CN106845881A - A kind of detection method of stock abnormal data, device and electronic equipment - Google Patents

A kind of detection method of stock abnormal data, device and electronic equipment Download PDF

Info

Publication number
CN106845881A
CN106845881A CN201510882926.2A CN201510882926A CN106845881A CN 106845881 A CN106845881 A CN 106845881A CN 201510882926 A CN201510882926 A CN 201510882926A CN 106845881 A CN106845881 A CN 106845881A
Authority
CN
China
Prior art keywords
stock
newly
data
increased
change data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201510882926.2A
Other languages
Chinese (zh)
Inventor
陈彩莲
王金炜
袁康
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Alibaba Group Holding Ltd
Original Assignee
Alibaba Group Holding Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Alibaba Group Holding Ltd filed Critical Alibaba Group Holding Ltd
Priority to CN201510882926.2A priority Critical patent/CN106845881A/en
Priority to PCT/CN2016/107016 priority patent/WO2017092599A1/en
Publication of CN106845881A publication Critical patent/CN106845881A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management

Abstract

The invention discloses a kind of detection method of stock abnormal data, device and electronic equipment.The detection method of the stock abnormal data includes：Obtain the newly-increased stateful transaction change data and corresponding newly-increased stock change data of order to be detected；Stock according to the newly-increased stateful transaction change data and the order application to be detected reduces pattern, detects whether newly-increased stock's change data are abnormal stock's change data.The method provided using the application, the correctness of stock's change that can be caused to the change of stateful transaction each time is carried out analysis in real time and judged such that it is able to reach fine-grained detection stock change data, the problem in stock's renewal process is found in time.

Description

A kind of detection method of stock abnormal data, device and electronic equipment
Technical field
The application is related to Data Detection Technology field, and in particular to a kind of detection method of stock abnormal data, Device and electronic equipment.
Background technology
In electronic business web site running, commodity stocks data and the commodity actual library of website records It is a relatively conventional problem that storage is inconsistent, i.e.,：The commodity stocks data of website are inaccurate.Inventory Can not only strong influence be produced to platform service, according to inaccurate while will also damage the interests of other each side. For one, the e-commerce website including magnanimity commodity is, it is necessary to real-time and accurately monitor inaccurate commodity storehouse Deposit data, intelligence problem analysis producing cause, inaccurate commodity stocks data have total understanding and The analysis of detailed reason, to accomplish calm reply.
Causing the inaccurate key factor of commodity stocks data includes business factor and platform technical factor.Wherein Business factor includes that businessman is set to the mistake of commodity stocks data, and platform technology factor includes technical reason Stock reduces inaccurate etc. during caused commodity transaction.For business factor, platform technology because Element is the main cause for causing commodity stocks data inaccurate, for example, to that should detain stock but after order operation Stock is not detained, or stock should be covered and do not cover stock etc. but.Because e-commerce platform is generally related to And multiple complication systems (transaction system and inventory management system etc.), therefore monitor in real time stock abnormal situation is wanted, In particular for the system for having magnanimity commodity, complexity is self-evident.
Most common situation is commodity stocks data more than commodity actual inventory, the inventory data of this mistake E-commerce website will be caused the problem of commodity oversold finally occur.Commodity oversold is that commodity stocks data are forbidden The most serious problem for really producing, while being also the problem that seller is easiest to find.After seller has found commodity oversold, Oversold situation is fed back into website first, then web technology personnel intervention investigation, this processing mode is solution The certainly most original method of commodity oversold problem.The method has the drawback that：Only surpass in real generation commodity After selling, as the system that could find be present, and unpredictable commodity have oversold risk.To understand Certainly this problem, prior art carries out off-line calculation by using Hadoop clusters, to find whether system deposits In commodity oversold risk.However, the method equally exists some shortcomings, i.e.,：The non real-time nature of off-line calculation Actual demand can not be met.
In order to detect stock abnormal in real time, the method detection of line monitor in real time has been typically employed at present Commodity stocks data through there is oversold situation.The method be using the commodity stocks data at certain time point as Benchmark inventory data (typically takes the stock at zero point moment, therefore is also zero point stock), by benchmark inventory data with Inventory change aggregate-value after the time point in prefixed time interval is compared, when discovery benchmark inventory data During less than inventory change aggregate-value, there is commodity oversold problem in decision-making system.
Examined because the detection method of above-mentioned on line real-time monitoring is the inventory change aggregate-value based on a period of time Survey with the presence or absence of oversold risk, therefore, if benchmark inventory data is very big, experience may be needed more long Time interval, benchmark inventory data can be detected less than inventory change aggregate-value.It can be seen that, the method The effect of real-time detection stock abnormal is not really achieved, i.e.,：Cannot be in one stock of exception of every generation Just detected during change data.
The content of the invention
The application provides a kind of detection method of stock abnormal data, device and electronic equipment, existing to solve Technology there is a problem of cannot real-time detection change data to each abnormal stock.
The application provides a kind of detection method of stock abnormal data, including：
Obtain the newly-increased stateful transaction change data and corresponding newly-increased stock change number of order to be detected According to；
Stock according to the newly-increased stateful transaction change data and the order application to be detected reduces pattern, Detect whether newly-increased stock's change data are abnormal stock's change data.
Optionally, the storehouse that data and the order application to be detected are changed according to the newly-increased stateful transaction The pattern of reducing is deposited, detects whether newly-increased stock's change data are abnormal stock's change data, including：
According to the newly-increased stateful transaction change data, the current stateful transaction of the order to be detected is obtained；
Stock according to the current stateful transaction and the order application to be detected reduces pattern, calculates described Newly-increased stock changes the desired value of data；
Judge whether newly-increased stock's change data and the desired value are identical；
If so, then judging that newly-increased stock's change data change data as normal stock；
If it is not, then judging that newly-increased stock's change data change data as the abnormal stock.
Optionally, the storehouse that data and the order application to be detected are changed according to the newly-increased stateful transaction The pattern of reducing is deposited, detects whether newly-increased stock's change data are abnormal stock's change data, including：
According to the newly-increased stateful transaction change data, the current stateful transaction of the order to be detected is obtained；
Stock according to the order application to be detected reduces pattern and newly-increased stock's change data, generation The forecasted transaction state of the order to be detected；
Judge whether the current stateful transaction and the forecasted transaction state of the order to be detected are identical；
If so, then judging that newly-increased stock's change data change data as normal stock；
If it is not, then judging that newly-increased stock's change data change data as the abnormal stock.
Optionally, the storehouse that data and the order application to be detected are changed according to the newly-increased stateful transaction The pattern of reducing is deposited, detects whether newly-increased stock's change data are abnormal stock's change data, including：
According to the newly-increased stateful transaction change data, the current stateful transaction of the order to be detected is obtained；
Stock according to the current stateful transaction and the order application to be detected reduces pattern, calculates described Newly-increased stock changes the desired value of data；And according to the stock of the order application to be detected reduce pattern and Newly-increased stock's change data, generate the forecasted transaction state of the order to be detected；
Judge whether newly-increased stock's change data and the desired value are identical, and the order to be detected Current stateful transaction and the forecasted transaction state it is whether identical；
If so, then judging that newly-increased stock's change data change data as normal stock；
If it is not, then judging that newly-increased stock's change data change data as the abnormal stock.
Optionally, if it is abnormal stock's change data to detect newly-increased stock's change data, Also include：
It is the testing result that the abnormal stock changes data to store newly-increased stock's change data.
Optionally, also include：
It is the order of stock's update abnormal by the hand marker to be detected.
Optionally, if it is that normal stock changes data to detect newly-increased stock's change data, also wrap Include：
Judge whether the order to be detected is marked as the order of stock's update abnormal；
If so, it is the detection knot that the abnormal stock changes data then to delete newly-increased stock's change data Really.
Optionally, newly-increased stateful transaction change data for obtaining order to be detected and corresponding newly-increased Stock changes data, in the following way：
According to the order number of the order to be detected, newly-increased stateful transaction change data and described new are obtained Increase stock's change data.
Optionally, also include：
The generation newly-increased stock that acquisition is prestored changes abnormality processing result during data；The exception Result storage is in the newly-increased stateful transaction change data or newly-increased stock's change data；
The abnormality processing result is changed the abnormal cause of data as the abnormal stock.
Optionally, the result storage is in the newly-increased stateful transaction change data；Will be described different Normal result storage also included before the newly-increased stateful transaction is changed in data：
The stock's interface provided by inventory management system, obtains the abnormality processing result.
Optionally, when the stock abnormal detection notice of the correspondence order to be detected is listened to, perform described The detection method of stock abnormal data.
Optionally, the detection method of the stock abnormal data is operated in and processed based on real-time distributed calculating In the anomaly data detection platform of framework establishment.
Optionally, the stock abnormal detection notice, is generated using following steps：
By incremental data real-time synchronization device, by newly-increased stateful transaction change data and the newly-increased storehouse Deposit change data syn-chronization to the anomaly data detection platform；
Newly-increased stateful transaction change data and the newly-increased storehouse are received in the anomaly data detection platform After depositing at least one of change data, if default transmission stock abnormal detection notice condition is set up, send The stock of the order belonging to the correspondence newly-increased stateful transaction change data or newly-increased stock's change data is different Normal detection notice.
Optionally, the default transmission stock abnormal detection notice condition includes：Current time with receive When the time interval of the newly-increased stateful transaction change data or newly-increased stock's change data reaches default Between be spaced, or memory headroom shared by order to be detected reaches default memory headroom.
Optionally, described that the newly-increased stateful transaction change data and newly-increased stock's change data are same Walk after the anomaly data detection platform, also include：
According to default data normalization rule, to newly-increased stateful transaction change data and the newly-increased storehouse Depositing change data carries out data normalization treatment.
Accordingly, the application also provides a kind of detection means of stock abnormal data, including：
First acquisition unit, the newly-increased stateful transaction for obtaining order to be detected changes data and corresponding Newly-increased stock change data；
Detection unit, for according to the newly-increased stateful transaction change data and the order application to be detected Stock reduces pattern, detects whether newly-increased stock's change data are abnormal stock's change data.
Optionally, the detection unit includes：
Subelement is obtained, for according to the newly-increased stateful transaction change data, obtaining the order to be detected Current stateful transaction；
Computation subunit, for being buckled according to the stock of the current stateful transaction and the order application to be detected Size reduction mode, calculates the desired value that the newly-increased stock changes data；
Whether judgment sub-unit is identical for judging newly-increased stock's change data and the desired value；
Judge normal subelement, if being yes for above-mentioned judged result, judge newly-increased stock's change Data are that normal stock changes data；
Judge abnormal subelement, if being no for above-mentioned judged result, judge newly-increased stock's change Data are abnormal stock's change data.
Optionally, the detection unit includes：
Subelement is obtained, for according to the newly-increased stateful transaction change data, obtaining the order to be detected Current stateful transaction；
Computation subunit, for reducing pattern and the newly-increased storehouse according to the stock of the order application to be detected Change data are deposited, the forecasted transaction state of the order to be detected is generated；
Judgment sub-unit, current stateful transaction and the forecasted transaction shape for judging the order to be detected Whether state is identical；
Judge normal subelement, if being yes for above-mentioned judged result, judge newly-increased stock's change Data are that normal stock changes data；
Judge abnormal subelement, if being no for above-mentioned judged result, judge newly-increased stock's change Data are abnormal stock's change data.
Optionally, the detection unit includes：
Subelement is obtained, for according to the newly-increased stateful transaction change data, obtaining the order to be detected Current stateful transaction；
Computation subunit, for being buckled according to the stock of the current stateful transaction and the order application to be detected Size reduction mode, calculates the desired value that the newly-increased stock changes data；And according to the order application to be detected Stock reduce pattern and newly-increased stock change data, generate the forecasted transaction shape of the order to be detected State；
Judgment sub-unit, it is whether identical for judging newly-increased stock's change data and the desired value, with And whether the current stateful transaction and the forecasted transaction state of the order to be detected are identical；
Judge normal subelement, if being yes for above-mentioned judged result, judge newly-increased stock's change Data are that normal stock changes data；
Judge abnormal subelement, if being no for above-mentioned judged result, judge newly-increased stock's change Data are abnormal stock's change data.
Optionally, if it is abnormal stock's change data to detect newly-increased stock's change data, Also include：
Storage result unit, is that the abnormal stock changes number for storing newly-increased stock's change data According to testing result.
Optionally, also include：
Indexing unit, for being the order of stock's update abnormal by the hand marker to be detected.
Optionally, if it is that normal stock changes data to detect newly-increased stock's change data, also wrap Include：
Judging unit, for judging whether the order to be detected is marked as ordering for stock's update abnormal It is single；
Unit is deleted, if being yes for above-mentioned judged result, deleting newly-increased stock's change data is The abnormal stock changes the testing result of data.
Optionally, also include：
Second acquisition unit, exception during data is changed for the generation newly-increased stock that acquisition is prestored Result；The abnormality processing result storage is in newly-increased stateful transaction change data or the newly-increased storehouse Deposit change data in；
Setting unit, the exception for the abnormality processing result to be changed data as the abnormal stock Reason.
Optionally, when the stock abnormal detection notice of the correspondence order to be detected is listened to, perform described The detection method of stock abnormal data.
Optionally, the detection method of the stock abnormal data is operated in and processed based on real-time distributed calculating In the anomaly data detection platform of framework establishment.
Optionally, also include：
Generation notification unit, for generating the stock abnormal detection notice.
Optionally, the generation notification unit includes：
Synchronous subelement, for by incremental data real-time synchronization device, the newly-increased stateful transaction being changed Data and the newly-increased stock change data syn-chronization to the anomaly data detection platform；
Transmission sub-unit, for receiving the newly-increased stateful transaction change in the anomaly data detection platform After at least one of data and newly-increased stock's change data, if default transmission stock abnormal detection notice Condition is set up, then send the correspondence newly-increased stateful transaction change data or newly-increased stock's change data institute The stock abnormal detection notice of the order of category.
Optionally, the generation notification unit also includes：
Data processing subelement, for according to default data normalization rule, to the newly-increased stateful transaction Change data and newly-increased stock's change data carry out data normalization treatment.
Accordingly, the application also provides a kind of electronic equipment, including：
Display；
Processor；And
Memory, the memory is configured to store the detection means of stock abnormal data, and the stock is different When the detection means of regular data is by the computing device, comprise the following steps：Obtain the new of order to be detected Increase stateful transaction change data and corresponding newly-increased stock change data；According to the newly-increased stateful transaction Change data, detect whether newly-increased stock's change data are abnormal stock's change data.
Compared with prior art, the application has advantages below:
The detection method of stock abnormal data, device and electronic equipment that the application is provided, it is to be checked by obtaining Survey the newly-increased stateful transaction change data and corresponding newly-increased stock change data of order；And according to newly-increased Stateful transaction changes data and the stock of order application to be detected reduces pattern, the newly-increased stock's change data of detection Whether it is abnormal stock's change data, i.e.,：The stock change caused to the change of stateful transaction each time Correctness carries out analysis in real time and judges such that it is able to reach fine-grained detection stock change data, in time It was found that the problem in stock's renewal process.
Brief description of the drawings
Fig. 1 is the flow chart of the detection method embodiment of the stock abnormal data of the application；
Fig. 2 is that the detection method embodiment of the stock abnormal data of the application sends stock abnormal detection notice Flow chart；
Fig. 3 is the signal of the detection method embodiment anomaly data detection platform of the stock abnormal data of the application Figure；
Fig. 4 is a kind of flow chart of the detection method embodiment step S103 of the stock abnormal data of the application；
Fig. 5 is another flow of the detection means embodiment step S103 of the stock abnormal data of the application Figure；
Fig. 6 is another flow of the detection means embodiment step S103 of the stock abnormal data of the application Figure；
Fig. 7 is the schematic diagram of the detection means embodiment of the stock abnormal data of the application；
Fig. 8 is the specific signal of the detection means embodiment detection unit 103 of the stock abnormal data of the application Figure；
Fig. 9 is the specific schematic diagram of the detection means embodiment of the stock abnormal data of the application；
Figure 10 is the specific of the detection means embodiment generation notification unit 213 of the stock abnormal data of the application Schematic diagram；
Figure 11 is the schematic diagram of the electronic equipment embodiment of the application.
Specific embodiment
Elaborate many details in order to fully understand the application in the following description.But the application Can be implemented with being much different from other manner described here, those skilled in the art can without prejudice to Similar popularization is done in the case of the application intension, therefore the application is not limited by following public specific implementation.
In this application, there is provided a kind of detection method of stock abnormal data, device and electronic equipment. It is described in detail one by one in the following examples.
The basic thought of core of the detection method of the stock abnormal data that the application is provided is：Based on order The corresponding stock's change data of stateful transaction change Data Detection whether there is exception, i.e.,：Fine-grained detection Stock changes data.Stock caused by being changed to stateful transaction each time changes and detects, thus Problem present in stock's renewal process can be in real time found, so as to improve the accuracy of commodity stocks data.
Fig. 1 is refer to, it is the flow chart of the detection method embodiment of the stock abnormal data of the application.It is described Method comprises the following steps：
Step S101：Obtain the change of newly-increased stateful transaction data and the corresponding newly-increased storehouse of order to be detected Deposit change data.
Newly-increased stateful transaction change data and newly-increased stock change data described in the embodiment of the present application, the two is equal To belong to the change data of same order, the order that these two aspects data are belonged to together is referred to as treating by the embodiment of the present application Detection order.Wherein, increasing stateful transaction change data newly refers to, produced friendship when being operated to order Easy Status Change data.Order operation includes：The operations such as order generation, payment, delivery or reimbursement operation. Operation to order will change the stateful transaction of order, so as to produce stateful transaction to change data.With operation phase Corresponding, the stateful transaction of order includes：Order status, payment status, delivery state and reimbursement state etc.. Newly-increased stock's change data refer to that newly-increased stock corresponding with newly-increased stateful transaction change data changes data, I.e.：When being operated to order, stateful transaction change data and stock's change aspect data of data two will be produced.
Example 1, shopping website sells a kind of clothes, and the clothes has 200 stocks, wherein it is red 100, It is blue 100, and current commodity all reduces pattern using the stock of " take and subtract stock "；When small red at this When 2, red clothes and arrearage are taken in website, system firstly generates a new order, it is assumed that order Number be 100000001；At the same time, stateful transaction change data and stock's change data will be also produced, i.e.,： Newly-increased stateful transaction change data and newly-increased stock change data；Wherein, stateful transaction change data include： Order number=100000001, the clothes of commodity=red, order status=come into force, payment status=arrearage, Delivery state=non-shipment, reimbursement state=non- reimbursement；It is " take and subtract stock " because stock reduces pattern Pattern, thus stock change data include：Order number=100000001, the clothes of commodity=red, storehouse can be sold Amount of change=- 2 deposited, result=98 that stock can be sold, amount of change=0 for withholding stock, the knot of withholding stock Really=0, take stock amount of change=0, take stock result=0.
Example 2, if small red in example 1 arrearage always after commodity are taken, because time-out causes order to be closed Close, then when order is closed, stateful transaction change data and stock's change data will be produced；Wherein, conclude the business Status Change data include：Order number=100000001, the clothes of commodity=red, order status=come into force, Payment status=order closing, delivery state=non-shipment, reimbursement state=non- reimbursement；Because stock reduces pattern It is the pattern of " take and subtract stock ", therefore stock's change data include：Order number=100000001, commodity= The clothes of red, amount of change=2 that stock can be sold, result=100, the amount of change of the stock that withholds that stock can be sold =0, withhold stock result=0, take stock amount of change=0, take stock result=0.
To change data based on stateful transaction to detect stock's change data with the presence or absence of abnormal, it is necessary first to obtain Take newly-increased stateful transaction change data and corresponding newly-increased stock change data these two aspects data.At this In embodiment, the newly-increased stateful transaction change data and corresponding newly-increased stock for obtaining order to be detected become More data, in the following way：According to the order number of order to be detected, newly-increased stateful transaction change number is obtained Data are changed according to newly-increased stock.
When the newly-increased stateful transaction for getting order to be detected changes data and corresponding newly-increased stock change After data these two aspects data, it is possible to enter step S103 and be based on stateful transaction change data to stock's change Data are detected with the presence or absence of abnormal.
It should be noted that the stateful transaction change data and stock's change data in practical application are generally originated In different systems, i.e.,：Stateful transaction changes data source in transaction system, stock change data source in Inventory management system.The data of different system become to be even more sequencing, if the change of each data is all The method that the embodiment of the present application of triggering is provided, then probably due to another aspect data are also less than, cause There is the result of erroneous judgement.Should although hereafter can be corrected by detection again again after data are obtained both ways Mistake result of determination, but but greatly reduce detection efficiency.It can be seen that, if transaction shape can got State is detected, will improve detection efficiency and be again after changing data and stock's change aspect data of data two System performance.
In order to be detected again after two aspect data are got, the method that the embodiment of the present application is provided is Performed when the stock abnormal detection notice of correspondence order to be detected is listened to.
Fig. 2 is refer to, it is the detection method embodiment transmission stock abnormal inspection of the stock abnormal data of the application Survey the flow chart for notifying.In the present embodiment, stock abnormal detection notice, can be generated using following steps：
Step S201：By incremental data real-time synchronization device, by the newly-increased stateful transaction change data and The newly-increased stock changes data syn-chronization to the anomaly data detection platform.
When concrete operations are carried out to order, can be by incremental data real-time synchronization device, by transaction system The newly-increased stock change data syn-chronization that the newly-increased stateful transaction change data and inventory management system for producing are produced is arrived In anomaly data detection platform.
For the ease of realizing anomaly data detection platform, the embodiment of the present application is based at real-time distributed calculating Reason framework establishment anomaly data detection platform.Fig. 3 is refer to, it is the detection of the stock abnormal data of the application The schematic diagram of embodiment of the method anomaly data detection platform.DRC in Fig. 3 is incremental data real-time synchronization dress Put, examined the real-time synchronization of transaction system and the incremental data of inventory management system to abnormal data by DRC Platform is surveyed, as the data source of tradeSpout and invSpout.Detection platform include tradeSpout and Two data sources of invSpout, and tri- computing modules of etlBolt, actionBolt and checkBolt.Wherein, TradeSpout data sources include newly-increased stateful transaction change data, and invSpout data sources include that newly-increased stock becomes More data.Due to data source by DRC access come in, therefore tradeSpout and invSpout's and Hair number is related to the topic of data source, the two mutual correspondence.
EtlBolt computing modules in Fig. 3 detection platforms subscribe to the change data of tradeSpout and invSpout, Used as the next stage of tradeSpout and invSpout data sources, etlBolt has isolated data source quantity physically Relation, can freely improve number of concurrent.After etlBolt gets any one newly-increased data, by newly-increased number According to order number launch.
Preferably, data and newly-increased stock can also be changed to newly-increased stateful transaction by etlBolt computing modules Change Data Data is pre-processed (processed including data filtering etc.), so as to real when will detect abnormal In the data Cun Chudao HBASE middle tables of needs.
Step S203：The anomaly data detection platform receive the newly-increased stateful transaction change data and After at least one of the newly-increased stock change data, if default transmission stock abnormal detection notice condition into It is vertical, then send the correspondence newly-increased stateful transaction change data or the newly-increased stock changes ordering belonging to data Single stock abnormal detection notice.
The method that the embodiment of the present application is provided is to receive newly-increased stateful transaction change in anomaly data detection platform After at least one of data and newly-increased stock change data, stock abnormal is controlled by actionBolt computing modules The transmission of detection notice.ActionBolt computing modules are used to control to send stock abnormal detection notice, when default Transmission stock abnormal detection notice condition set up when order number is launched.
Specifically, default transmission stock abnormal detection notice condition includes：Current time with receive it is described Between the time interval of newly-increased stateful transaction change data or newly-increased stock's change data reaches the default time Every, or memory headroom shared by order to be detected reaches default memory headroom.Wherein, first bar Part is sent after the prefixed time interval after getting first newly-increased data for being ordered belonging to the newly-increased data Single stock abnormal detection notice, i.e.,：Assuming that two aspect data arrival time interval should be in Preset Time In interval；Above-mentioned second condition is when the memory headroom of the newly-increased data of storage reaches default internal memory maximum magnitude When, send the stock abnormal detection notice for the newly-increased affiliated order of data.
By above-mentioned steps S201 and step S203, can control to perform the opportunity of the method that the application is provided. CheckBolt computing modules in detection platform subscribe to the data of actionBolt, when receiving actionBolt During the stock abnormal detection notice of transmission, the order number in notice takes out the order number from HBASE Newly-increased stateful transaction change data and newly-increased stock change data, perform the embodiment of the present application provide method, The detection of stock abnormal data is carried out, if detecting data exception, by abnormal data output to database In.The embodiment of the present application by the detection logic of stock abnormal data, as the business of anomaly data detection platform Plug-in unit, real time execution gets up in detection platform by way of JAR bags, realizes business objective.
The embodiment of the present application processes framework STORM and HBASE number by existing real-time distributed calculating Anomaly data detection platform is built according to storage system, the process of platform construction is not only simplify, and build Detection platform will not be produced independently of transaction system and inventory management system to transaction system and inventory management system Raw burden.
Step S103：According to the newly-increased stateful transaction change data and the stock of the order application to be detected Pattern is reduced, detects whether newly-increased stock's change data are abnormal stock's change data.
When the newly-increased stateful transaction for getting order to be detected changes data and corresponding newly-increased stock change After data, it is possible to change data based on stateful transaction to detect stock's change data with the presence or absence of abnormal.
Stock's pattern of reducing described in the embodiment of the present application is provided by inventory management system, for example, taking Subtract inventory mode or payment subtracts stock's isotype.Transaction system determines to use any stock according to service needed Reduce pattern.Therefore, stock's pattern of reducing can be stored in stateful transaction change data.Stock changes number It is relevant according to stock pattern is reduced, under identical stateful transaction change data, if stock reduces pattern not Together, then the stock's change data for producing are also different.The method that the embodiment of the present application is provided is based on known Stock reduces the major premise of pattern, and changing the newly-increased stock's change data of Data Detection according to newly-increased stateful transaction is No exception.
In actual applications, can whether abnormal using the newly-increased stock's change data of various detection schemes detection. Three kinds of optional embodiments of step S103 are given below：
1) scheme one
The basic thought of scheme one is to realize changing the detection that data change data to stock from stateful transaction, i.e.,： The actual value and desired value of stock's change data are compared.Refer to Fig. 4, its be the application stock it is different A kind of flow chart of the detection method embodiment step S103 of regular data.The step of scheme one, S103 included：
Step S1031：According to the newly-increased stateful transaction change data, the current of the order to be detected is obtained Stateful transaction.
Scheme one is firstly the need of the current transaction shape that order to be detected is obtained from newly-increased stateful transaction change data State.Illustrated with the current stateful transaction of example 1 pair in step S101, increase stateful transaction change in the example newly Data include：Order number=100000001, the clothes of commodity=red, order status=come into force, shape of paying the bill State=arrearage, delivery state=non-shipment, reimbursement state=non- reimbursement, wherein current stateful transaction is：Order State=come into force, payment status=arrearage, delivery state=non-shipment, reimbursement state=non- reimbursement.
Step S1033：Stock according to the current stateful transaction and the order application to be detected reduces mould Formula, calculates the desired value that the newly-increased stock changes data.
Stock according to current stateful transaction and order application to be detected reduces pattern, can calculate newly-increased storehouse Deposit the desired value of change data.Continuation is illustrated with 1 pair of this step of example in step S101, due to current Commodity all reduce pattern using the stock of " take and subtract stock ", and current stateful transaction is：Order status= Come into force, payment status=arrearage, delivery state=non-shipment, reimbursement state=non- reimbursement, therefore stock become The desired value of more data should be：Order number=100000001, the clothes of commodity=red, the change that stock can be sold More amount=- 2, result=98 that stock can be sold, amount of change=0 of the stock that withholds, result=0 of the stock that withholds, account for With amount of change=0 of stock, result=0 of occupancy stock.
In actual applications, the meter that stock changes data predicting value can be set according to specific application demand Rule is calculated, so that the stock of " take and subtract stock " reduces pattern as an example, applicable computation rule includes：1) When order status are Pending The Entry Into Force states, if can sell stock subtracted, desired value can sell stock for covering；2) When order status are active states, it is contemplated that be worth and subtracted for stock can be sold；3) closed before order status are payment When closing, if can sell stock subtracted, desired value can sell stock for covering.Above-mentioned a variety of calculating is advised Then, the simply change of specific embodiment, all without departing from the core of the application, therefore all in the guarantor of the application Within the scope of shield.
Step S1035：Judge whether newly-increased stock's change data and the desired value are identical.
The actual change data newly-increased stock changed in data change data with the stock that step S1033 is obtained Desired value contrasted, judge whether the two consistent.
Step S1037：If so, then judging that newly-increased stock's change data change data as normal stock.
If the actual value in newly-increased stock's change data is identical with desired value, can determine that newly-increased stock becomes More data are that normal stock changes data.
Step S1039：If it is not, then judging that newly-increased stock's change data change number as the abnormal stock According to.
Opposite, if the actual value in newly-increased stock's change data is differed with desired value, can determine that Newly-increased stock's change data are abnormal stock's change data.
By scheme one, can realize changing data to the different of stock's change data this dimensions from stateful transaction Normal inventory data detection.
2) scheme two
With scheme one conversely, the basic thought of scheme two is to realize that change data from stock changes to stateful transaction The detection of data, i.e.,：The actual value and desired value of change data of concluding the business are compared.Fig. 5 is refer to, its It is another flow chart of the detection method embodiment step S103 of the stock abnormal data of the application.Described According to the newly-increased stateful transaction change data, detect whether newly-increased stock's change data are abnormal stock Change data, including：
Step S1031 '：According to the newly-increased stateful transaction change data, working as the order to be detected is obtained Preceding stateful transaction.
Step S1031 ' is identical with step S1031, and here is omitted.
Step S1033 '：Stock according to the order application to be detected reduces pattern and the newly-increased stock becomes More data, generate the forecasted transaction state of the order to be detected.
Pattern is reduced according to the stock that newly-increased stock changes data and order application to be detected, can reversely be derived Go out the forecasted transaction state of order to be detected.Still illustrated with 1 pair of this step of example in step S101, by Pattern is all reduced using the stock of " take and subtract stock " in current commodity, and stock's change data are：Order Odd numbers=100000001, the clothes of commodity=red, amount of change=- 2 that stock can be sold, result that stock can be sold =98, withhold amount of change=0 of stock, result=0 of the stock that withholds, amount of change=0 for taking stock, take storehouse Result=0 deposited, therefore currently the desired value of stateful transaction should be：Order status=come into force, payment status =arrearage, delivery state=non-shipment, reimbursement state=non- reimbursement.
Step S1035 '：Judging the current stateful transaction and the forecasted transaction state of the order to be detected is It is no identical.
By the real trade state in newly-increased stateful transaction change data with the forecasted transaction state for getting one by one Contrasted, judged whether the actual value of various stateful transactions is consistent with desired value.
Step S1037 '：If so, then judging that newly-increased stock's change data change data as normal stock.
If the actual value in newly-increased stateful transaction change data is identical with desired value, newly-increased storehouse is can determine that It is that normal stock changes data to deposit change data.
Step S1037 '：If it is not, then judging that newly-increased stock's change data are abnormal stock's change Data.
Opposite, if the actual value in newly-increased stateful transaction change data is differed with desired value, can Judge that newly-increased stock's change data change data as abnormal stock.
By scheme two, can realize changing data to the different of stateful transaction change data this dimensions from stock Normal inventory data detection.
3) scheme three
Scheme three is that scheme one is combined with scheme two, realizes that change data from stateful transaction changes to stock Data, and change two detections of dimension that data change data to stateful transaction from stock.Refer to Fig. 6, It is another flow chart of the detection method embodiment step S103 of the stock abnormal data of the application.It is described According to the newly-increased stateful transaction change data, detect whether newly-increased stock's change data are abnormal storehouse Deposit change data often, including：
Step S1031 "：According to the newly-increased stateful transaction change data, working as the order to be detected is obtained Preceding stateful transaction.
Step S1031 " is identical with step S1031, and here is omitted.
Step S1033 "：Stock according to the current stateful transaction and the order application to be detected reduces mould Formula, calculates the desired value that the newly-increased stock changes data；And according to the storehouse of the order application to be detected The pattern of reducing and newly-increased stock's change data are deposited, the forecasted transaction state of the order to be detected is generated.
This step combines above-mentioned steps S1033 and step S1033 ', and here is omitted.
Step S1035 "：Judge whether newly-increased stock's change data and the desired value are identical, Yi Jisuo Whether the current stateful transaction and the forecasted transaction state for stating order to be detected are identical.
This step judges whether the actual value of newly-increased stock's change data is identical with desired value, and to be detected orders Whether single current stateful transaction and forecasted transaction state be identical.
Step S1037 "：If so, then judging that newly-increased stock's change data change data as normal stock.
If the actual value in newly-increased stock's change data is identical with desired value, and order to be detected is current Stateful transaction and forecasted transaction state are also identical, then can determine that newly-increased stock's change data are normal stock Change data.
Step S1039 "：If it is not, then judging that newly-increased stock's change data are abnormal stock's change Data.
Opposite, if the actual value in newly-increased stock's change data is differed with desired value, or it is to be detected The current stateful transaction and forecasted transaction state of order are differed, then can determine that newly-increased stock's change data are Abnormal stock's change data.
In actual applications, one of such scheme can be selected to realize step according to specific application demand S103.Above-mentioned a variety of detection schemes, the simply change of specific embodiment, all without departing from the application Core, therefore all within the protection domain of the application.
If it should be noted that it is abnormal stock's change that step S103 detects newly-increased stock's change data Data, because the exception is probably due to lacking the erroneous judgement that the original such as newly-increased data of one side is thus resulted in, being Enable that the later stage corrects to erroneous judgement result, the method that the embodiment of the present application is provided also includes：Storage Newly-increased stock's change data are the step of abnormal stock changes the testing result of data.Also, for the ease of Later stage is easy to find and is judged to abnormal stock's change data, and preferred method is by hand marker to be detected It is the order of stock's update abnormal.Accordingly, when being detected to every a pair newly-increased data, if detection It is that normal stock changes data to newly-increased stock change data, then detection method also includes：Judge to be detected Whether order is marked as the order of stock's update abnormal.If it find that the order has been labeled as stock more New abnormal order, then it is the detection knot that abnormal stock changes data to need to delete newly-increased stock's change data Really, it is achieved in the correction to judging result by accident.
By above-mentioned steps S101 and step S103, can be to the stock caused by the change of stateful transaction each time Change data are detected, real-time to find problem present in stock's renewal process, so as to improve commodity stocks The accuracy of data.
In actual applications, not only need to detect abnormal stock's change data, in addition it is also necessary to orient exception The producing cause of data, to determine effective solution, so as to accomplish to stop loss in time.By the application reality The method for applying example offer, can directly determine the abnormal cause compared with fineness degree, for example, can sell stock should subtract not Subtract or the stock covering can be sold and do not cover.But, in actual applications, in addition it is also necessary to analyze abnormal data The fine granularity reason of generation, for example, it is that can sell stock not to cause that stock can be sold the further reason that should do not subtract Foot, or commodity data be deleted etc..The method that prior art passes through artificial lookup inventory management system daily record, To obtain the fine granularity reason that abnormal stock updates the data generation.Due to the method be by manually-operated, It is a great engineering so that consuming substantial amounts of manpower.
In order to solve prior art presence cannot be automatically positioned the fine granularity that abnormal stock updates the data generation The problem of reason, the method that the embodiment of the present application is provided also includes：1) generation for prestoring is obtained described new Increase abnormality processing result during stock's change data；The abnormality processing result storage is in the newly-increased transaction shape State is changed in data or newly-increased stock's change data；2) using the abnormality processing result as the exception Stock change data abnormal cause.
Abnormality processing result described in the embodiment of the present application refers to that inventory management system is in generation stock's change number According to when the error code or error description that externally return.Inventory management system generate stock change data when, one As can all analyze failure cause, then externally return to corresponding error code and error description, and these are wrong Error code and error description recorded in abnormal log file.Prior art is to search abnormal log text by artificial The method of part obtains fine-grained abnormal cause.
The method that the embodiment of the present application is provided is that above-mentioned abnormality processing result is stored in advance in into newly-increased stateful transaction In change data or newly-increased stock change data so that when judging that newly-increased stock's change data are abnormal data When, can be gone out so as to get stock's change data by directly reading the abnormality processing result for prestoring Now abnormal fine granularity reason.
Preferably, abnormality processing result is stored in advance in newly-increased stateful transaction change data so that new In the case of increasing stateful transaction change data and newly-increased stock change data are nonsynchronous, abnormality processing knot can be used Fruit substitutes newly-increased stock's change data, and data are changed with the stock for determining exception, reaches and reduces abnormal data The effect of False Rate.
Specifically, inventory management system provides stock's interface so that transaction system is called under the scene for needing. The return information that stock's interface mappings are brief numeral is stored in newly-increased stateful transaction change data by transaction system In.
It is in the above-described embodiment, there is provided a kind of detection method of stock abnormal data, corresponding, The application also provides a kind of detection means of stock abnormal data.The device is the embodiment phase with the above method Correspondence.
Fig. 7 is refer to, it is the schematic diagram of the detection means embodiment of the stock abnormal data of the application.Due to Device embodiment is substantially similar to embodiment of the method, so describing fairly simple, related part is referring to method The part explanation of embodiment.Device embodiment described below is only schematical.
A kind of detection means of the stock abnormal data of the present embodiment, including：
First acquisition unit 101, the newly-increased stateful transaction for obtaining order to be detected changes data and right with it The newly-increased stock change data answered；
Detection unit 103, for according to newly-increased stateful transaction change data and the order application to be detected Stock reduce pattern, detect whether newly-increased stock's change data are abnormal stock's change data.
Fig. 8 is refer to, it is the detection means embodiment detection unit 103 of the stock abnormal data of the application Specific schematic diagram.Optionally, the detection unit 103 includes：
Subelement 1031 is obtained, for according to the newly-increased stateful transaction change data, obtaining described to be detected The current stateful transaction of order；
Computation subunit 1033, for the storehouse according to the current stateful transaction and the order application to be detected The pattern of reducing is deposited, the desired value that the newly-increased stock changes data is calculated；
Whether judgment sub-unit 1035 is identical for judging newly-increased stock's change data and the desired value；
Judge normal subelement 1037, if being yes for above-mentioned judged result, judge the newly-increased stock Change data are that normal stock changes data；
Judge abnormal subelement 1039, if being no for above-mentioned judged result, judge the newly-increased stock Change data are abnormal stock's change data.
Optionally, the detection unit 103 includes：
Subelement is obtained, for according to the newly-increased stateful transaction change data, obtaining the order to be detected Current stateful transaction；
Computation subunit, for reducing pattern and the newly-increased storehouse according to the stock of the order application to be detected Change data are deposited, the forecasted transaction state of the order to be detected is generated；
Judgment sub-unit, current stateful transaction and the forecasted transaction shape for judging the order to be detected Whether state is identical；
Judge normal subelement, if being yes for above-mentioned judged result, judge newly-increased stock's change Data are that normal stock changes data；
Judge abnormal subelement, if being no for above-mentioned judged result, judge newly-increased stock's change Data are abnormal stock's change data.
Optionally, the detection unit 103 includes：
Subelement is obtained, for according to the newly-increased stateful transaction change data, obtaining the order to be detected Current stateful transaction；
Computation subunit, for being buckled according to the stock of the current stateful transaction and the order application to be detected Size reduction mode, calculates the desired value that the newly-increased stock changes data；And according to the order application to be detected Stock reduce pattern and newly-increased stock change data, generate the forecasted transaction shape of the order to be detected State；
Judgment sub-unit, it is whether identical for judging newly-increased stock's change data and the desired value, with And whether the current stateful transaction and the forecasted transaction state of the order to be detected are identical；
Judge normal subelement, if being yes for above-mentioned judged result, judge newly-increased stock's change Data are that normal stock changes data；
Judge abnormal subelement, if being no for above-mentioned judged result, judge newly-increased stock's change Data are abnormal stock's change data.
Fig. 9 is refer to, it is the specific schematic diagram of the detection means embodiment of the stock abnormal data of the application. Optionally, if it is abnormal stock's change data to detect newly-increased stock's change data, also wrap Include：
Storage result unit 201, is abnormal stock's change for storing newly-increased stock's change data The testing result of data.
Optionally, also include：
Indexing unit 203, for being the order of stock's update abnormal by the hand marker to be detected.
Optionally, if it is that normal stock changes data to detect newly-increased stock's change data, also wrap Include：
Judging unit 205, for judging whether the order to be detected is marked as stock's update abnormal Order；
Unit 207 is deleted, if being yes for above-mentioned judged result, newly-increased stock's change data is deleted The testing result of data is changed for the abnormal stock.
Optionally, also include：
Second acquisition unit 209, it is different during the generation that the prestores newly-increased stock change data for obtaining Normal result；The abnormality processing result storage is in the newly-increased stateful transaction change data or described newly-increased In stock's change data；
Setting unit 211, for the result is former as the exception of abnormal stock's change data Cause.
Optionally, when the stock abnormal detection notice of the correspondence order to be detected is listened to, perform described The detection method of stock abnormal data.
Optionally, the detection method of the stock abnormal data is operated in and processed based on real-time distributed calculating In the anomaly data detection platform of framework establishment.
Optionally, also include：
Generation notification unit 213, for generating the stock abnormal detection notice.
Figure 10 is refer to, it is the detection means embodiment generation notification unit of the stock abnormal data of the application 213 specific schematic diagram.Optionally, the generation notification unit 213 includes：
Synchronous subelement 2131, for by incremental data real-time synchronization device, by the newly-increased stateful transaction Change data and the newly-increased stock change data syn-chronization to the anomaly data detection platform；
Transmission sub-unit 2133, for receiving the newly-increased stateful transaction in the anomaly data detection platform After at least one of change data and newly-increased stock's change data, if default transmission stock abnormal detection Notification condition is set up, then send the correspondence newly-increased stateful transaction change data or newly-increased stock's change number According to the stock abnormal detection notice of affiliated order.
Optionally, the generation notification unit 213 also includes：
Data processing subelement 2132, for according to default data normalization rule, to the newly-increased transaction Status Change data and newly-increased stock's change data carry out data normalization treatment.
Figure 11 is refer to, it is the schematic diagram of the electronic equipment embodiment of the application.Due to apparatus embodiments base This is similar in appearance to embodiment of the method, so describe fairly simple, referring to the part of embodiment of the method in place of correlation Illustrate.Apparatus embodiments described below are only schematical.
The a kind of electronic equipment of the present embodiment, the electronic equipment includes：Display 1101；Processor 1102； And memory 1103, the memory 1103 is configured to store the detection means of stock abnormal data, institute When the detection means for stating stock abnormal data is performed by the processor 1102, comprise the following steps：Acquisition is treated Detect the newly-increased stateful transaction change data and corresponding newly-increased stock change data of order；According to described Newly-increased stateful transaction change data, detect whether newly-increased stock's change data are abnormal stock's change number According to.
The detection method of stock abnormal data, device and electronic equipment that the application is provided, it is to be checked by obtaining Survey the newly-increased stateful transaction change data and corresponding newly-increased stock change data of order；And according to newly-increased Stateful transaction changes data and the stock of order application to be detected reduces pattern, the newly-increased stock's change data of detection Whether it is abnormal stock's change data, i.e.,：The stock change caused to the change of stateful transaction each time Correctness carries out analysis in real time and judges such that it is able to reach fine-grained detection stock change data, in time It was found that the problem in stock's renewal process.
Although the application is disclosed as above with preferred embodiment, it is not for limiting the application, Ren Heben Art personnel are not being departed from spirit and scope, can make possible variation and modification, Therefore the scope that the protection domain of the application should be defined by the application claim is defined.
In a typical configuration, computing device includes one or more processors (CPU), input/output Interface, network interface and internal memory.
Internal memory potentially includes the volatile memory in computer-readable medium, random access memory And/or the form, such as read-only storage (ROM) or flash memory (flash RAM) such as Nonvolatile memory (RAM). Internal memory is the example of computer-readable medium.
1st, computer-readable medium includes that permanent and non-permanent, removable and non-removable media can be by Any method or technique realizes information Store.Information can be computer-readable instruction, data structure, journey The module of sequence or other data.The example of the storage medium of computer includes, but are not limited to phase transition internal memory (PRAM), static RAM (SRAM), dynamic random access memory (DRAM), its The random access memory (RAM) of his type, read-only storage (ROM), electrically erasable is read-only deposits Reservoir (EEPROM), fast flash memory bank or other memory techniques, read-only optical disc read-only storage (CD-ROM), digital versatile disc (DVD) or other optical storages, magnetic cassette tape, tape magnetic magnetic Disk storage or other magnetic storage apparatus or any other non-transmission medium, can be used for storage can be set by calculating The standby information for accessing.Defined according to herein, computer-readable medium does not include non-temporary computer-readable matchmaker Body (transitory media), such as data-signal and carrier wave of modulation.
2nd, it will be understood by those skilled in the art that embodiments herein can be provided as method, system or computer Program product.Therefore, the application can use complete hardware embodiment, complete software embodiment or combine software With the form of the embodiment of hardware aspect.And, the application can be used and wherein include meter at one or more Calculation machine usable program code computer-usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) on implement computer program product form.

Claims (29)

1. a kind of detection method of stock abnormal data, it is characterised in that including：
Obtain the newly-increased stateful transaction change data and corresponding newly-increased stock change number of order to be detected According to；
Stock according to the newly-increased stateful transaction change data and the order application to be detected reduces pattern, Detect whether newly-increased stock's change data are abnormal stock's change data.
2. the detection method of stock abnormal data according to claim 1, it is characterised in that described Pattern is reduced according to the stock of the newly-increased stateful transaction change data and the order application to be detected, institute is detected State whether newly-increased stock's change data are abnormal stock's change data, including：
According to the newly-increased stateful transaction change data, the current stateful transaction of the order to be detected is obtained；
Stock according to the current stateful transaction and the order application to be detected reduces pattern, calculates described Newly-increased stock changes the desired value of data；
Judge whether newly-increased stock's change data and the desired value are identical；
If so, then judging that newly-increased stock's change data change data as normal stock；
If it is not, then judging that newly-increased stock's change data change data as the abnormal stock.
3. the detection method of stock abnormal data according to claim 1, it is characterised in that described Pattern is reduced according to the stock of the newly-increased stateful transaction change data and the order application to be detected, institute is detected State whether newly-increased stock's change data are abnormal stock's change data, including：
According to the newly-increased stateful transaction change data, the current stateful transaction of the order to be detected is obtained；
Stock according to the order application to be detected reduces pattern and newly-increased stock's change data, generation The forecasted transaction state of the order to be detected；
Judge whether the current stateful transaction and the forecasted transaction state of the order to be detected are identical；
If so, then judging that newly-increased stock's change data change data as normal stock；
If it is not, then judging that newly-increased stock's change data change data as the abnormal stock.
4. the detection method of stock abnormal data according to claim 1, it is characterised in that described Pattern is reduced according to the stock of the newly-increased stateful transaction change data and the order application to be detected, institute is detected State whether newly-increased stock's change data are abnormal stock's change data, including：
According to the newly-increased stateful transaction change data, the current stateful transaction of the order to be detected is obtained；
Stock according to the current stateful transaction and the order application to be detected reduces pattern, calculates described Newly-increased stock changes the desired value of data；And according to the stock of the order application to be detected reduce pattern and Newly-increased stock's change data, generate the forecasted transaction state of the order to be detected；
Judge whether newly-increased stock's change data and the desired value are identical, and the order to be detected Current stateful transaction and the forecasted transaction state it is whether identical；
If so, then judging that newly-increased stock's change data change data as normal stock；
If it is not, then judging that newly-increased stock's change data change data as the abnormal stock.
5. the detection method of stock abnormal data according to claim 1, it is characterised in that if inspection It is abnormal stock's change data to measure newly-increased stock's change data, is also included：
It is the testing result that the abnormal stock changes data to store newly-increased stock's change data.
6. the detection method of stock abnormal data according to claim 5, it is characterised in that also include：
It is the order of stock's update abnormal by the hand marker to be detected.
7. the detection method of stock abnormal data according to claim 6, it is characterised in that if inspection It is that normal stock changes data to measure newly-increased stock's change data, is also included：
Judge whether the order to be detected is marked as the order of stock's update abnormal；
If so, it is the detection knot that the abnormal stock changes data then to delete newly-increased stock's change data Really.
8. the detection method of stock abnormal data according to claim 1, it is characterised in that described to obtain The newly-increased stateful transaction change data and corresponding newly-increased stock change data of order to be detected are taken, is used Following manner：
According to the order number of the order to be detected, newly-increased stateful transaction change data and described new are obtained Increase stock's change data.
9. the detection method of stock abnormal data according to claim 1, it is characterised in that also include：
The generation newly-increased stock that acquisition is prestored changes abnormality processing result during data；The exception Result storage is in the newly-increased stateful transaction change data or newly-increased stock's change data；
The abnormality processing result is changed the abnormal cause of data as the abnormal stock.
10. the detection method of stock abnormal data according to claim 9, it is characterised in that described Result storage is in the newly-increased stateful transaction change data；Exist by abnormality processing result storage Before in the newly-increased stateful transaction change data, also include：
The stock's interface provided by inventory management system, obtains the abnormality processing result.
The detection method of 11. stock abnormal data according to claim 1, it is characterised in that work as monitoring During to the stock abnormal detection notice for corresponding to the order to be detected, the detection of the stock abnormal data is performed Method.
The detection method of 12. stock abnormal data according to claim 11, it is characterised in that described The detection method of stock abnormal data operates in the abnormal number based on real-time distributed calculating treatment framework establishment According in detection platform.
The detection method of 13. stock abnormal data according to claim 12, it is characterised in that described Stock abnormal detection notice, is generated using following steps：
By incremental data real-time synchronization device, by newly-increased stateful transaction change data and the newly-increased storehouse Deposit change data syn-chronization to the anomaly data detection platform；
Newly-increased stateful transaction change data and the newly-increased storehouse are received in the anomaly data detection platform After depositing at least one of change data, if default transmission stock abnormal detection notice condition is set up, send The stock of the order belonging to the correspondence newly-increased stateful transaction change data or newly-increased stock's change data is different Normal detection notice.
The detection method of 14. stock abnormal data according to claim 13, it is characterised in that described Default transmission stock abnormal detection notice condition includes：Current time with receive the newly-increased stateful transaction The time interval of change data or newly-increased stock's change data reaches default time interval, or to be checked Survey the memory headroom shared by order and reach default memory headroom.
The detection method of 15. stock abnormal data according to claim 12, it is characterised in that in institute State and the newly-increased stateful transaction change data and the newly-increased stock are changed into data syn-chronization to the abnormal data After detection platform, also include：
According to default data normalization rule, to newly-increased stateful transaction change data and the newly-increased storehouse Depositing change data carries out data normalization treatment.
A kind of 16. detection means of stock abnormal data, it is characterised in that including：
First acquisition unit, the newly-increased stateful transaction for obtaining order to be detected changes data and corresponding Newly-increased stock change data；
Detection unit, for according to the newly-increased stateful transaction change data and the order application to be detected Stock reduces pattern, detects whether newly-increased stock's change data are abnormal stock's change data.
The detection means of 17. stock abnormal data according to claim 16, it is characterised in that described Detection unit includes：
Subelement is obtained, for according to the newly-increased stateful transaction change data, obtaining the order to be detected Current stateful transaction；
Computation subunit, for being buckled according to the stock of the current stateful transaction and the order application to be detected Size reduction mode, calculates the desired value that the newly-increased stock changes data；
Whether judgment sub-unit is identical for judging newly-increased stock's change data and the desired value；
Judge normal subelement, if being yes for above-mentioned judged result, judge newly-increased stock's change Data are that normal stock changes data；
Judge abnormal subelement, if being no for above-mentioned judged result, judge newly-increased stock's change Data are abnormal stock's change data.
The detection means of 18. stock abnormal data according to claim 16, it is characterised in that described Detection unit includes：
Subelement is obtained, for according to the newly-increased stateful transaction change data, obtaining the order to be detected Current stateful transaction；
Computation subunit, for reducing pattern and the newly-increased storehouse according to the stock of the order application to be detected Change data are deposited, the forecasted transaction state of the order to be detected is generated；
Judgment sub-unit, current stateful transaction and the forecasted transaction shape for judging the order to be detected Whether state is identical；
Judge normal subelement, if being yes for above-mentioned judged result, judge newly-increased stock's change Data are that normal stock changes data；
Judge abnormal subelement, if being no for above-mentioned judged result, judge newly-increased stock's change Data are abnormal stock's change data.
The detection means of 19. stock abnormal data according to claim 16, it is characterised in that described Detection unit includes：
Subelement is obtained, for according to the newly-increased stateful transaction change data, obtaining the order to be detected Current stateful transaction；
Computation subunit, for being buckled according to the stock of the current stateful transaction and the order application to be detected Size reduction mode, calculates the desired value that the newly-increased stock changes data；And according to the order application to be detected Stock reduce pattern and newly-increased stock change data, generate the forecasted transaction shape of the order to be detected State；
Judgment sub-unit, it is whether identical for judging newly-increased stock's change data and the desired value, with And whether the current stateful transaction and the forecasted transaction state of the order to be detected are identical；
Judge normal subelement, if being yes for above-mentioned judged result, judge newly-increased stock's change Data are that normal stock changes data；
Judge abnormal subelement, if being no for above-mentioned judged result, judge newly-increased stock's change Data are abnormal stock's change data.
The detection means of 20. stock abnormal data according to claim 16, it is characterised in that if It is abnormal stock's change data to detect newly-increased stock's change data, is also included：
Storage result unit, is that the abnormal stock changes number for storing newly-increased stock's change data According to testing result.
The detection means of 21. stock abnormal data according to claim 20, it is characterised in that also wrap Include：
Indexing unit, for being the order of stock's update abnormal by the hand marker to be detected.
The detection means of 22. stock abnormal data according to claim 21, it is characterised in that if It is that normal stock changes data to detect newly-increased stock's change data, is also included：
Judging unit, for judging whether the order to be detected is marked as ordering for stock's update abnormal It is single；
Unit is deleted, if being yes for above-mentioned judged result, deleting newly-increased stock's change data is The abnormal stock changes the testing result of data.
The detection means of 23. stock abnormal data according to claim 16, it is characterised in that also wrap Include：
Second acquisition unit, exception during data is changed for the generation newly-increased stock that acquisition is prestored Result；The abnormality processing result storage is in newly-increased stateful transaction change data or the newly-increased storehouse Deposit change data in；
Setting unit, the exception for the abnormality processing result to be changed data as the abnormal stock Reason.
The detection means of 24. stock abnormal data according to claim 16, it is characterised in that work as prison When hearing the stock abnormal detection notice of the correspondence order to be detected, the inspection of the stock abnormal data is performed Survey method.
The detection means of 25. stock abnormal data according to claim 24, it is characterised in that described The detection method of stock abnormal data operates in the abnormal number based on real-time distributed calculating treatment framework establishment According in detection platform.
The detection means of 26. stock abnormal data according to claim 25, it is characterised in that also wrap Include：
Generation notification unit, for generating the stock abnormal detection notice.
The detection means of 27. stock abnormal data according to claim 26, it is characterised in that described Generation notification unit includes：
Synchronous subelement, for by incremental data real-time synchronization device, the newly-increased stateful transaction being changed Data and the newly-increased stock change data syn-chronization to the anomaly data detection platform；
Transmission sub-unit, for receiving the newly-increased stateful transaction change in the anomaly data detection platform After at least one of data and newly-increased stock's change data, if default transmission stock abnormal detection notice Condition is set up, then send the correspondence newly-increased stateful transaction change data or newly-increased stock's change data institute The stock abnormal detection notice of the order of category.
The detection means of 28. stock abnormal data according to claim 26, it is characterised in that described Generation notification unit also includes：
Data processing subelement, for according to default data normalization rule, to the newly-increased stateful transaction Change data and newly-increased stock's change data carry out data normalization treatment.
29. a kind of electronic equipment, it is characterised in that including：
Display；
Processor；And
Memory, the memory is configured to store the detection means of stock abnormal data, and the stock is different When the detection means of regular data is by the computing device, comprise the following steps：Obtain the new of order to be detected Increase stateful transaction change data and corresponding newly-increased stock change data；According to the newly-increased stateful transaction Change data, detect whether newly-increased stock's change data are abnormal stock's change data.
CN201510882926.2A 2015-12-04 2015-12-04 A kind of detection method of stock abnormal data, device and electronic equipment Pending CN106845881A (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN201510882926.2A CN106845881A (en) 2015-12-04 2015-12-04 A kind of detection method of stock abnormal data, device and electronic equipment
PCT/CN2016/107016 WO2017092599A1 (en) 2015-12-04 2016-11-24 Method and apparatus for detecting abnormal inventory data, and electronic device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201510882926.2A CN106845881A (en) 2015-12-04 2015-12-04 A kind of detection method of stock abnormal data, device and electronic equipment

Publications (1)

Publication Number Publication Date
CN106845881A true CN106845881A (en) 2017-06-13

Family

ID=58796277

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201510882926.2A Pending CN106845881A (en) 2015-12-04 2015-12-04 A kind of detection method of stock abnormal data, device and electronic equipment

Country Status (2)

Country Link
CN (1) CN106845881A (en)
WO (1) WO2017092599A1 (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107633343A (en) * 2017-08-09 2018-01-26 杭州洋驼网络科技有限公司 Transaction data changes risk recognition system and method
CN109815262A (en) * 2018-12-27 2019-05-28 四川驹马科技有限公司 A kind of inventory management method and its system based on WAL mode
CN109872100A (en) * 2018-12-28 2019-06-11 航天信息股份有限公司 A kind of product oil inventory change management method and system
CN111125180A (en) * 2018-10-30 2020-05-08 千寻位置网络有限公司 Inventory data statistical method and device, server and distributed purchasing system
CN113807923A (en) * 2021-09-23 2021-12-17 杭州右文网络科技有限公司 Order processing method and device, electronic equipment and readable storage medium
CN114548952A (en) * 2022-04-22 2022-05-27 武汉中科通达高新技术股份有限公司 Transaction data verification method and device, computer equipment and storage medium

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1257365A (en) * 1998-11-27 2000-06-21 日本电气株式会社 Method and device for database synchronisation in network management system
CN102945257A (en) * 2012-10-18 2013-02-27 北京京东世纪贸易有限公司 Method and device for initializing database
CN104636933A (en) * 2015-02-11 2015-05-20 广州唯品会信息科技有限公司 Method and device for positioning oversell reasons of e-commerce website
CN104699712A (en) * 2013-12-09 2015-06-10 阿里巴巴集团控股有限公司 Method and device for updating stock record information in database
CN105096065A (en) * 2014-04-16 2015-11-25 阿里巴巴集团控股有限公司 Inventory deduction method and inventory deduction device

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1257365A (en) * 1998-11-27 2000-06-21 日本电气株式会社 Method and device for database synchronisation in network management system
CN102945257A (en) * 2012-10-18 2013-02-27 北京京东世纪贸易有限公司 Method and device for initializing database
CN104699712A (en) * 2013-12-09 2015-06-10 阿里巴巴集团控股有限公司 Method and device for updating stock record information in database
CN105096065A (en) * 2014-04-16 2015-11-25 阿里巴巴集团控股有限公司 Inventory deduction method and inventory deduction device
CN104636933A (en) * 2015-02-11 2015-05-20 广州唯品会信息科技有限公司 Method and device for positioning oversell reasons of e-commerce website

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107633343A (en) * 2017-08-09 2018-01-26 杭州洋驼网络科技有限公司 Transaction data changes risk recognition system and method
CN111125180A (en) * 2018-10-30 2020-05-08 千寻位置网络有限公司 Inventory data statistical method and device, server and distributed purchasing system
CN109815262A (en) * 2018-12-27 2019-05-28 四川驹马科技有限公司 A kind of inventory management method and its system based on WAL mode
CN109872100A (en) * 2018-12-28 2019-06-11 航天信息股份有限公司 A kind of product oil inventory change management method and system
CN113807923A (en) * 2021-09-23 2021-12-17 杭州右文网络科技有限公司 Order processing method and device, electronic equipment and readable storage medium
CN113807923B (en) * 2021-09-23 2024-02-09 杭州右文网络科技有限公司 Order processing method and device, electronic equipment and readable storage medium
CN114548952A (en) * 2022-04-22 2022-05-27 武汉中科通达高新技术股份有限公司 Transaction data verification method and device, computer equipment and storage medium

Also Published As

Publication number Publication date
WO2017092599A1 (en) 2017-06-08

Similar Documents

Publication Publication Date Title
CN106845881A (en) A kind of detection method of stock abnormal data, device and electronic equipment
US10853812B2 (en) Blockchain transaction safety
CN110232565B (en) Resource clearing method, device, computer equipment and storage medium
CN106656536A (en) Method and device for processing service invocation information
US20180365696A1 (en) Financial fraud detection using user group behavior analysis
CN110245035A (en) A kind of link trace method and device
CN107392722A (en) Order processing method, apparatus, electronic equipment and storage medium
WO2020088098A1 (en) Block chain-based quality data processing method and apparatus
CN107688899A (en) Business process monitoring method and device
CN106716454A (en) Utilizing machine learning to identify non-technical loss
CN105243001B (en) The abnormality alarming method and device of business object
CN104346365A (en) Device and method for determining specific service associated logs
CN107451812B (en) APP payment method on mobile terminal based on block chain technology
CN109523124A (en) Asset data processing method, device, computer equipment and storage medium
CN113807960A (en) Cross-link processing method, device and system between heterogeneous chains and electronic equipment
CN106529953A (en) Method and device for carrying out risk identification on business attributes
CN106157066A (en) Mapping relations processing method, system and information processing platform equipment
CN105447707B (en) Data processing method and device
CN108023769B (en) Internet of Things group, which hinders, determines method and device
CN110471329A (en) A kind of water quality monitoring and managing method and device, equipment, medium based on block catenary system
CN109345249A (en) A kind of payment fail processing method and device
CN106056331A (en) Commodity inventory early warning method and early warning system
CN110457367B (en) Method and system for discovering data transaction
CN114549193A (en) List screening method, apparatus, device, storage medium and program product
CN111652718A (en) Method, device, equipment and medium for monitoring value flow direction based on relational network diagram

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
REG Reference to a national code

Ref country code: HK

Ref legal event code: DE

Ref document number: 1237508

Country of ref document: HK

RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20170613