TW201737164A - Method and apparatus for correcting service data prediction - Google Patents
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Abstract
Description
本發明涉及電子技術應用領域，具體而言，涉及一種業務預測資料校正的方法和裝置。 The present invention relates to the field of electronic technology applications, and in particular, to a method and apparatus for correcting traffic prediction data.
隨著電商平臺的日益發展，對電商平臺中未來營業額的預測計算，成為了當前越來越多的電商預測未來營業額的技術手段，而如何精確的預估得到電商的未來營業額成為了亟待解決的問題。 With the development of e-commerce platforms, the forecasting of future turnover in e-commerce platforms has become a technical means for more and more e-commerce to predict future turnover, and how to accurately estimate the future of e-commerce Turnover has become an urgent problem to be solved.
現有技術中由於經營個體當日業務量受活動、行銷手段以及促銷時間等諸多因素的影響，該經營個體當日的業務量將比該經營個體的歷史業務量存在明顯提升。而在該當日之前，商務系統將會對第二天的業務量進行預估，但是若預估得到的預測結果小於該當日實際營業額，則說明該預估結果存在錯誤，預測精度太低。 In the prior art, since the business volume of the individual business on the day is affected by many factors such as activities, marketing means, and promotion time, the business volume of the individual business on the day will be significantly higher than the historical business volume of the business individual. Before the date, the business system will estimate the traffic volume of the next day, but if the predicted result is less than the actual turnover of the day, it indicates that the forecast result is wrong and the prediction accuracy is too low.
現有預測經營個體的營業額的技術中，通常使用小時累計業務量預測演算法，即，根據歷史資料獲得各小時累計業務量占總業務量的比例，然後根據當日業務量的總預測值計算出每小時的累計業務量預測值。 In the existing technology for predicting the turnover of an individual operating entity, an hourly cumulative traffic forecasting algorithm is generally used, that is, a ratio of accumulated traffic volume per hour to total traffic volume is obtained based on historical data, and then calculated based on the total predicted value of the current day traffic volume. Cumulative traffic forecast per hour.
但是，根據歷史資料預測經營個體第二天的業務量(或，營業額)，以及當天每小時的累計業務量，從而根據24個小時資料點描繪經營個體業務量的變化趨勢，該方法僅能只在業務量較平穩時才能取到較好的效果，一旦第二天即時業務量發生顯著變化時，不僅會出現預測值不準確的問題，甚至會出現當天24點結算時，業務量預測總值比24點前已累計的真實業務量小的嚴重錯誤。 However, based on historical data, it is predicted that the business volume (or turnover) of the business entity on the second day, and the accumulated traffic volume per hour on the day, so as to depict the trend of the business volume of the individual business based on the 24-hour data point, the method can only Only when the business volume is relatively stable can we get better results. Once the real-time business volume changes significantly the next day, not only will the forecast value be inaccurate, but even when the 24 o'clock settlement is performed on the same day, the total traffic forecast will be A serious error that is less than the actual amount of traffic accumulated before 24 o'clock.
針對上述由於現有技術中缺少對經營個體在第二天實際業務量顯著變化的情況下校正預測業務量的技術，導致預測資料低於實際業務量，從而帶來的預測精度低的問題，目前尚未提出有效的解決方案。 In view of the above-mentioned technology for correcting the predicted traffic volume in the case where the actual individual has significantly changed the actual business volume on the second day in the prior art, the prediction data is lower than the actual traffic volume, and the problem of low prediction accuracy is not yet Propose an effective solution.
本發明實施例提供了一種業務預測資料校正的方法和裝置，以至少解決由於現有技術中缺少對經營個體在第二天實際業務量顯著變化的情況下校正預測業務量的技術，導致預測資料低於實際業務量，從而帶來的預測精度低的技術問題。 The embodiment of the invention provides a method and a device for correcting service prediction data, so as to at least solve the problem that the prediction data is low due to the lack of technology in the prior art for correcting the predicted traffic volume when the actual business volume changes significantly on the second day. The actual business volume, resulting in technical problems with low prediction accuracy.
根據本發明實施例的一個方面，提供了一種業務預測資料校正的方法，包括：在第一時刻提取第一業務資料，其中，第一業務資料包括：在第一時刻產生的業務成交量；判斷第二時刻對應的第二業務預測資料是否大於第一業務資料，其中，第二時刻為第一時刻之後相鄰的時間點；在判斷結果為否的情況下，計算第二業務預測資料與 第一業務資料的差值；依據差值校正第二時刻至結算時刻對應的業務預測資料。 According to an aspect of the present invention, a method for correcting a service prediction data includes: extracting a first service data at a first moment, wherein the first service data includes: a transaction volume generated at a first moment; Whether the second service prediction data corresponding to the second time is greater than the first service data, wherein the second time is an adjacent time point after the first time; if the determination result is no, the second service prediction data is calculated The difference between the first service data; the business prediction data corresponding to the settlement time from the second time to the settlement time.
根據本發明實施例的另一方面，還提供了一種業務預測資料校正的裝置，包括：提取模組，用於在第一時刻提取第一業務資料，其中，第一業務資料包括：在第一時刻產生的業務成交量；判斷模組，用於判斷第二時刻對應的第二業務預測資料是否大於第一業務資料，其中，第二時刻為第一時刻之後相鄰的時間點；計算模組，用於在判斷結果為否的情況下，計算第二業務預測資料與第一業務資料的差值；校正模組，用於依據差值校正第二時刻至結算時刻對應的業務預測資料。 According to another aspect of the embodiments of the present invention, an apparatus for correcting service prediction data is provided, including: an extraction module, configured to extract a first service data at a first moment, where the first service data includes: a business volume that is generated at a time; the determining module is configured to determine whether the second service prediction data corresponding to the second time is greater than the first service data, wherein the second time is an adjacent time point after the first time; the computing module And a method for calculating a difference between the second service prediction data and the first service data when the determination result is no; the correction module is configured to correct the service prediction data corresponding to the settlement time according to the difference time.
在本發明實施例中，通過在第一時刻提取第一業務資料，其中，第一業務資料包括：在第一時刻產生的業務成交量；判斷第二時刻對應的第二業務預測資料是否大於第一業務資料，其中，第二時刻為第一時刻之後相鄰的時間點；在判斷結果為否的情況下，計算第二業務預測資料與第一業務資料的差值；依據差值校正第二時刻至結算時刻對應的業務預測資料，達到了對經營個體第二天實際業務量發生變化時能夠及時校正的目的，從而實現了提升對業務量預測精度的技術效果，進而解決了由於現有技術中缺少對經營個體在第二天實際業務量顯著變化的情況下校正預測業務量的技術，導致預測資料低於實際業務量，從而帶來的預測精度低的技術問題。 In the embodiment of the present invention, the first service data is extracted by the first time, wherein the first service data includes: a transaction volume generated at the first time; and whether the second service prediction data corresponding to the second time is greater than a business data, wherein the second time is an adjacent time point after the first time; if the determination result is no, calculating a difference between the second service prediction data and the first service data; correcting the second according to the difference The business forecast data corresponding to the time of settlement to the settlement time can achieve the purpose of timely correcting the actual business volume of the second day of the operation, thereby realizing the technical effect of improving the accuracy of the traffic volume prediction, thereby solving the problem in the prior art. The lack of technology for correcting the forecasted traffic volume in the case of a significant change in the actual business volume of the business entity on the second day leads to a technical problem that the forecasting data is lower than the actual traffic volume, resulting in low prediction accuracy.
10‧‧‧電腦終端 10‧‧‧Computer terminal
50‧‧‧獲取模組 50‧‧‧Get Module
51‧‧‧資料計算模組 51‧‧‧Data Calculation Module
52‧‧‧提取模組 52‧‧‧ extraction module
54‧‧‧判斷模組 54‧‧‧Judgement module
56‧‧‧計算模組 56‧‧‧Computation Module
58‧‧‧校正模組 58‧‧‧ calibration module
102‧‧‧處理器 102‧‧‧Processor
104‧‧‧記憶體 104‧‧‧ memory
106‧‧‧傳輸裝置 106‧‧‧Transportation device
521‧‧‧判斷單元 521‧‧‧judging unit
522‧‧‧提取單元 522‧‧‧ extraction unit
581‧‧‧數值生成單元 581‧‧‧Numerical generation unit
582‧‧‧校正單元 582‧‧‧Correction unit
5821‧‧‧第一校正子單元 5821‧‧‧First Calibration Subunit
5822‧‧‧數值生成子單元 5822‧‧‧Numerical generation subunit
5823‧‧‧第二校正子單元 5823‧‧‧Second correction subunit
此處所說明的圖式用來提供對本發明的進一步理解，構成本發明的一部分，本發明的示意性實施例及其說明用於解釋本發明，並不構成對本發明的不當限定。在圖式中：圖1是本發明實施例的一種業務預測資料校正的方法的電腦終端的硬體結構方塊圖；圖2是根據本發明實施例一的業務預測資料校正的方法的流程圖；圖3a是根據本發明實施例一的一種業務預測資料校正的方法的流程示意圖；圖3b是根據本發明實施例一的另一種業務預測資料校正的方法的流程示意圖；圖4是根據本發明實施例一的一種業務預測資料校正的方法中業務資料和業務預測資料的曲線示意圖；圖5是根據本發明實施例二的業務預測資料校正的裝置的結構示意圖；圖6是根據本發明實施例二的一種業務預測資料校正的裝置的結構示意圖；圖7是根據本發明實施例二的另一種業務預測資料校正的裝置的結構示意圖；圖8是根據本發明實施例二的又一種業務預測資料校正的裝置的結構示意圖；圖9是根據本發明實施例二的再一種業務預測資料校 正的裝置的結構示意圖。 The drawings are intended to provide a further understanding of the invention and are intended to be a part of the invention. 1 is a block diagram of a hardware structure of a computer terminal for a method for correcting service prediction data according to an embodiment of the present invention; and FIG. 2 is a flowchart of a method for correcting service prediction data according to Embodiment 1 of the present invention; FIG. 3 is a schematic flowchart of a method for correcting service prediction data according to Embodiment 1 of the present invention; FIG. 3b is a schematic flowchart of another method for correcting service prediction data according to Embodiment 1 of the present invention; FIG. 4 is a schematic diagram of a method for correcting service prediction data according to Embodiment 1 of the present invention; FIG. 5 is a schematic structural diagram of an apparatus for correcting service prediction data according to Embodiment 2 of the present invention; FIG. 6 is a schematic diagram of an apparatus according to Embodiment 2 of the present invention; FIG. 7 is a schematic structural diagram of another apparatus for correcting service prediction data according to Embodiment 2 of the present invention; FIG. 8 is a schematic diagram of another service prediction data correction according to Embodiment 2 of the present invention; Schematic diagram of the device; FIG. 9 is another service forecasting data school according to the second embodiment of the present invention. A schematic diagram of the structure of a positive device.
為了使所屬技術領域中具有通常知識者更好地理解本發明方案，下面將結合本發明實施例中的圖式，對本發明實施例中的技術方案進行清楚、完整地描述，顯然，所描述的實施例僅是本發明一部分的實施例，而不是全部的實施例。基於本發明中的實施例，所屬技術領域中具有通常知識者在沒有做出創造性勞動前提下所獲得的所有其他實施例，都應當屬於本發明保護的範圍。 The technical solutions in the embodiments of the present invention are clearly and completely described in the following, in which the embodiments of the present invention are described in detail in the embodiments of the present invention. The embodiments are only a part of the embodiments of the invention, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without departing from the inventive scope are intended to be within the scope of the invention.
需要說明的是，本發明的說明書和請求項書及上述圖式中的術語“第一”、“第二”等是用於區別類似的物件，而不必用於描述特定的順序或先後次序。應該理解這樣使用的資料在適當情況下可以互換，以便這裡描述的本發明的實施例能夠以除了在這裡圖示或描述的那些以外的順序實施。此外，術語“包括”和“具有”以及他們的任何變形，意圖在於覆蓋不排他的包含，例如，包含了一系列步驟或單元的過程、方法、系統、產品或設備不必限於清楚地列出的那些步驟或單元，而是可包括沒有清楚地列出的或對於這些過程、方法、產品或設備固有的其它步驟或單元。 It should be noted that the terms "first", "second" and the like in the specification and the claims of the present invention and the above drawings are used to distinguish similar items, and are not necessarily used to describe a specific order or order. It is to be understood that the materials so used are interchangeable, where appropriate, so that the embodiments of the invention described herein can be carried out in a sequence other than those illustrated or described herein. In addition, the terms "comprises" and "comprises" and "the" and "the" are intended to cover a non-exclusive inclusion, for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to Those steps or units may include other steps or units not explicitly listed or inherent to such processes, methods, products or devices.
根據本發明實施例，還提供了一種業務預測資料校正的方法的方法實施例，需要說明的是，在圖式的流程圖示 出的步驟可以在諸如一組電腦可執行指令的電腦系統中執行，並且，雖然在流程圖中示出了邏輯順序，但是在某些情況下，可以以不同於此處的循序執行所示出或描述的步驟。 According to an embodiment of the present invention, an embodiment of a method for correcting a service prediction data is further provided. The steps may be performed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases it may be shown in a different order than here. Or the steps described.
本發明實施例一所提供的方法實施例可以在行動終端、電腦終端或者類似的運算裝置中執行。以運行在電腦終端上為例，圖1是本發明實施例的一種業務預測資料校正的方法的電腦終端的硬體結構方塊圖。如圖1所示，電腦終端10可以包括一個或多個(圖中僅示出一個)處理器102(處理器102可以包括但不限於微處理器MCU或場可程式閘陣列FPGA等的處理裝置)、用於儲存資料的記憶體104、以及用於通訊功能的傳輸裝置106。所屬技術領域中具有通常知識者可以理解，圖1所示的結構僅為示意，其並不對上述電子裝置的結構造成限定。例如，電腦終端10還可包括比圖1中所示更多或者更少的元件，或者具有與圖1所示不同的配置。 The method embodiment provided by Embodiment 1 of the present invention can be executed in a mobile terminal, a computer terminal or the like. Taking a computer terminal as an example, FIG. 1 is a hardware block diagram of a computer terminal for a method for correcting service prediction data according to an embodiment of the present invention. As shown in FIG. 1, computer terminal 10 may include one or more (only one shown) processor 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a field programmable gate array FPGA, etc. ), a memory 104 for storing data, and a transmission device 106 for communication functions. It will be understood by those of ordinary skill in the art that the structure shown in FIG. 1 is merely illustrative and does not limit the structure of the above electronic device. For example, computer terminal 10 may also include more or fewer components than those shown in FIG. 1, or have a different configuration than that shown in FIG.
記憶體104可用於儲存應用軟體的軟體程式以及模組，如本發明實施例中的業務預測資料校正的方法對應的程式指令/模組，處理器102通過運行儲存在記憶體104內的軟體程式以及模組，從而執行各種功能應用以及資料處理，即實現上述的應用程式的漏洞檢測方法。記憶體104可包括高速隨機記憶體，還可包括非易失性記憶體，如一個或者多個磁性儲存裝置、快閃記憶體、或者其他非易失性固態記憶體。在一些實例中，記憶體104可進一步 包括相對於處理器102遠端設置的記憶體，這些遠端存放器可以通過網路連接至電腦終端10。上述網路的實例包括但不限於網際網路、企業內部網、區域網路、行動通訊網及其組合。 The memory 104 can be used to store software programs and modules of the application software, such as the program instructions/modules corresponding to the method for correcting the business prediction data in the embodiment of the present invention, and the processor 102 runs the software program stored in the memory 104. And modules to perform various functional applications and data processing, that is, to implement the vulnerability detection method of the above application. Memory 104 may include high speed random memory and may also include non-volatile memory such as one or more magnetic storage devices, flash memory, or other non-volatile solid state memory. In some examples, memory 104 can be further Including memory disposed remotely from the processor 102, the remote registers can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, intranets, regional networks, mobile communication networks, and combinations thereof.
傳輸裝置106用於經由一個網路接收或者發送資料。上述的網路具體實例可包括電腦終端10的通訊供應商提供的無線網路。在一個實例中，傳輸裝置106包括一個網路介面卡(Network Interface Controller，NIC)，其可通過基站與其他網路設備相連從而可與網際網路進行通訊。在一個實例中，傳輸裝置106可以為射頻(Radio Frequency，RF)模組，其用於通過無線方式與網際網路進行通訊。 Transmission device 106 is for receiving or transmitting data via a network. The above specific network example may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network interface controller (NIC) that can be connected to other network devices through a base station to communicate with the Internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module for communicating wirelessly with the Internet.
在上述運行環境下，本發明提供了如圖2所示的業務預測資料校正的方法。圖2是根據本發明實施例一的業務預測資料校正的方法的流程圖。 In the above operating environment, the present invention provides a method of correcting traffic prediction data as shown in FIG. 2. 2 is a flow chart of a method for correcting traffic prediction data according to Embodiment 1 of the present invention.
步驟S202，在第一時刻提取第一業務資料，其中，第一業務資料包括：在第一時刻產生的業務成交量；本發明上述步驟S202中，本發明實施例提供的業務預測資料校正的方法可以適用於電商平臺中對經營個體當天業務量的預測資料校正，避免經營個體當天業務量總預測資料小於當日某時刻業務資料真實值，在本發明實施例提供的業務預測資料校正的方法中，若要判定經營個體的總預測資料是否小於實際的業務成交量，則需要首先在第一時刻提取第一業務資料，該第一業務資料可以為0點至 第一時刻的業務成交量的累計值。 In step S202, the first service data is extracted at the first time, where the first service data includes: the volume of the service generated at the first time; the method for correcting the service prediction data provided by the embodiment of the present invention in the above step S202 of the present invention It can be applied to the forecasting data correction of the business volume of the business entity in the e-commerce platform, and avoids the fact that the total forecasting data of the business entity on the same day is smaller than the real value of the business data at a certain time on the current day, in the method for correcting the business forecasting data provided by the embodiment of the present invention. If it is determined whether the total forecast data of the operating entity is smaller than the actual business volume, the first business data needs to be first extracted at the first moment, and the first business data may be from 0 to The cumulative value of the business volume at the first moment.
其中，本發明實施例中在第一時刻可以精確至秒，即，提取第一業務資料時，該第一時刻的結構可以表示為：年-月-日 小時：分鐘：秒(yyyy-mm-dd hh：mm：ss)的時刻點。在本發明實施例中由於第一時刻可以精確到秒，而由該第一時刻提取的第一業務資料將為精確至秒的業務成交量，綜上依據精確至秒的業務成交量在對後續預測資料校正時，將提高預測值的校正精度，其中，校正過程見步驟S204至S208。 In the embodiment of the present invention, the first moment can be accurate to the second, that is, when the first service data is extracted, the structure of the first moment can be expressed as: year-month-day hour: minute: second (yyyy-mm- Dd hh:mm:ss). In the embodiment of the present invention, since the first moment can be accurate to the second, the first service data extracted by the first moment will be the traffic volume accurate to the second, and the transaction volume based on the second to the second is When the prediction data is corrected, the correction accuracy of the predicted value is improved, and the correction process is as shown in steps S204 to S208.
步驟S204，判斷第二時刻對應的第二業務預測資料是否大於第一業務資料，其中，第二時刻為第一時刻之後相鄰的時間點；基於步驟S202中提取的第一業務資料，本發明上述步驟S204中，將第二時刻對應的第二業務預測資料與該第一業務資料進行比較，判斷該第二業務預測資料是否大於第一業務資料，其中，第二時刻為第一時刻之後相鄰的時間點。 In step S204, it is determined whether the second service prediction data corresponding to the second time is greater than the first service data, wherein the second time is an adjacent time point after the first time; and the present invention is based on the first service data extracted in step S202. In the above step S204, the second service prediction data corresponding to the second time is compared with the first service data, and it is determined whether the second service prediction data is greater than the first service data, where the second time is after the first time The point in time of the neighbor.
具體的，假設第一時刻提取的第一業務資料為：09：15：20的業務資料Date1，第二時刻對應的第二業務預測資料可以為：10：00對應的業務預測資料Date2，在得到Date1後將Date2與Date1進行判斷，判斷該Date2是否大於Date1。 Specifically, it is assumed that the first service data extracted at the first moment is: the service data Date1 of 09:15:20, and the second service prediction data corresponding to the second time may be: the service prediction data Date2 corresponding to 10:00, After Date1, Date2 and Date1 are judged to determine whether the Date2 is greater than Date1.
步驟S206，在判斷結果為否的情況下，計算第二業務預測資料與第一業務資料的差值； 本發明上述步驟S206中，在判斷結果為第二預測資料小於第一業務資料的情況下，可以得到計算第二業務預測資料與第一業務資料的差值。 Step S206, if the determination result is negative, calculating a difference between the second service prediction data and the first service data; In the above step S206 of the present invention, if the result of the determination is that the second prediction data is smaller than the first service data, the difference between the second service prediction data and the first service data may be obtained.
具體的，仍舊以步驟S204中的示例為例，Date1大於Date2時，計算Date2與Date1之間的差值，即，差值D=Date1-Date2。 Specifically, the example in step S204 is still taken as an example. When Date1 is greater than Date2, the difference between Date2 and Date1 is calculated, that is, the difference D=Date1-Date2.
步驟S208，依據差值校正第二時刻至結算時刻對應的業務預測資料。 Step S208, correcting the business prediction data corresponding to the second time to the settlement time according to the difference.
基於步驟S206得到的差值，本發明上述步驟S208中，依據該差值，對第二時刻至結算時刻對應的業務預測資料進行校正，本發明實施例中將一天的業務資料和業務預測資料依據每天24個小時劃分為24個階段，即，0點至24點，共24個時段，在得到該差值時，將對第二時刻至24點對應的業務預測資料進行校正。 Based on the difference obtained in step S206, in the foregoing step S208 of the present invention, the service prediction data corresponding to the second time to the settlement time is corrected according to the difference, and the business data and the business prediction data of the day are determined according to the embodiment of the present invention. 24 hours a day is divided into 24 phases, that is, 0 to 24 points, a total of 24 time periods, when the difference is obtained, the business forecast data corresponding to the second time to 24 points will be corrected.
具體的，仍舊以步驟S204中的示例為例，當得到Date1與Date2的差值為D時，若第二時刻為10點，則依據該差值D校正10點至24點這15個時間點的業務預測資料。 Specifically, the example in step S204 is taken as an example. When the difference between Date1 and Date2 is D, if the second time is 10 points, the 15 time points from 10 to 24 are corrected according to the difference D. Business forecast data.
結合步驟S202至步驟S208，在本發明實施例中，業務量的預測值(即，本發明實施例提供的第二業務預測資料)是在預測業務日期之前通過預測演算法得到的當天業務資料的預估，即時業務資料是當天即時獲取的實際業務量值(即，本發明實施例提供的第一業務資料)。為了實現平滑補差，先根據歷史資料獲取各小時累計業務量占業 務總量的比值，然後將其乘以當日業務量預測值，得到當天25個小時點的預測值，據此繪製一條以小時為橫軸，業務量為縱軸的預測曲線。當即時資料比下一小時資料大時(即，第一業務資料大於第二業務預測資料的情況下)，計算該差值(計算第一業務資料與第二業務預測資料的差值)，並將當前時刻後面各小時點的預測值都加上該差值即完成補差。圖3a是根據本發明實施例一的另一種業務預測資料校正的方法的流程示意圖，如圖3a所示，在本發明實施例提供的業務預測資料校正的方法中，通過依據離線預測總業務量與離線預測小時銷量的占比，得到離線資料(即，本發明實施例中的業務預測資料)，通過在第一時刻更新得到的實際業務資料，若該業務資料大於與第一時刻相鄰的第二時刻對應的業務預測資料，則通過比較業務資料與該業務預測資料，生成校正值(即，本發明實施例中的差值)，進而通過校正值校正第二時刻至結算時刻對應的業務預測資料。 In conjunction with the step S202 to the step S208, in the embodiment of the present invention, the predicted value of the traffic (that is, the second service prediction data provided by the embodiment of the present invention) is the same day service data obtained by the prediction algorithm before the predicted service date. It is estimated that the real-time business data is the actual business volume value that is acquired on the same day (that is, the first service data provided by the embodiment of the present invention). In order to achieve smooth replenishment, we first obtain accumulated business volume per hour based on historical data. The ratio of the total amount of traffic, and then multiply it by the predicted value of the traffic volume of the day, and obtain the predicted value of the 25-hour point of the day, and draw a prediction curve with the hour as the horizontal axis and the traffic volume as the vertical axis. When the real-time data is larger than the next-hour data (that is, the first service data is larger than the second service forecast data), the difference is calculated (calculating the difference between the first service data and the second service forecast data), and The difference is added to the predicted value of each hour after the current time to complete the replenishment. FIG. 3 is a schematic flowchart of another method for correcting service prediction data according to the first embodiment of the present invention. As shown in FIG. 3a, in the method for correcting service prediction data provided by the embodiment of the present invention, the total traffic is predicted by offline. Obtaining offline data (that is, the service prediction data in the embodiment of the present invention), and obtaining the actual service data obtained by updating at the first time, if the service data is greater than the first time The service prediction data corresponding to the second time is obtained by comparing the service data with the service prediction data to generate a correction value (that is, the difference value in the embodiment of the present invention), and then correcting the service corresponding to the settlement time by the correction value. Forecast data.
由上可知，本發明上述實施例一所提供的方案，通過在第一時刻提取第一業務資料，其中，第一業務資料包括：在第一時刻產生的業務成交量；判斷第二時刻對應的第二業務預測資料是否大於第一業務資料；在判斷結果為否的情況下，計算第二業務預測資料與第一業務資料的差值；依據差值校正第二時刻至結算時刻對應的業務預測資料，達到了對經營個體第二天實際業務量發生變化時能夠及時校正的目的，從而實現了提升對業務量預測精度的技 術效果，進而解決了由於現有技術中缺少對經營個體在第二天實際業務量顯著變化的情況下校正預測業務量的技術，導致預測資料低於實際業務量，從而帶來的預測精度低的技術問題。 It can be seen that, in the solution provided by the foregoing first embodiment, the first service data is extracted at the first moment, where the first service data includes: the volume of the service generated at the first moment; and the second moment is determined. Whether the second service prediction data is larger than the first service data; if the determination result is negative, calculating the difference between the second service prediction data and the first service data; correcting the service prediction corresponding to the settlement time according to the difference time The data has reached the goal of being able to correct in time when the actual business volume of the individual is changed on the second day, thereby realizing the technique of improving the accuracy of the business volume prediction. The technical effect further solves the problem that the prior art lacks the technique for correcting the predicted traffic volume in the case where the actual business volume changes significantly on the second day in the prior art, and the prediction data is lower than the actual traffic volume, thereby resulting in low prediction accuracy. technical problem.
具體的，圖3b是根據本發明實施例一的另一種業務預測資料校正的方法的流程示意圖，如圖3b所示，本發明實施例提供的業務預測資料校正的方法具體如下：可選的，步驟S202中在第一時刻提取第一業務資料包括： Specifically, FIG. 3b is a schematic flowchart of another method for correcting service prediction data according to the embodiment of the present invention. As shown in FIG. 3b, the method for correcting service prediction data provided by the embodiment of the present invention is specifically as follows: The extracting the first service data at the first time in step S202 includes:
Step1，判斷第一時刻是否大於結算時刻；本發明上述步驟S202中的Step1中，在提取第一業務資料之前，需要判定當前的提取時間是否為結算時刻，即，若第一時刻為結算時刻，表明當天已經結束，已經沒有必要對當前時刻對應的業務資料和業務預測資料進行比較，校驗過程結束，所以在提取第一業務資料前，需對提取第一業務資料的時刻是否為結算時刻進行判斷。 Step 1 , determining whether the first time is greater than the settlement time; in Step 1 of the above step S202 of the present invention, before extracting the first service data, it is required to determine whether the current extraction time is a settlement time, that is, if the first time is a settlement time, It indicates that the day has ended, it is no longer necessary to compare the business data and business forecast data corresponding to the current time, and the verification process is finished. Therefore, before extracting the first service data, it is necessary to check whether the time at which the first service data is extracted is the settlement time. Judge.
Step2，在判斷結果為否的情況下，提取第一業務資料。 Step 2: In the case that the determination result is no, the first service data is extracted.
基於Step1的判斷，本發明上述步驟Step2中，在判斷結果為該第一時刻小於結算時刻的情況下，提取第一業務資料。 Based on the judgment of Step 1, in the above step Step 2 of the present invention, when the result of the determination is that the first time is less than the settlement time, the first service data is extracted.
具體的，結合Step1和Step2，本發明實施例提供的業務預測資料校正的方法在提取第一業務資料的過程中具體如下：判斷當前第一時刻是否大於結算時刻，在判斷結 果為是的情況下，第一業務資料提取流程結束；在判斷結果為否的情況下，提取第一時刻對應的第一業務資料，其中，第一業務資料可以為在第一時刻精確至秒的情況下，提取的業務成交量，該業務成交量可以為0點至第一時刻的累積成交量。 Specifically, in combination with Step 1 and Step 2, the method for correcting the service prediction data provided by the embodiment of the present invention is specifically as follows: determining whether the current first moment is greater than the settlement time, and determining the knot. If yes, the first service data extraction process ends; if the determination result is negative, the first service data corresponding to the first time is extracted, wherein the first service data may be accurate to the second time at the first time. In the case of the extracted business volume, the volume of the business may be from 0 to the accumulated volume at the first moment.
可選的，在步驟S202中的在第一時刻提取第一業務資料之前，本發明實施例提供的業務預測資料校正的方法還包括： Optionally, before the first service data is extracted at the first time in step S202, the method for correcting the service prediction data provided by the embodiment of the present invention further includes:
步驟S200，獲取業務預測資料和預設時間的累積業務量占業務總量的比重；本發明上述步驟S200中，本發明實施例中的業務預測資料可以以日業務預測資料為例，預設時間的累積業務量可以以每小時累積業務量為例進行說明，其中，本發明實施例中在實際業務成交之前的一天將預先得到該日一天每個小時的業務預測資料，其中，在獲取每個小時的業務預測資料的過程中，首先，需要獲取日業務預測資料、至少7天的歷史業務資料和每小時占當天總業務資料的比值；其次，依據至少7天的歷史業務資料和每小時占當天總業務資料的比值得到每小時累積業務量占業務總量的比重，其中，本發明實施例中的比重取的是最近7天比重的平均值。即，先算每個小時占當天總業務資料的比值，再取每小時最近7天比值的平均。這樣避免某天突高或突低的異常。 In step S200, the service prediction data and the cumulative traffic volume of the preset time account are used as the proportion of the total traffic volume. In the foregoing step S200, the service prediction data in the embodiment of the present invention may take the daily service prediction data as an example, and preset time. The cumulative traffic volume can be described by taking the accumulated traffic volume per hour as an example. In the embodiment of the present invention, the business forecast data of each hour of the day will be obtained in advance one day before the actual business transaction, wherein each of the acquisitions is obtained. In the process of hourly business forecasting, first, you need to obtain daily business forecast data, at least 7 days of historical business data, and the ratio of hourly total business data; secondly, based on at least 7 days of historical business data and hourly share. The ratio of the total business data of the day is the proportion of the accumulated traffic per hour to the total volume of the business. The weight in the embodiment of the present invention is the average of the proportions of the last 7 days. That is, first calculate the ratio of each hour to the total business data of the day, and then take the average of the ratio of the last 7 days of each hour. This avoids abnormalities that are sudden or low on a certain day.
具體的，本發明實施例中的日業務預測資料可以通過 在一個時間區間內採樣計算得到的日均業務資料，即，通過採集一週(7天)的每日業務成交資料，通過求和的方式得到一週的業務成交總數據，進而依據該業務成交總數據通過除以7個成交日得到日均業務資料，並將該日均業務資料作為日業務預測資料。 Specifically, the daily service prediction data in the embodiment of the present invention may pass The daily average business data is sampled and calculated in a time interval, that is, by collecting the daily business transaction data of one week (7 days), the total transaction data of one week is obtained by the summation method, and then the total transaction data according to the business is obtained. The daily average business data is obtained by dividing by 7 transaction days, and the daily business data is used as the daily business forecast data.
假設，需要獲取Day1(2月1日)的日業務預測資料，則需要獲取Day1之前1月26至1月31日7天的成交資料，該經營個體在1月26至1月31日的每日業務成交資料可以為Date1，Date2，Date3，Date4，Date5，Date6和Date7，通過對7天每日業務成交資料的求和，可以得到一週的業務成交總數據，一週的業務成交總資料表示如下：DateZ1~7=Date1+Date2+Date3+Date4+Date5+Date6+Date7；進而為得到預測Day1的日業務預測資料，則一週的業務成交總數據通過除以7個成交日得到日均業務資料，即，DateDay1=DateZ1~7/7，在得到日均業務資料後可以將該DateDay1作為Day1的日業務預測資料；或者，通過預測演算法模型得到Day1的日業務預測資料。 Assume that you need to obtain Day1 (February 1st) daily business forecast data, you need to obtain the transaction data from January 26 to January 31, 7 days before Day1, the business entity from January 26 to January 31 The daily business transaction data can be Date1, Date2, Date3, Date4, Date5, Date6 and Date7. Through the summation of the daily business transaction data for 7 days, the total transaction data of the week can be obtained. The total transaction data of the week is expressed as follows. :DateZ 1~7 =Date1+Date2+Date3+Date4+Date5+Date6+Date7; In order to get the forecasted daily business forecast data, the total business transaction data for one week is divided into 7 transaction days to get the daily business data. That is, Date Day1 = DateZ 1~7 /7. After obtaining the daily average business data, the Date Day1 can be used as the daily business forecast data of Day1; or, the daily business forecast data of Day1 can be obtained by the prediction algorithm model.
若獲取每小時累積業務量占業務總量的比重，則需要先算每個小時占當天總業務資料的比值，再取每小時最近7天比值的平均，最後得到每小時累積業務量占業務總量的比重，例如，先計算7天內每小時占當天總業務資料的比值，可以得到： Day1：P10~P124；Day2：P20~P224；…… If you get the proportion of accumulated business volume per hour to the total business volume, you need to calculate the ratio of the total business data per hour to the total business data of the day, and then take the average of the ratio of the last 7 days of the hour, and finally get the accumulated business volume per hour. The proportion of the quantity, for example, first calculate the ratio of hourly total business data for the day within 7 days, you can get: Day1: P 1 0~P 1 24; Day2: P 2 0~P 2 24;......
Day7：P70~P724；其中，Day1至Day7的每小時占當天總業務資料的比值為每小時累積業務量占當天總業務資料的占比。 Day7: P 7 0~P 7 24; Among them, the ratio of the daily business data of Day1 to Day7 to the total business data of the day is the proportion of accumulated business volume per hour to the total business data of the day.
進而，再取每小時最近7天比值的平均，可以得到： P0’=(P10+P20+……+P70)/7；P1’=(P11+P21+……+P71)/7；…… Furthermore, taking the average of the ratio of the last 7 days of each hour, we can get: P 0 '=(P 1 0+P 2 0+...+P 7 0)/7; P 1 '=(P 1 1+P 2 1+...+P 7 1)/7;......
P23’=(P124+P224+……+P724)/7。 P 23 '=(P 1 24+P 2 24+...+P 7 24)/7.
由上可知，得到每小時累積業務量占業務總量的比重P0’，P1’，……，P24’。 It can be seen from the above that the cumulative traffic volume per hour accounts for the proportion of the total traffic P 0 ', P 1 ', ..., P 24 '.
這裡需要說明的是本發明實施例提供的獲取日業務預測資料和每小時累積業務量占業務總量的比重的方法僅以上述為例，以實現本發明實施例提供的業務預測資料校正的方法為準，具體不做限定。 It is to be noted that the method for obtaining the daily service prediction data and the proportion of the accumulated traffic per hour to the total amount of the service provided by the embodiment of the present invention is only exemplified by the above, and the method for correcting the service prediction data provided by the embodiment of the present invention is implemented. The standard is not limited.
步驟S201，將業務預測資料和預設時間的累積業務量占業務總量的比重進行計算，得到對應各個時間點的業務預測資料。 Step S201: Calculate the proportion of the business forecast data and the accumulated traffic volume of the preset time to the total amount of the service, and obtain the service prediction data corresponding to each time point.
基於步驟S200獲取的日業務預測資料和每小時累積業務量占業務總量的比重，本發明上述步驟S201中，將獲取到的日業務預測資料和每小時累積業務量占業務總量的比重進行計算，將得到每小時業務預測資料。具體如 下： Based on the daily service forecast data acquired in step S200 and the proportion of accumulated traffic per hour to the total amount of services, in the above step S201 of the present invention, the acquired daily service forecast data and the accumulated traffic volume per hour account for the proportion of the total traffic volume. Calculate, you will get hourly business forecast data. Specific as under:
Step1，計算日業務預測資料和比重的乘積，將乘積確定為每小時業務預測資料。 Step1: Calculate the product of the daily business forecast data and the specific gravity, and determine the product as the hourly business forecast data.
由上述步驟S200和S201可知，通過計算日業務預測資料和比重的乘積，可以將得到乘積確定為每小時業務預測資料。具體如下，仍舊以步驟S200中的示例為例，假設日業務預測資料為DateDay1，每小時累積業務量占業務總量的比重為P0’，P1’，……，P24’：將DateDay1分別與P0’，P1’，……，P24’進行乘積，可以得到Day1的每小時業務預測資料D’0~D’24。 As can be seen from the above steps S200 and S201, by calculating the product of the daily traffic prediction data and the specific gravity, the obtained product can be determined as the hourly traffic prediction data. Specifically, as follows, the example in step S200 is taken as an example. Assume that the daily business forecast data is Date Day1 , and the cumulative traffic volume per hour accounts for P 0 ', P 1 ', ..., P 24 ': Date Day1 is multiplied with P 0 ', P 1 ', ..., P 24 ', respectively, and can get Day1's hourly business forecast data D'0~D'24.
此外，預設時間的累積業務量占業務總量的比重還可以為以秒為單位的每個小時中秒級的每小時累積業務量占業務總量的比重，本發明實施例中的預設時間的累積業務量以每小時的累積業務量為例，預設時間的累積業務量占業務總量的比重以每小時的累積業務量占業務總量的比重為例進行說明，以實現本發明實施例提供的業務預測資料校正的方法為準，具體不做限定。 In addition, the proportion of the accumulated traffic in the preset time to the total amount of the service may also be the proportion of the accumulated traffic per hour in the second-hour per second in the total amount of the service, which is preset in the embodiment of the present invention. The accumulated traffic of time is exemplified by the accumulated traffic volume per hour. The cumulative traffic volume of the preset time accounts for the proportion of the total traffic volume, and the proportion of the accumulated traffic volume per hour to the total traffic volume is taken as an example to implement the present invention. The method for correcting the service prediction data provided by the embodiment is applicable, and is not limited.
綜上，結合步驟S200和步驟S201中計算每小時業務預測資料的方法，本發明實施例提供的業務預測資料校正的方法中除了步驟S200和步驟S201中通過求平均值得到每小時業務預測資料，還可以通過店鋪的訪問率、購物車的添加率、商品的收藏率以及交易資料作為訓練特徵生成業務預測資料計算模型，進而得到業務預測資料，本發明 實施例提供的業務預測資料的方法以平均值演算法為例進行說明，以實現本發明實施例提供的業務預測資料的方法為準，具體不做限定。 In summary, in combination with the method for calculating the hourly traffic prediction data in the step S200 and the step S201, the method for correcting the service prediction data provided by the embodiment of the present invention obtains the hourly service prediction data by averaging in steps S200 and S201. It is also possible to generate a business prediction data calculation model by using the access rate of the store, the addition rate of the shopping cart, the collection rate of the product, and the transaction data as the training features, thereby obtaining the business prediction data, and the present invention. The method for predicting the service data provided by the embodiment is described by taking the average value algorithm as an example, and the method for implementing the service prediction data provided by the embodiment of the present invention is used as a standard, and is not limited thereto.
可選的，步驟S208中依據差值校正第二時刻至結算時刻對應的業務預測資料包括： Optionally, the service prediction data corresponding to the second time to the settlement time according to the difference correction in step S208 includes:
Step1，依據差值生成校正值；本發明上述步驟S208中的Step1中，在步驟S206中第二業務預測資料小於第一業務資料的情況下，依據第一業務資料和第二業務預測資料的差值生成校正值。 Step 1 , generating a correction value according to the difference; in Step 1 in step S208 of the present invention, in the case where the second service prediction data is smaller than the first service data in step S206, the difference between the first service data and the second service prediction data is The value generates a correction value.
Step2，依據校正值對第二時刻至結算時刻對應的業務預測資料進行調整，得到校正後的業務預測資料。 Step 2: Adjust the service prediction data corresponding to the second time to the settlement time according to the correction value, and obtain the corrected business prediction data.
基於步驟Step1生成的校正值，本發明上述Step2中，依據校正值對第二時刻至結算時刻對應的業務預測資料進行調整，得到校正後的業務預測資料。 Based on the correction value generated in step Step 1, in the above Step 2 of the present invention, the business prediction data corresponding to the second time to the settlement time is adjusted according to the correction value, and the corrected business prediction data is obtained.
具體的，本發明實施例中依據校正值對第二時刻至結算時刻對應的業務預測資料進行調整可以包括兩種方式，其中，方式一，為將差值確定為校正值，並將差值加至第二時刻至結算時刻對應的業務預測資料，得到校正後的業務預測資料；方式二，依據差值生成校正比重，並依據校正比重對第二時刻至結算時刻對應的業務預測資料進行調整，得到校正後的業務預測資料。這裡方式一執行步驟A，方式二執行步驟B和步驟C。 Specifically, in the embodiment of the present invention, adjusting the service prediction data corresponding to the second time to the settlement time according to the correction value may include two modes, where manner 1 is to determine the difference as the correction value, and add the difference The business forecast data corresponding to the second time to the settlement time is obtained, and the corrected business forecast data is obtained; in the second method, the correction weight is generated according to the difference, and the business forecast data corresponding to the second time to the settlement time is adjusted according to the corrected specific gravity, Obtained corrected business forecast data. Here, mode one performs step A, and mode two performs step B and step C.
進一步地，可選的，步驟S208中的Step2中依據校正值對第二時刻至結算時刻對應的業務預測資料進行調 整，得到校正後的業務預測資料包括： 本發明實施例中，依據校正值對第二時刻至結算時刻對應的業務預測資料進行調整，得到校正後的業務預測資料包括以下兩種方式： Further, optionally, Step 2 in step S208 adjusts the service prediction data corresponding to the second time to the settlement time according to the correction value. The corrected business forecast data includes: In the embodiment of the present invention, the service prediction data corresponding to the second time to the settlement time is adjusted according to the correction value, and the corrected service prediction data includes the following two methods:
方式一，校正值為差值的情況； In the first mode, the correction value is the difference value;
步驟A，在將差值確定為校正值的情況下，將差值加至第二時刻至結算時刻對應的業務預測資料，得到校正後的業務預測資料；本發明上述步驟A中，將第一業務資料與第二業務預測資料求差得到的差值與第二時刻至結算時刻對應的每小時業務預測資料求和，得到校正後的每小時業務預測資料。 Step A: In the case where the difference is determined as the correction value, the difference is added to the service prediction data corresponding to the second time to the settlement time to obtain the corrected service prediction data; in the above step A of the present invention, the first The difference between the service data and the second service prediction data is compared with the hourly service prediction data corresponding to the second time to the settlement time, and the corrected hourly service prediction data is obtained.
具體的，假設第一業務資料與第二業務預測資料求差得到的差值為△d，第二時刻至結算時刻的每小時業務預測資料可以為D’i~D’24，將D’i~D’24分別與△d求和，得到校正後的每小時業務預測資料D’i+△d~D’24+△d。 Specifically, it is assumed that the difference between the first service data and the second service prediction data is Δd, and the hourly service prediction data from the second time to the settlement time may be D'i~D'24, and D'i ~D'24 is summed with Δd, respectively, and the corrected hourly traffic prediction data D'i+Δd~D'24+Δd is obtained.
或者， or,
方式二，校正值為依據差值生成的校正比重的情況； In the second mode, the correction value is a case of the correction weight generated based on the difference;
步驟B，依據差值生成校正比重，並將校正比重確定為校正值；本發明上述步驟B中，假設該差值為△d，依據該△d生成校正比重Bi。 In step B, the corrected specific gravity is generated according to the difference, and the corrected specific gravity is determined as the correction value. In the above step B of the present invention, the difference is assumed to be Δd, and the corrected specific gravity Bi is generated according to the Δd.
步驟C，對第二時刻至結算時刻對應的業務預測資料依據校正比重進行調整，得到校正後的業務預測資料。 In step C, the business forecast data corresponding to the second time to the settlement time is adjusted according to the corrected specific gravity, and the corrected business forecast data is obtained.
基於步驟B中得到的校正比重，本發明上述步驟C中，在得到校正比重Bi後，依據該校正比重Bi對第二時刻至結算時刻對應的業務預測資料進行調整，得到校正後的業務預測資料。 Based on the corrected specific gravity obtained in step B, in the above step C of the present invention, after the corrected specific gravity Bi is obtained, the business forecast data corresponding to the second time to the settlement time is adjusted according to the corrected specific gravity Bi, and the corrected business forecast data is obtained. .
具體的，假設校正比重Bi，第二時刻至結算時刻的每小時業務預測資料可以為D’i~D’24，將D’i~D’24分別與Bi乘積，得到校正後的每小時業務預測資料D’i*Bi~D’24*Bi。 Specifically, it is assumed that the corrected specific gravity Bi, the hourly business forecast data from the second time to the settlement time may be D'i~D'24, and D'i~D'24 are respectively multiplied with Bi to obtain the corrected hourly service. Forecast data D'i*Bi~D'24*Bi.
結合上述步驟S202至步驟S208，如圖3b所示，假設離線預測當天總業務量為X(即，本發明實施例中的日業務預測資料)，離線預測每小時業務量占總業務量占比為p n ，n=0,1,2,...,23(即，本發明實施例中的每小時累積業務量占業務總量的比重)，則24個小時業務量預測值為X* p n (n=0,1,2,...,23)(即，本發明實施例中的每小時業務預測資料)。即時資料是在業務量當天即時追蹤到的實際已產生業務量，記為Ti(即，本發明實施例中的第一業務資料)，i精確到秒，即表示yyyy-mm-dd hh：mm：ss的時刻點。補差技術指，當前時刻i(小時為h)獲取的即時資料Ti與(h+1)小時預測值(第二時刻對應的第二業務預測資料)做對比，即△t=T i -X*P h+1(即，本發明實施例中的第二業務預測資料與第一業務資料的差值)，當△t>0時將(h+1)小時到23點的每小時預測值加上△t(即，本發明實施例中的將差值加至第二時刻至結算時刻對應的每小時業務預測資料，得到校正後的每小時業務預 測資料)。這裡需要說明的是，如圖3b所示，當實施資料比小時預測值小時(即，第一業務資料小於第二業務預測資料的情況下)，則重新提取實際產生的業務資料，即，第一時刻之後，結算時刻之前，提取業務資料，直至提取的業務資料大於該業務資料對應的提取時刻之後相鄰位置時刻的業務預測資料；如圖3b所示，在一次校正流程結束後，將進入第二次校正準備流程，即，在存在T1時刻的業務資料大於T2時刻的業務預測資料的情況下，將執行校正流程。 In combination with the foregoing steps S202 to S208, as shown in FIG. 3b, it is assumed that the total traffic volume of the offline prediction day is X (ie, the daily service prediction data in the embodiment of the present invention), and the offline traffic volume per hour accounts for the total traffic volume ratio. For p n , n = 0, 1, 2, ..., 23 (i.e., the cumulative traffic per hour in the embodiment of the present invention accounts for the proportion of the total traffic), the 24-hour traffic forecast is X * p n (n = 0, 1, 2, ..., 23) (i.e., hourly traffic prediction data in the embodiment of the present invention). The instant data is the actual generated traffic that is tracked on the same day of the traffic, and is recorded as T i (ie, the first service data in the embodiment of the present invention), i is accurate to the second, that is, yyyy-mm-dd hh: Mm: ss moment. The replenishment technique refers to comparing the real-time data T i obtained at the current time i (hours h) with the (h+1) hour prediction value (the second service prediction data corresponding to the second time), that is, Δ t = T i - X * P h +1 (ie, the difference between the second service prediction data and the first service data in the embodiment of the present invention), and the hourly predicted value of (h+1) hours to 23 points when Δ t >0 plus △ t (i.e., the difference in the embodiment was added to a second embodiment of the present invention to the service time per hour time point corresponding to the predicted billing information, obtained per hour traffic prediction information after correction). It should be noted that, as shown in FIG. 3b, when the implementation data is smaller than the hourly prediction value (that is, the first service data is smaller than the second service prediction data), the actual generated business data is re-extracted, that is, the first After a moment, before the settlement time, the business data is extracted until the extracted business data is larger than the business prediction data of the adjacent location after the extraction time corresponding to the service data; as shown in FIG. 3b, after the calibration process ends, the data will enter. The second correction preparation process, that is, in the case where the business data having the time T1 is greater than the business prediction data at the time T2, the correction process will be executed.
圖4是根據本發明實施例一的一種業務預測資料校正的方法中業務資料和業務預測資料的曲線示意圖；先根據歷史資料獲取各小時累計業務量占業務總量的比值，然後將其乘以當日業務量預測值，得到當天24個小時點的預測值，據此繪製一條以小時為橫軸，業務量為縱軸的預測曲線。當即時資料比下一小時資料大時，計算該差值，並將當前時刻後面各小時點的預測值都加上該差值即完成補差。如圖4所示，10點之後的虛線部分都是預測值，23點的值即為業務量總預測值X，09：15：20即時獲得的資料為T9，將T9與其後一個小時的整點10點的預測資料比較。上圖T9值未比10點預測值大不做修正，但假若09：15：20即時拿到的資料如畫的粗線條，明顯發現其值比10點的預測值大，此時的曲線出現了陡峰。這種預測是完全錯誤的，因為曲線表現的是小時的累計資料，T10之後的值肯定比T9大，至少是持平，通過補差技術可以達 到帶點虛線的效果將所有後麵點的預測值做個平滑提升，及時有效解決了因當天經營個體效果突變而造成的預測不準確甚至是預測離譜問題。 4 is a schematic diagram of a service data and a service prediction data in a method for correcting service prediction data according to a first embodiment of the present invention; first, according to historical data, a ratio of accumulated traffic per hour to a total amount of services is obtained, and then multiplied by The forecast value of the current day's traffic volume is the predicted value of the 24-hour point of the day, and a prediction curve with the hour as the horizontal axis and the traffic volume as the vertical axis is drawn accordingly. When the real-time data is larger than the next hour data, the difference is calculated, and the predicted value of each hour after the current time is added to the difference to complete the replenishment. As shown in Fig. 4, the dotted line after 10 o'clock is the predicted value, the value of 23 o'clock is the total predicted value of the traffic X, and the data obtained immediately by 09:15:20 is T9, and the whole of T9 is followed by one hour. Compare the forecast data at 10 points. The T9 value in the above figure is not larger than the 10 point prediction value, but if the data obtained immediately at 09:15:20 is as thick as the picture, it is obvious that the value is larger than the predicted value of 10 points. The curve appears at this time. Steep peak. This kind of prediction is completely wrong, because the curve shows the accumulated data of the hour. The value after T10 is definitely larger than T9, at least it is flat, and it can be achieved by the technique of replenishment. The effect of dotted lines is used to smoothly improve the predicted values of all subsequent points, and effectively solve the problem of inaccurate prediction or even outrageous prediction caused by sudden changes in the individual operation of the day.
基於上述步驟S202至步驟S208，本發明實施例提供的業務預測資料校正的方法除了可以適用於“雙十一”或“雙十二”這類單天促銷情況，還可以適用於活動時間持續超過兩天的促銷活動，例如，連續三天的聚划算活動、黃金週；又或者一個小時或預設時間內的促銷活動，基於上述環境，本發明實施例中業務預測資料校正的方法對業務資料的校正原理，即，無論任一促銷活動，在該活動開始的前一天或前一時間段內都會生成該促銷活動時期內的業務預測資料，當在促銷活動進行時，若實際的業務資料大於該時刻相鄰時間點對應的業務預測資料，則通過比較該業務預測資料和該時刻的業務資料，生成校正值，並依據該校正值校正該時刻相鄰時間點至結算時刻的業務預測資料。 Based on the foregoing steps S202 to S208, the method for correcting the service prediction data provided by the embodiment of the present invention can be applied to the single-day promotion situation such as “Double Eleven” or “Double Twelve”, and can also be applied to the activity time continuously exceeding Two-day promotion activities, for example, three consecutive days of cost-effective activities, Golden Week; or one hour or a preset time promotion, based on the above environment, the method for correcting business forecast data in the embodiment of the present invention The principle of correction, that is, regardless of any promotion, the business forecast data during the promotion period will be generated on the day before or during the previous period of the activity. When the promotion is carried out, if the actual business data is greater than The service prediction data corresponding to the adjacent time points at the time is generated by comparing the service prediction data with the service data at the time, and correcting the service prediction data from the adjacent time point to the settlement time according to the correction value.
舉例來說，以3天的促銷期為例，在3天促銷期之前，將對該3天促銷期活動的業務資料進行預測，進而得到3天的業務預測資料，即，Dday1、Dday2和Dday3 For example, taking the 3-day promotion period as an example, before the 3-day promotion period, the business data of the 3-day promotion period will be predicted, and then the 3-day business forecast data will be obtained, that is, D day1 and D day2. And D day3
當在3天促銷的活動中時，假設第一天Day1的11：35：30秒的實際產生的業務資料Date1’大於12點至1點該1個小時時間段的業務預測資料Date2，則依據當前的業務資料Date1’與該業務預測資料Date2生成校正值J1，J1可以為Date1’與Date2的差值，在對12點至Day1 當天結算時間對應的業務預測資料Date2~Date12進行校正時，可以通過將J1分別添加至該業務預測資料，即，得到Date2+J1~Date12+J1；當本次校正結束後，在Day1當天11：35：30(秒級)之後還存在實際的業務資料大於相鄰時間點對應的業務預測資料，則繼續執行將業務資料與業務預測資料進行比較，並通過比較生成的校正值，校正該相鄰時間點至當天結算時刻對應的業務預測資料；同理，3天促銷的Day2和Day3的業務預測資料的校正與Day1中的方法相同，在此不再贅述。與3天的促銷期相同的是，黃金週的促銷活動中，對業務預測資料校正的方法與3天的促銷期相同。 When in the 3-day promotion event, it is assumed that the actual business data Date1' of Day1's 11:35:30 seconds on Day 1 is greater than the business forecast data Date2 of the 1 hour period from 12:00 to 1 point, based on The current business data Date1' and the business prediction data Date2 generate a correction value J1, and J1 may be the difference between Date1' and Date2, at 12 o'clock to Day1 When the business forecast data Date2~Date12 corresponding to the settlement time of the day is corrected, the J1 can be added to the service forecast data, that is, Date2+J1~Date12+J1; when the current correction is finished, on Day1 of Day1: After 35:30 (seconds), the actual business data is larger than the business forecast data corresponding to the adjacent time points, and then the business data is compared with the business forecast data, and the adjacent correction value is compared to correct the adjacent The business forecast data corresponding to the settlement time of the day to the same time; similarly, the correction of the business forecast data of Day2 and Day3 of the 3-day promotion is the same as that in Day1, and will not be described here. As with the 3-day promotion period, during the Golden Week promotion, the method of correcting the business forecast data is the same as the 3-day promotion period.
若是一小時的促銷時間，同樣的，在該一小時促銷活動發起的前一天，將會對發起促銷一小時當天的業務資料做一預測，若在促銷活動當天，該小時內的實際產生的業務資料大於該小時相鄰時間點對應的業務預測資料，則與上述業務預測資料校正的方法相同，通過業務資料與業務預測資料的比較生成校正值，進而依據該校正值校正相鄰時間點至當天結算時刻的業務預測資料。 If it is a one-hour promotion time, the same day, the day before the launch of the one-hour promotion, a forecast will be made on the business data of the one-hour promotion day. If the promotion is on the day of the promotion, the actual business generated during the hour. If the data is larger than the service prediction data corresponding to the adjacent time point of the hour, the method is the same as the method for correcting the service prediction data, and the correction value is generated by comparing the service data with the service prediction data, and then the adjacent time point is corrected to the current day according to the correction value. Business forecast data at the time of settlement.
這裡本發明實施例提供的業務預測資料校正的方法可以適用於阿裡巴巴公司的一種利用結構化查詢語言(Structures Query Language，簡稱SQL)部署的大數據計算服務(Open Data Processing Service，簡稱ODPS)平臺。 The method for correcting the service prediction data provided by the embodiment of the present invention can be applied to a platform of Alibaba Corporation that utilizes the Structured Query Language (SQL) deployment of the Open Data Processing Service (ODPS) platform. .
需要說明的是，對於前述的各方法實施例，為了簡單 描述，故將其都表述為一系列的動作組合，但是所屬技術領域中具有通常知識者應該知悉，本發明並不受所描述的動作順序的限制，因為依據本發明，某些步驟可以採用其他順序或者同時進行。其次，所屬技術領域中具有通常知識者也應該知悉，說明書中所描述的實施例均屬於優選實施例，所涉及的動作和模組並不一定是本發明所必須的。 It should be noted that, for the foregoing method embodiments, for the sake of simplicity It is described as a series of combinations of actions, but those of ordinary skill in the art will recognize that the present invention is not limited by the described order of actions, as some steps may be employed in accordance with the present invention. Sequential or simultaneous. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.
通過以上的實施方式的描述，所屬技術領域中具有通常知識者可以清楚地瞭解到根據上述實施例的業務預測資料校正的方法可借助軟體加必需的通用硬體平臺的方式來實現，當然也可以通過硬體，但很多情況下前者是更佳的實施方式。基於這樣的理解，本發明的技術方案本質上或者說對現有技術做出貢獻的部分可以以軟體產品的形式體現出來，該電腦軟體產品儲存在一個儲存媒介(如ROM/RAM、磁碟、光碟)中，包括若干指令用以使得一台終端設備(可以是手機，電腦，伺服器，或者網路設備等)執行本發明各個實施例所述的方法。 Through the description of the above embodiments, those skilled in the art can clearly understand that the method for correcting the service prediction data according to the above embodiment can be implemented by means of a software plus a necessary general hardware platform, and of course, Through hardware, but in many cases the former is a better implementation. Based on such understanding, the technical solution of the present invention, which is essential or contributes to the prior art, can be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk, CD-ROM). The instructions include a number of instructions for causing a terminal device (which may be a cell phone, a computer, a server, or a network device, etc.) to perform the methods described in various embodiments of the present invention.
根據本發明實施例，還提供了一種用於實施上述方法實施例的裝置實施例，本發明上述實施例所提供的裝置可以在電腦終端上運行。 According to an embodiment of the present invention, an apparatus embodiment for implementing the foregoing method embodiments is also provided. The apparatus provided by the foregoing embodiment of the present invention can be run on a computer terminal.
圖5是根據本發明實施例二的業務預測資料校正的裝置的結構示意圖。 FIG. 5 is a schematic structural diagram of an apparatus for correcting service prediction data according to Embodiment 2 of the present invention.
如圖5所示，該業務預測資料校正的裝置可以包括： 提取模組52、判斷模組54、計算模組56和校正模組58。 As shown in FIG. 5, the device for correcting the service prediction data may include: The extraction module 52, the determination module 54, the calculation module 56, and the correction module 58 are provided.
其中，提取模組52，用於在第一時刻提取第一業務資料，其中，第一業務資料包括：在第一時刻產生的業務成交量；判斷模組54，用於判斷第二時刻對應的第二業務預測資料是否大於第一業務資料，其中，第二時刻為第一時刻之後相鄰的時間點；計算模組56，用於在判斷結果為否的情況下，計算第二業務預測資料與第一業務資料的差值；校正模組58，用於依據差值校正第二時刻至結算時刻對應的業務預測資料。 The extraction module 52 is configured to extract the first service data at the first time, where the first service data includes: a transaction volume generated at the first time; the determining module 54 is configured to determine the second time corresponding to the Whether the second service prediction data is greater than the first service data, wherein the second time is an adjacent time point after the first time; the calculation module 56 is configured to calculate the second service prediction data if the determination result is negative The difference from the first service data; the correction module 58 is configured to correct the service prediction data corresponding to the settlement time according to the difference.
由上可知，本發明上述實施例二所提供的方案，通過在第一時刻提取第一業務資料，其中，第一業務資料包括：在第一時刻產生的業務成交量；判斷第二時刻對應的第二業務預測資料是否大於第一業務資料；在判斷結果為否的情況下，計算第二業務預測資料與第一業務資料的差值；依據差值校正第二時刻至結算時刻對應的業務預測資料，達到了對經營個體第二天實際業務量發生變化時能夠及時校正的目的，從而實現了提升對業務量預測精度的技術效果，進而解決了由於現有技術中缺少對經營個體在第二天實際業務量顯著變化的情況下校正預測業務量的技術，導致預測資料低於實際業務量，從而帶來的預測精度低的技術問題。 It can be seen that, in the solution provided by the foregoing embodiment 2 of the present invention, the first service data is extracted by the first time, wherein the first service data includes: a transaction volume generated at a first moment; and a second moment is determined. Whether the second service prediction data is larger than the first service data; if the determination result is negative, calculating the difference between the second service prediction data and the first service data; correcting the service prediction corresponding to the settlement time according to the difference time The data has achieved the purpose of timely correction for the change of the actual business volume of the second day of operation, thereby realizing the technical effect of improving the accuracy of the business volume prediction, and thus solving the problem of the second day in the prior art due to the lack of The technique of correcting the predicted traffic volume in the case where the actual traffic volume changes significantly, resulting in a technical problem that the prediction data is lower than the actual traffic volume, resulting in low prediction accuracy.
此處需要說明的是，上述提取模組52、判斷模組54、計算模組56和校正模組58對應於實施例一中的步驟 S202至步驟S208，四個模組與對應的步驟所實現的示例和應用場景相同，但不限於上述實施例一所公開的內容。需要說明的是，上述模組作為裝置的一部分可以運行在實施例一提供的電腦終端10中，可以通過軟體實現，也可以通過硬體實現。 It should be noted that the extraction module 52, the determination module 54, the calculation module 56, and the correction module 58 correspond to the steps in the first embodiment. S202 to step S208, the four modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the first embodiment. It should be noted that the above-mentioned module can be implemented in the computer terminal 10 provided in the first embodiment as a part of the device, and can be implemented by software or by hardware.
可選的，圖6是根據本發明實施例二的一種業務預測資料校正的裝置的結構示意圖。如圖6所示，提取模組52包括：判斷單元521和提取單元522。 Optionally, FIG. 6 is a schematic structural diagram of an apparatus for correcting service prediction data according to Embodiment 2 of the present invention. As shown in FIG. 6, the extraction module 52 includes a determination unit 521 and an extraction unit 522.
其中，判斷單元521，用於判斷第一時刻是否大於結算時刻；提取單元522，用於在判斷結果為否的情況下，提取第一業務資料。 The determining unit 521 is configured to determine whether the first time is greater than the settlement time; and the extracting unit 522 is configured to: when the determination result is negative, extract the first service data.
此處需要說明的是，上述判斷單元521和提取單元522對應於實施例一中的步驟S202中的Step1和Step2，兩個模組與對應的步驟所實現的示例和應用場景相同，但不限於上述實施例一所公開的內容。需要說明的是，上述模組作為裝置的一部分可以運行在實施例一提供的電腦終端10中，可以通過軟體實現，也可以通過硬體實現。 It should be noted that the foregoing determining unit 521 and the extracting unit 522 correspond to Step 1 and Step 2 in step S202 in the first embodiment, and the two modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited thereto. The content disclosed in the first embodiment above. It should be noted that the above-mentioned module can be implemented in the computer terminal 10 provided in the first embodiment as a part of the device, and can be implemented by software or by hardware.
可選的，圖7是根據本發明實施例二的另一種業務預測資料校正的裝置的結構示意圖。如圖7所示，本發明實施例提供的業務預測資料校正的裝置還包括：獲取模組50和資料計算模組51。 Optionally, FIG. 7 is a schematic structural diagram of another apparatus for correcting service prediction data according to Embodiment 2 of the present invention. As shown in FIG. 7, the apparatus for correcting service prediction data provided by the embodiment of the present invention further includes: an acquisition module 50 and a data calculation module 51.
其中，獲取模組50，用於在第一時刻提取第一業務資料之前，獲取業務預測資料和預設時間的累積業務量占業務總量的比重；資料計算模組51，用於將業務預測資 料和預設時間的累積業務量占業務總量的比重進行計算，得到對應各個時間點的業務預測資料。 The obtaining module 50 is configured to obtain the proportion of the accumulated business volume of the service forecasting data and the preset time to the total amount of the business before the first service data is extracted at the first time; the data calculating module 51 is configured to predict the service Capital The accumulated business volume of the material and the preset time is calculated as the proportion of the total business volume, and the business forecast data corresponding to each time point is obtained.
此處需要說明的是，上述獲取模組50和資料計算模組51對應於實施例一中的步驟S200和步驟S201，兩個模組與對應的步驟所實現的示例和應用場景相同，但不限於上述實施例一所公開的內容。需要說明的是，上述模組作為裝置的一部分可以運行在實施例一提供的電腦終端10中，可以通過軟體實現，也可以通過硬體實現。 It should be noted that the foregoing obtaining module 50 and the data calculating module 51 correspond to step S200 and step S201 in the first embodiment, and the two modules are the same as the examples and application scenarios implemented by the corresponding steps, but not It is limited to the content disclosed in the above embodiment 1. It should be noted that the above-mentioned module can be implemented in the computer terminal 10 provided in the first embodiment as a part of the device, and can be implemented by software or by hardware.
可選的，圖8是根據本發明實施例二的又一種業務預測資料校正的裝置的結構示意圖。如圖8所示，校正模組58包括：數值生成單元581和校正單元582。 Optionally, FIG. 8 is a schematic structural diagram of another apparatus for correcting service prediction data according to Embodiment 2 of the present invention. As shown in FIG. 8, the correction module 58 includes a numerical value generating unit 581 and a correcting unit 582.
其中，數值生成單元581，用於依據差值生成校正值；校正單元582，用於依據校正值對第二時刻至結算時刻對應的業務預測資料進行調整，得到校正後的業務預測資料。 The value generating unit 581 is configured to generate a correction value according to the difference value, and the correcting unit 582 is configured to adjust the service prediction data corresponding to the second time to the settlement time according to the correction value to obtain the corrected service prediction data.
此處需要說明的是，上述數值生成單元581和校正單元582對應於實施例一中的步驟S208中的Step1和Step2，兩個模組與對應的步驟所實現的示例和應用場景相同，但不限於上述實施例一所公開的內容。需要說明的是，上述模組作為裝置的一部分可以運行在實施例一提供的電腦終端10中，可以通過軟體實現，也可以通過硬體實現。 It should be noted that the above-mentioned numerical value generating unit 581 and the correcting unit 582 correspond to Step 1 and Step 2 in step S208 in the first embodiment, and the two modules are the same as the examples and application scenarios implemented by the corresponding steps, but not It is limited to the content disclosed in the above embodiment 1. It should be noted that the above-mentioned module can be implemented in the computer terminal 10 provided in the first embodiment as a part of the device, and can be implemented by software or by hardware.
進一步地，可選的，圖9是根據本發明實施例二的再一種業務預測資料校正的裝置的結構示意圖。如圖9所 示，校正單元582包括：第一校正子單元5821、數值生成子單元5822和第二校正子單元5823。 Further, optionally, FIG. 9 is a schematic structural diagram of another apparatus for correcting service prediction data according to Embodiment 2 of the present invention. As shown in Figure 9 The correction unit 582 includes a first syndrome unit 5821, a value generation subunit 5822, and a second syndrome unit 5823.
其中，第一校正子單元5821，用於在將差值確定為校正值的情況下，將差值加至第二時刻至結算時刻對應的業務預測資料，得到校正後的業務預測資料；或者，數值生成子單元5822，用於依據差值生成校正比重，並將校正比重確定為校正值；第二校正子單元5823，用於對第二時刻至結算時刻對應的業務預測資料依據校正比重進行調整，得到校正後的業務預測資料。 The first correction subunit 5821 is configured to: when the difference is determined as the correction value, add the difference to the service prediction data corresponding to the second time to the settlement time to obtain the corrected service prediction data; or The value generating sub-unit 5822 is configured to generate a corrected specific gravity according to the difference, and determine the corrected specific gravity as the correction value. The second correcting sub-unit 5823 is configured to adjust the service forecasting data corresponding to the second time to the settlement time according to the corrected specific gravity. , get corrected business forecast data.
此處需要說明的是，上述第一校正子單元5821、數值生成子單元5822和第二校正子單元5823對應於實施例一中的步驟S208中Step2中的步驟A至步驟C，三個模組與對應的步驟所實現的示例和應用場景相同，但不限於上述實施例一所公開的內容。需要說明的是，上述模組作為裝置的一部分可以運行在實施例一提供的電腦終端10中，可以通過軟體實現，也可以通過硬體實現。 It should be noted that, the first correcting subunit 5821, the numerical generating subunit 5822, and the second correcting subunit 5823 correspond to the steps A to C in the step 2 in the step S208 in the first embodiment, and the three modules. The examples and application scenarios implemented by the corresponding steps are the same, but are not limited to the content disclosed in the first embodiment. It should be noted that the above-mentioned module can be implemented in the computer terminal 10 provided in the first embodiment as a part of the device, and can be implemented by software or by hardware.
由上可知，本發明實施例提供的業務預測資料校正的裝置，通過在第一時刻提取第一業務資料，並將該第一業務資料與第二時刻的第二業務預測資料進行比較，在第二業務預測資料小於第一業務資料的情況下，計算第一業務資料與第二業務預測資料之間的差值，並依據該差值校正第二時刻至結算時刻的業務預測資料，通過補差技術可以達到帶點虛線的效果將所有後麵點的預測值做個平滑提升，及時有效解決了因當天經營個體效果突變而造成的預 測不準確甚至是預測離譜問題。 It can be seen that the device for correcting the service prediction data provided by the embodiment of the present invention compares the first service data with the second service prediction data at the second time by comparing the first service data at the first time, When the second service prediction data is smaller than the first service data, the difference between the first service data and the second service prediction data is calculated, and the service prediction data from the second time to the settlement time is corrected according to the difference, and the compensation technique is adopted. The effect of dotted lines can be achieved, and the predicted values of all the subsequent points can be smoothly improved, and the premise caused by the sudden change of the individual operating effects of the day can be effectively solved in time. Inaccurate measurement or even prediction of outrageous problems.
本發明的實施例還提供了一種儲存媒介。可選地，在本實施例中，上述儲存媒介可以用於保存上述實施例一所提供的業務預測資料校正的方法所執行的程式碼。 Embodiments of the present invention also provide a storage medium. Optionally, in this embodiment, the foregoing storage medium may be used to save the code executed by the method for correcting the service prediction data provided in Embodiment 1 above.
可選地，在本實施例中，上述儲存媒介可以位於電腦網路中電腦終端群中的任意一個電腦終端中，或者位於行動終端群中的任意一個行動終端中。 Optionally, in this embodiment, the foregoing storage medium may be located in any one of the computer terminal groups in the computer network, or in any one of the mobile terminal groups.
可選地，在本實施例中，儲存媒介被設置為儲存用於執行以下步驟的程式碼：在第一時刻提取第一業務資料，其中，第一業務資料包括：在第一時刻產生的業務成交量；判斷第二時刻對應的第二業務預測資料是否大於第一業務資料，其中，第二時刻為第一時刻之後相鄰的時間點；在判斷結果為否的情況下，計算第二業務預測資料與第一業務資料的差值；依據差值校正第二時刻至結算時刻對應的業務預測資料。 Optionally, in this embodiment, the storage medium is configured to store a code for performing the following steps: extracting the first service data at the first moment, where the first service data includes: the service generated at the first moment The volume of the second service is determined to be greater than the first service data, wherein the second time is an adjacent time point after the first time; and if the determination result is negative, the second service is calculated. The difference between the predicted data and the first business data; the business forecast data corresponding to the second time to the settlement time is corrected according to the difference.
可選地，在本實施例中，儲存媒介被設置為儲存用於執行以下步驟的程式碼：判斷第一時刻是否大於結算時刻；在判斷結果為否的情況下，提取第一業務資料。 Optionally, in this embodiment, the storage medium is configured to store a code for performing the following steps: determining whether the first time is greater than a settlement time; and if the determination result is negative, extracting the first service data.
可選地，在本實施例中，儲存媒介被設置為儲存用於執行以下步驟的程式碼：獲取日業務預測資料和每小時累積業務量占業務總量的比重；將日業務預測資料和每小時累積業務量占業務總量的比重進行計算，得到每小時業務 預測資料。 Optionally, in this embodiment, the storage medium is configured to store a code for performing the following steps: acquiring daily business forecast data and accumulating traffic volume per hour as a percentage of the total business volume; The hourly accumulated business volume accounts for the proportion of the total business volume, and the hourly business is obtained. Forecast data.
可選地，在本實施例中，儲存媒介被設置為儲存用於執行以下步驟的程式碼：計算日業務預測資料和比重的乘積，將乘積確定為每小時業務預測資料。 Alternatively, in the present embodiment, the storage medium is arranged to store a code for performing the following steps: calculating the product of the daily business forecast data and the specific gravity, and determining the product as the hourly business forecast data.
可選地，在本實施例中，儲存媒介被設置為儲存用於執行以下步驟的程式碼：將差值加至第二時刻至當日結算時刻對應的每小時業務預測資料，得到校正後的每小時業務預測資料。 Optionally, in this embodiment, the storage medium is configured to store a code for performing the following steps: adding the difference to the hourly business forecast data corresponding to the second time to the current settlement time, and obtaining the corrected each Hour business forecast data.
上述本發明實施例序號僅為了描述，不代表實施例的優劣。 The serial numbers of the embodiments of the present invention are merely described, and do not represent the advantages and disadvantages of the embodiments.
在本發明的上述實施例中，對各個實施例的描述都各有側重，某個實施例中沒有詳述的部分，可以參見其他實施例的相關描述。 In the above-mentioned embodiments of the present invention, the descriptions of the various embodiments are different, and the parts that are not detailed in a certain embodiment can be referred to the related descriptions of other embodiments.
在本發明所提供的幾個實施例中，應該理解到，所揭露的技術內容，可通過其它的方式實現。其中，以上所描述的裝置實施例僅是示意性的，例如該單元的劃分，僅為一種邏輯功能劃分，實際實現時可以有另外的劃分方式，例如多個單元或元件可以結合或者可以集成到另一個系統，或一些特徵可以忽略，或不執行。另一點，所顯示或討論的相互之間的耦合或直接耦合或通訊連接可以是通過一些介面，單元或模組的間接耦合或通訊連接，可以是電性或其它的形式。 In the several embodiments provided by the present invention, it should be understood that the disclosed technical contents may be implemented in other manners. The device embodiments described above are only schematic. For example, the division of the unit is only a logical function division. In actual implementation, there may be another division manner. For example, multiple units or components may be combined or may be integrated into Another system, or some features can be ignored or not executed. Alternatively, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, unit or module, and may be electrical or otherwise.
該作為分離部件說明的單元可以是或者也可以不是實體上分開的，作為單元顯示的部件可以是或者也可以不是 實體單元，即可以位於一個地方，或者也可以分佈到多個網路單元上。可以根據實際的需要選擇其中的部分或者全部單元來實現本實施例方案的目的。 The unit described as a separate component may or may not be physically separate, and the component displayed as a unit may or may not be The physical unit can be located in one place or distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
另外，在本發明各個實施例中的各功能單元可以集成在一個處理單元中，也可以是各個單元單獨實體存在，也可以兩個或兩個以上單元集成在一個單元中。上述集成的單元既可以採用硬體的形式實現，也可以採用軟體功能單元的形式實現。 In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist as a separate entity, or two or more units may be integrated into one unit. The above integrated unit can be implemented in the form of a hardware or a software functional unit.
該集成的單元如果以軟體功能單元的形式實現並作為獨立的產品銷售或使用時，可以儲存在一個電腦可讀取儲存媒介中。基於這樣的理解，本發明的技術方案本質上或者說對現有技術做出貢獻的部分或者該技術方案的全部或部分可以以軟體產品的形式體現出來，該電腦軟體產品儲存在一個儲存媒介中，包括若干指令用以使得一台電腦設備(可為個人電腦、伺服器或者網路設備等)執行本發明各個實施例該方法的全部或部分步驟。而前述的儲存媒介包括：隨身碟、唯讀記憶體(ROM，Read-Only Memory)、隨機存取記憶體(RAM，Random Access Memory)、行動硬碟、磁碟或者光碟等各種可以儲存程式碼的媒介。 The integrated unit, if implemented as a software functional unit and sold or used as a standalone product, can be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may contribute to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium. A number of instructions are included to cause a computer device (which may be a personal computer, server or network device, etc.) to perform all or part of the steps of the method of various embodiments of the present invention. The foregoing storage medium includes: a flash drive, a read-only memory (ROM), a random access memory (RAM, a random access memory), a mobile hard disk, a magnetic disk, or a compact disk, and the like. Medium.
以上所述僅是本發明的優選實施方式，應當指出，對於所屬技術領域中具有通常知識者來說，在不脫離本發明原理的前提下，還可以做出若干改進和潤飾，這些改進和潤飾也應視為本發明的保護範圍。 The above description is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present invention. It should also be considered as the scope of protection of the present invention.
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| CN111311014B (en) * | 2020-02-27 | 2024-04-12 | 广州酷旅旅行社有限公司 | Service data processing method, device, computer equipment and storage medium |
| CN112488377A (en) * | 2020-11-25 | 2021-03-12 | 上海中通吉网络技术有限公司 | Method and device for predicting daily order quantity of express delivery, storage medium and electronic equipment |
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| CN102711177A (en) * | 2012-04-26 | 2012-10-03 | 北京邮电大学 | Service prediction based load balancing method |
| CN103490956A (en) * | 2013-09-22 | 2014-01-01 | 杭州华为数字技术有限公司 | Self-adaptive energy-saving control method, device and system based on traffic predication |
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