribution of the processing instruction of the current service type in the time slices;
and if no processing instruction exists in any time slice, taking the minimum exception threshold value in the exception threshold values consumed by processing in all time slices of the current service type as the exception threshold value of the time slice.
Preferably, the calculating and storing an exception threshold of processing time consumed by the processing instruction of each service type in each time slice according to the time consumption distribution of the processing instruction of each service type in the time slices includes:
calculating the mean value and the variance of the processing time of the processing instructions of each service type in each time slice according to the time consumption distribution of the processing instructions of each service type in a plurality of time slices;
and calculating and storing an abnormal threshold value of the processing time of the processing instruction of each service type in each time slice according to the average value, the variance and the preset precision of the processing time of the processing instruction of each service type in each time slice.
In another aspect, an embodiment herein provides an apparatus for determining an instruction commit exception, where the apparatus includes:
a time-consuming calculation module: calculating the time consumption of the instruction according to the submission time of the instruction;
an anomaly threshold determination module: determining an abnormal threshold value of the instruction according to the service type of the instruction and the submission time of the instruction;
a judging module: judging whether the consumed time of the instruction exceeds the exception threshold, and if the consumed time of the instruction exceeds the exception threshold of the instruction, determining that the instruction is submitted to exception;
wherein the anomaly threshold is determined by:
dividing the transaction time to obtain a plurality of time slices;
determining the time consumption distribution of processing instructions of each service type in a plurality of time slices within preset days;
and calculating and storing an abnormal threshold value of the processing time of the processing instruction of each service type in each time slice according to the time consumption distribution of the processing instruction of each service type in a plurality of time slices.
In yet another aspect, embodiments herein also provide a computer device comprising a memory, a processor, and a computer program stored on the memory, the computer program, when executed by the processor, performing the instructions of any one of the methods described above.
In yet another aspect, embodiments herein also provide a computer-readable storage medium having stored thereon a computer program, which when executed by a processor of a computer device, performs the instructions of any one of the methods described above.
According to the technical scheme provided by the embodiment, whether the instruction is submitted to be abnormal or not is judged by judging whether the consumed time of the instruction exceeds the abnormal threshold value under the corresponding service type, the purpose of judging the abnormal threshold value according to different fund clearing conditions can be achieved, the consumed time exceeds the abnormal threshold value through automatic judgment, the labor consumption can be reduced, and the probability of errors in manual judgment is reduced. The abnormal threshold value of the processing time consumed by the processing instruction of each service type in a plurality of time slices is respectively determined, the time slices can be divided according to the processing instruction of the same service type, the corresponding abnormal threshold values are calculated according to different time slices, the distribution of the abnormal threshold values is further refined, the abnormal threshold values are more representative and targeted, and the instruction submission accuracy is improved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a first flowchart illustrating a method for determining an instruction commit exception according to an embodiment of the present disclosure;
FIG. 2 illustrates a first flow diagram for determining an anomaly threshold provided by embodiments herein;
FIG. 3 illustrates a second flow diagram for determining an anomaly threshold provided by embodiments herein;
FIG. 4 shows a flow diagram of a time slice analysis process provided by embodiments herein;
FIG. 5 illustrates a third flow diagram for determining an anomaly threshold provided by embodiments herein;
FIG. 6 illustrates a fourth flowchart for determining an anomaly threshold provided by embodiments herein;
FIG. 7 is a second flowchart illustrating a method for determining an instruction commit exception according to an embodiment of the present disclosure;
fig. 8 is a schematic block diagram illustrating a module structure of an instruction commit exception determining apparatus according to an embodiment of the present disclosure;
FIG. 9 is a block diagram illustrating an apparatus for determining an anomaly threshold according to an embodiment of the present disclosure;
fig. 10 shows a schematic structural diagram of a computer device provided in an embodiment herein.
Description of the symbols of the drawings:
100. a time consumption calculation module;
200. an anomaly threshold determination module;
300. a judgment module;
400. a time slice division module;
500. a time consumption distribution determining module;
600. an anomaly threshold calculation module;
1002. a computer device;
1004. a processor;
1006. a memory;
1008. a drive mechanism;
1010. an input/output module;
1012. an input device;
1014. an output device;
1016. a presentation device;
1018. a graphical user interface;
1020. a network interface;
1022. a communication link;
1024. a communication bus.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments herein without making any creative effort, shall fall within the scope of protection.
In the fund clearing process in the prior art, most of business personnel send prompt mails to the staff every day to remind the staff of paying attention to instructions with overlong processing time and prevent abnormal submission conditions such as missed delivery or late delivery, but the mode consumes manpower, and errors and careless omission easily occur when the business personnel send the prompt mails due to different delivery deadline times of different fund clearing. Therefore, there is a need for a method for determining an instruction commit exception, which can automatically determine instructions with too long processing time, reduce the labor consumption, and improve the accuracy of instruction commit.
In order to solve the above problem, embodiments herein provide a method for determining an instruction commit exception. Fig. 1 is a schematic diagram of steps of a method for determining an instruction commit exception according to an embodiment of the present disclosure, and the present disclosure provides the method operation steps according to an embodiment or a flowchart, but may include more or less operation steps based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual system or apparatus product executes, it can execute sequentially or in parallel according to the method shown in the embodiment or the figures.
Referring to fig. 1, a method for determining an instruction commit exception, the method comprising:
s102: calculating the time consumption of the instruction according to the submission time of the instruction;
s104: determining an abnormal threshold value of the instruction according to the service type of the instruction and the submission time of the instruction;
s106: judging whether the consumed time of the instruction exceeds the exception threshold, and if the consumed time of the instruction exceeds the exception threshold of the instruction, determining that the instruction is submitted to exception;
referring to fig. 2, wherein the anomaly threshold is determined by the following process:
s108: dividing the transaction time to obtain a plurality of time slices;
s110: determining the time consumption distribution of processing instructions of each service type in a plurality of time slices within preset days;
s112: and calculating and storing an abnormal threshold value of the processing time of the processing instruction of each service type in each time slice according to the time consumption distribution of the processing instruction of each service type in a plurality of time slices.
The fund clearance of different service types corresponding to different instructions is realized, whether the instruction is submitted to be abnormal or not is judged by judging whether the consumed time of the instruction exceeds the abnormal threshold value under the corresponding service type, the purpose of judging the abnormal threshold value according to the different fund clearance conditions can be realized, the labor consumption can be reduced by automatically judging the instruction of which the consumed time exceeds the abnormal threshold value, and the probability of errors in manual judgment is reduced. The abnormal threshold value of the processing time consumed by the processing instruction of each service type in a plurality of time slices is respectively determined, the time slices can be divided according to the processing instruction of the same service type, the corresponding abnormal threshold values are calculated according to different time slices, the distribution of the abnormal threshold values is further refined, the abnormal threshold values are more representative and targeted, and the instruction submission accuracy is improved.
In this embodiment, the dividing the transaction time into a plurality of time slices includes:
the transaction time is divided according to a preset time period to form a plurality of equal time slices.
Specifically, the transaction time is the normal work time of the bank, and may be 8 o 'clock earlier to 5 o' clock later, and during this time, the transaction may be performed. The preset time period can be determined according to the actual working condition, and when the transaction time is divided, the transaction time can be divided every 30 minutes to obtain a plurality of time slices. After the transaction time is divided into a plurality of equal time slices, the abnormal threshold value can be refined into a plurality of time slices, and the representativeness of the abnormal threshold value can be stronger after the abnormal threshold value is refined.
The time for submitting the instruction is the corresponding time when the instruction is sent by the instruction system, and the time consumed by the instruction can be calculated from the time for submitting the instruction to the current time, namely the time consumed by subtracting the time for submitting the instruction from the current time. The service type of the instruction may include a guaranteed delivery, a daily end-to-end monetary settlement, a call into a DVP account, and so on.
The preset days are selected according to actual working conditions, in order to guarantee the accuracy of calculating the abnormal threshold, a plurality of working days before the current transaction date can be taken as the preset days, for example, the current day is 4 months and 1 day, and a plurality of working days can be rewound from 3 months and 31 days to be taken as the preset days. After the transaction is finished every day, the preset number of days can be updated, and the processing instructions of all service types in the preset number of days are determined again, so that the accuracy of calculating the abnormal threshold value is ensured. For example, at day 2/4, several working days may be rewound from day 1/4 as the preset number of days. And distributing the processing instructions of each service type in preset days according to time slices, wherein each time slice corresponds to a plurality of time slices in each service type, and each time slice possibly comprises the time consumption of a plurality of processing instructions. Further, each processing instruction is distributed according to the commit time, and the time consumed by the processing instruction is stored in the corresponding time slice, for example, an instruction at 8 am takes 100 seconds, the instruction is distributed in the time slice from 8 am to 30 am, and the time slice from 8 am to 30 am stores 100 seconds. And calculating and storing the exception threshold of the processing time consumption in each time slice corresponding to each service type, wherein for the service type called into the DVP account, the exception threshold of the processing time consumption from 8 o ' clock to 8 o ' clock and 30 o ' clock is 120 seconds, and the exception threshold of the processing time consumption from 8 o ' clock to 9 o ' clock is 150 seconds.
Referring to fig. 3, in this embodiment, before determining the time consumption distribution of the processing instructions of each service type in a plurality of time slices in a preset number of days, the method further includes:
s109: and eliminating the processing instructions with the processing time consumption larger than a preset error value from the processing instructions of each service type in the preset days.
The preset error value can be set according to the actual working condition, the processing time consumption of some processing instructions can be too long due to the occurrence of some errors, and the processing time consumption of the processing instructions is an error value at the moment and needs to be eliminated so as to improve the accuracy of the calculation of the abnormal threshold.
Referring to fig. 4, in this embodiment, before calculating and storing an exception threshold of processing time consumed by processing instructions of each service type in each time slice according to the time consumption distribution of the processing instructions of each service type in the time slices, the method further includes:
s202: determining idle time slices according to the time consumption distribution of the processing instructions in a plurality of time slices, wherein the number of the processing instructions corresponding to the idle time slices does not exceed a set number;
s204: merging adjacent idle time slices according to the time sequence to obtain merged time slices;
s206: and integrating the merged time slices and the time slices which are not merged according to the time sequence, and re-determining the time slices.
Specifically, the set number may be determined according to a preset number of days, and generally, the set number is proportional to the preset number of days, assuming that the preset number of days is 3 days, the set number may be 3 days, the preset number of days is 30 days, and the set number may be 30 days. The larger the preset number of days is, the larger the number of processing instructions in each time slice is, and the larger the number of processing instructions in the corresponding idle time slice is, for any service type. The adjacent idle time slices are merged and then the time slices are determined again, so that on one hand, unnecessary calculation can be reduced, and the calculation speed is improved; on the other hand, because the number of processing instructions in the idle time slices is small, the error may be large when the abnormal threshold is calculated through a sample with a small number, and therefore after the idle time slices are combined, the calculation error of the abnormal threshold can be reduced, and the calculation accuracy is improved. When the idle time slices are combined, if 3 idle time slices are adjacent, the 3 idle time slices are combined into 1, and if 4 idle time slices are adjacent, the 4 idle time slices are combined into 2.
Referring to fig. 5, in this embodiment, before calculating and storing an exception threshold of processing time consumed by processing instructions of each service type in each time slice according to a time consumption distribution of the processing instructions of each service type in the time slices, the method further includes:
s302: judging whether processing instructions exist in each time slice of the current service type;
s304: if the processing instruction exists in each time slice, calculating and storing an abnormal threshold value of the processing time consumption of the processing instruction of the current service type in each time slice according to the time consumption distribution of the processing instruction of the current service type in the time slices;
s306: and if no processing instruction exists in any time slice, taking the minimum exception threshold value in the exception threshold values consumed by processing in all time slices of the current service type as the exception threshold value of the time slice.
In particular, there are some traffic types that burst instantaneously concentrated in a certain time period, while there are other time periods in which there are fewer or possibly no processing instructions. And in the case of no processing instruction in any time slice, taking the minimum exception threshold value in the exception threshold values consumed by processing in all time slices of the current service type as the exception threshold value of the time slice. Therefore, the situation that if one processing instruction corresponds to a time slice when no abnormal threshold value exists in the time slice can be prevented, the time slice can not judge the abnormality of the processing instruction.
Referring to fig. 6, in this embodiment, calculating and storing an exception threshold of processing time consumed by processing instructions of each service type in each time slice according to a time consumption distribution of the processing instructions of each service type in a plurality of time slices includes:
s1121: calculating the mean value and the variance of the processing time of the processing instructions of each service type in each time slice according to the time consumption distribution of the processing instructions of each service type in a plurality of time slices;
s1122: and calculating and storing an abnormal threshold value of the processing time of the processing instruction of each service type in each time slice according to the average value, the variance and the preset precision of the processing time of the processing instruction of each service type in each time slice.
Specifically, taking a time slice as an example, when calculating the corresponding abnormal threshold, since the time consumed by processing a plurality of processing instructions in the time slice is consumed, the mean μ and the variance of the time consumed by processing are firstly calculated, and the standard deviation σ can be obtained after the variance is obtained. The preset accuracy is set according to actual needs, and since the processing time in the time slice is normally distributed, the probability of the numerical distribution in (mu-sigma, mu + sigma) is 0.6827, the probability of the numerical distribution in (mu-2 sigma, mu +2 sigma) is 0.9545, and the probability of the numerical distribution in (mu-3 sigma, mu +3 sigma) is 0.9973. If the preset accuracy value is high, the mu +3 sigma can be calculated and used as an abnormal threshold value. It should be noted that: the processing time consumption less than μ -3 σ is not identified here because it is not an abnormal case because the processing time consumption is small, and therefore, only μ +3 σ is taken as the abnormality threshold. The processing instruction of each service type is obtained by the method, and the processing time-consuming exception threshold value in each time slice is stored in the database.
Referring to fig. 7, in this embodiment, the determining an exception threshold of the instruction according to the service type of the instruction and the commit time of the instruction includes:
s1041: determining a target service type according to the service type of the instruction;
s1042: determining a target time slice according to the submission time of the instruction;
s1043: according to the target service type and the target time slice, inquiring the target service type and an abnormal threshold value under the target time slice from a database;
s1044: and taking the inquired exception threshold value as the exception threshold value of the instruction.
Steps S1041 and S1042 may be executed in parallel, and there is no necessary order.
Specifically, the target service type and the abnormal threshold value under the target time slice may be found according to the service type and the submission time of the instruction, for example, it takes 100 seconds to submit an instruction calling into the DVP account at the time point 8 am and 15 minutes, and the abnormal threshold value stored in the time slice calling into the DVP account at the time point 8 am and 30 minutes is found for 120 seconds, and since 100 seconds is less than 120 seconds, the instruction is submitted normally. By searching for the target service type and the abnormal threshold value under the target time slice, the instructions can be distinguished according to the time sequence, the abnormal threshold value under the time slice is used as a judgment standard when the instruction is positioned under the time slice, the method refines the instruction to the dimension of the time slice for judgment, and the precision of judging the time-consuming abnormity of the instruction is improved.
Based on the above method for determining an instruction commit exception, the present embodiment further provides a device for determining an instruction commit exception. The apparatus may include systems (including distributed systems), software (applications), modules, components, servers, clients, etc. that employ the methods described herein in embodiments, in conjunction with any necessary apparatus to implement the hardware. Based on the same innovative concepts, embodiments herein provide an apparatus as described in the following embodiments. Since the implementation scheme of the apparatus for solving the problem is similar to that of the method, the specific apparatus implementation in the embodiment of the present disclosure may refer to the implementation of the foregoing method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Specifically, fig. 8 is a schematic block structure diagram of an embodiment of an apparatus for determining an instruction commit exception according to the present embodiment, and referring to fig. 8 and fig. 9, the apparatus for determining an instruction commit exception according to the present embodiment includes: the time consumption calculation module 100, the abnormity threshold value determination module 200 and the judgment module 300.
Time-consuming calculation module 100: calculating the time consumption of the instruction according to the submission time of the instruction;
anomaly threshold determination module 200: determining an abnormal threshold value of the instruction according to the service type of the instruction and the submission time of the instruction;
the judging module 300: judging whether the consumed time of the instruction exceeds the exception threshold, and if the consumed time of the instruction exceeds the exception threshold of the instruction, determining that the instruction is submitted to exception;
wherein the anomaly threshold is determined by:
dividing the transaction time to obtain a plurality of time slices;
determining the time consumption distribution of processing instructions of each service type in a plurality of time slices within preset days;
and calculating and storing an abnormal threshold value of the processing time of the processing instruction of each service type in each time slice according to the time consumption distribution of the processing instruction of each service type in a plurality of time slices.
Referring to fig. 9, in an embodiment herein, there is also provided an apparatus for determining an anomaly threshold, the apparatus comprising:
the time slice division module 400: dividing the transaction time to obtain a plurality of time slices;
the time-consumption distribution determination module 500: determining the time consumption distribution of processing instructions of each service type in a plurality of time slices within preset days;
anomaly threshold calculation module 600: and calculating and storing an abnormal threshold value of the processing time of the processing instruction of each service type in each time slice according to the time consumption distribution of the processing instruction of each service type in a plurality of time slices.
In an embodiment herein, referring to fig. 10, a computer device 1002 is also provided. Computer device 1002 may include one or more processors 1004, such as one or more Central Processing Units (CPUs) or Graphics Processors (GPUs), each of which may implement one or more hardware threads. The computer device 1002 may also comprise any memory 1006 for storing any kind of information, such as code, settings, data, etc., and in a particular embodiment a computer program on the memory 1006 and executable on the processor 1004, the computer program when executed by the processor 1004 may perform instructions according to the above described method. For example, and without limitation, the memory 1006 may include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent fixed or removable components of computer device 1002. In one case, when the processor 1004 executes the associated instructions, which are stored in any memory or combination of memories, the computer device 1002 can perform any of the operations of the associated instructions. The computer device 1002 also includes one or more drive mechanisms 1008, such as a hard disk drive mechanism, an optical disk drive mechanism, or the like, for interacting with any memory.
Corresponding to the methods in fig. 1-7, the embodiments herein also provide a computer-readable storage medium having stored thereon a computer program, which, when executed by a processor, performs the steps of the above-described method.
Embodiments herein also provide computer readable instructions, wherein when executed by a processor, a program thereof causes the processor to perform the method as shown in fig. 1-7.
It should be understood that, in various embodiments herein, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments herein.
It should also be understood that, in the embodiments herein, the term "and/or" is only one kind of association relation describing an associated object, meaning that three kinds of relations may exist. For example, a and/or B, may represent: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided herein, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments herein.
In addition, functional units in the embodiments herein may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present invention may be implemented in a form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The principles and embodiments of this document are explained herein using specific examples, which are presented only to aid in understanding the methods and their core concepts; meanwhile, for the general technical personnel in the field, according to the idea of this document, there may be changes in the concrete implementation and the application scope, in summary, this description should not be understood as the limitation of this document.
Claims (10)
1. A method for determining an instruction commit exception, the method comprising:
calculating the time consumption of the instruction according to the submission time of the instruction;
determining an abnormal threshold value of the instruction according to the service type of the instruction and the submission time of the instruction;
judging whether the consumed time of the instruction exceeds the exception threshold, and if the consumed time of the instruction exceeds the exception threshold of the instruction, determining that the instruction is submitted to exception;
wherein the anomaly threshold is determined by:
dividing the transaction time to obtain a plurality of time slices;
determining the time consumption distribution of processing instructions of each service type in a plurality of time slices within preset days;
and calculating and storing an abnormal threshold value of the processing time of the processing instruction of each service type in each time slice according to the time consumption distribution of the processing instruction of each service type in a plurality of time slices.
2. The method according to claim 1, wherein the determining the exception threshold of the instruction according to the service type of the instruction and the time of submitting the instruction comprises:
determining a target service type according to the service type of the instruction;
determining a target time slice according to the submission time of the instruction;
according to the target service type and the target time slice, inquiring the target service type and an abnormal threshold value under the target time slice from a database;
and taking the inquired exception threshold value as the exception threshold value of the instruction.
3. The method of claim 1, wherein the dividing the transaction time into time slices comprises:
the transaction time is divided according to a preset time period to form a plurality of equal time slices.
4. The method of claim 1, wherein before determining the time consumption distribution of the processing commands of each service type in a plurality of time slices in a preset number of days, the method further comprises:
and eliminating the processing instructions with the processing time consumption larger than a preset error value from the processing instructions of each service type in the preset days.
5. The method according to claim 1, wherein before the step of calculating and storing the time-consuming exception threshold for processing the processing instruction of each service type in each time slice according to the time-consuming distribution of the processing instruction of each service type in the time slices, the method further comprises:
determining idle time slices according to the time consumption distribution of the processing instructions in a plurality of time slices, wherein the number of the processing instructions corresponding to the idle time slices does not exceed a set number;
merging adjacent idle time slices according to the time sequence to obtain merged time slices;
and integrating the merged time slices and the time slices which are not merged according to the time sequence, and re-determining the time slices.
6. The method according to claim 1, wherein before the step of calculating and storing the time-consuming exception threshold for processing the processing instruction of each service type in each time slice according to the time-consuming distribution of the processing instruction of each service type in the time slices, the method further comprises:
judging whether processing instructions exist in each time slice of the current service type;
if the processing instruction exists in each time slice, calculating and storing an abnormal threshold value of the processing time consumption of the processing instruction of the current service type in each time slice according to the time consumption distribution of the processing instruction of the current service type in the time slices;
and if no processing instruction exists in any time slice, taking the minimum exception threshold value in the exception threshold values consumed by processing in all time slices of the current service type as the exception threshold value of the time slice.
7. The method according to claim 1, wherein the calculating and storing the exception threshold of the processing time of the processing instruction of each service type in each time slice according to the time consumption distribution of the processing instruction of each service type in the time slices comprises:
calculating the mean value and the variance of the processing time of the processing instructions of each service type in each time slice according to the time consumption distribution of the processing instructions of each service type in a plurality of time slices;
and calculating and storing an abnormal threshold value of the processing time of the processing instruction of each service type in each time slice according to the average value, the variance and the preset precision of the processing time of the processing instruction of each service type in each time slice.
8. An apparatus for determining an instruction commit exception, the apparatus comprising:
a time-consuming calculation module: calculating the time consumption of the instruction according to the submission time of the instruction;
an anomaly threshold determination module: determining an abnormal threshold value of the instruction according to the service type of the instruction and the submission time of the instruction;
a judging module: judging whether the consumed time of the instruction exceeds the exception threshold, and if the consumed time of the instruction exceeds the exception threshold of the instruction, determining that the instruction is submitted to exception;
wherein the anomaly threshold is determined by:
dividing the transaction time to obtain a plurality of time slices;
determining the time consumption distribution of processing instructions of each service type in a plurality of time slices within preset days;
and calculating and storing an abnormal threshold value of the processing time of the processing instruction of each service type in each time slice according to the time consumption distribution of the processing instruction of each service type in a plurality of time slices.
9. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program, when executed by the processor, performs the instructions of the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor of a computer device, is adapted to carry out the instructions of the method according to any one of claims 1-7.
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